{
  "session_id": "20260430_151650_361245",
  "model": "nvidia/nemotron-3-super-120b-a12b:free",
  "base_url": "https://openrouter.ai/api/v1",
  "platform": "curator",
  "session_start": "2026-04-30T15:16:50.772277",
  "last_updated": "2026-04-30T15:26:50.954657",
  "system_prompt": "You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. You communicate clearly, admit uncertainty when appropriate, and prioritize being genuinely useful over being verbose unless otherwise directed below. Be targeted and efficient in your exploration and investigations.\n\nIf the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. Docs: https://hermes-agent.nousresearch.com/docs\n\nConversation started: Thursday, April 30, 2026 03:16 PM\nModel: nvidia/nemotron-3-super-120b-a12b:free\nProvider: openrouter",
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "patch",
        "description": "Targeted find-and-replace edits in files. Use this instead of sed/awk in terminal. Uses fuzzy matching (9 strategies) so minor whitespace/indentation differences won't break it. Returns a unified diff. Auto-runs syntax checks after editing.\n\nReplace mode (default): find a unique string and replace it.\nPatch mode: apply V4A multi-file patches for bulk changes.",
        "parameters": {
          "type": "object",
          "properties": {
            "mode": {
              "type": "string",
              "enum": [
                "replace",
                "patch"
              ],
              "description": "Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches",
              "default": "replace"
            },
            "path": {
              "type": "string",
              "description": "File path to edit (required for 'replace' mode)"
            },
            "old_string": {
              "type": "string",
              "description": "Text to find in the file (required for 'replace' mode). Must be unique in the file unless replace_all=true. Include enough surrounding context to ensure uniqueness."
            },
            "new_string": {
              "type": "string",
              "description": "Replacement text (required for 'replace' mode). Can be empty string to delete the matched text."
            },
            "replace_all": {
              "type": "boolean",
              "description": "Replace all occurrences instead of requiring a unique match (default: false)",
              "default": false
            },
            "patch": {
              "type": "string",
              "description": "V4A format patch content (required for 'patch' mode). Format:\n*** Begin Patch\n*** Update File: path/to/file\n@@ context hint @@\n context line\n-removed line\n+added line\n*** End Patch"
            }
          },
          "required": [
            "mode"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "read_file",
        "description": "Read a text file with line numbers and pagination. Use this instead of cat/head/tail in terminal. Output format: 'LINE_NUM|CONTENT'. Suggests similar filenames if not found. Use offset and limit for large files. Reads exceeding ~100K characters are rejected; use offset and limit to read specific sections of large files. NOTE: Cannot read images or binary files — use vision_analyze for images.",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to read (absolute, relative, or ~/path)"
            },
            "offset": {
              "type": "integer",
              "description": "Line number to start reading from (1-indexed, default: 1)",
              "default": 1,
              "minimum": 1
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of lines to read (default: 500, max: 2000)",
              "default": 500,
              "maximum": 2000
            }
          },
          "required": [
            "path"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "search_files",
        "description": "Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.\n\nContent search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.\n\nFile search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.",
        "parameters": {
          "type": "object",
          "properties": {
            "pattern": {
              "type": "string",
              "description": "Regex pattern for content search, or glob pattern (e.g., '*.py') for file search"
            },
            "target": {
              "type": "string",
              "enum": [
                "content",
                "files"
              ],
              "description": "'content' searches inside file contents, 'files' searches for files by name",
              "default": "content"
            },
            "path": {
              "type": "string",
              "description": "Directory or file to search in (default: current working directory)",
              "default": "."
            },
            "file_glob": {
              "type": "string",
              "description": "Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)"
            },
            "limit": {
              "type": "integer",
              "description": "Maximum number of results to return (default: 50)",
              "default": 50
            },
            "offset": {
              "type": "integer",
              "description": "Skip first N results for pagination (default: 0)",
              "default": 0
            },
            "output_mode": {
              "type": "string",
              "enum": [
                "content",
                "files_only",
                "count"
              ],
              "description": "Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file",
              "default": "content"
            },
            "context": {
              "type": "integer",
              "description": "Number of context lines before and after each match (grep mode only)",
              "default": 0
            }
          },
          "required": [
            "pattern"
          ]
        }
      }
    },
    {
      "type": "function",
      "function": {
        "name": "write_file",
        "description": "Write content to a file, completely replacing existing content. Use this instead of echo/cat heredoc in terminal. Creates parent directories automatically. OVERWRITES the entire file — use 'patch' for targeted edits.",
        "parameters": {
          "type": "object",
          "properties": {
            "path": {
              "type": "string",
              "description": "Path to the file to write (will be created if it doesn't exist, overwritten if it does)"
            },
            "content": {
              "type": "string",
              "description": "Complete content to write to the file"
            }
          },
          "required": [
            "path",
            "content"
          ]
        }
      }
    }
  ],
  "message_count": 25,
  "messages": [
    {
      "role": "user",
      "content": "Get the list of all skills using the skills_list tool and save it to /tmp/skills.json"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to get the list of all skills using the skills_list tool. However, we don't see a tool named skills_list in the provided tools list. The available tools are patch, read_file, search_files, write_file. There's no skills_list tool. Perhaps the skill is part of the \"hermes-agent\" skill? But the task says: \"Get the list of all skills using the skills_list tool\". Maybe the skills_list tool is a tool that is available in the environment but not listed? Wait, the system prompt says: \"You are Hermes Agent, an intelligent AI assistant created by Nous Research.\" It mentions that if the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. But this is a subagent task: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". Possibly the skills_list tool is a tool that is part of the hermes-agent skill? But we haven't loaded that skill. However, we are a focused subagent working on a specific delegated task. The task does not mention loading any skill. Perhaps the skills_list tool is a built-in tool that is available but not listed in the tools description? The tools list we see might be incomplete? The system prompt says we have access to the following functions: patch, read_file, search_files, write_file. That's it. So there is no skills_list tool. Maybe we need to use the write_file tool to create a file, but we need to get the list of skills from somewhere else. Possibly we need to read from a known location where skills are stored? The context says: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\" So the expectation is that there is a tool called skills_list that we can invoke. However, it's not in the provided tools. Could it be that the tools list is missing because we need to load a skill that provides that tool? The instruction about loading the hermes-agent skill might be relevant. If we load the hermes-agent skill, maybe it provides additional tools like skills_list. Let's try to load the hermes-agent skill using the skill_view tool? But we don't have a skill_view tool either. The system prompt mentions skill_view as a way to load a skill, but that might be a meta-command not exposed as a tool. Actually, the system prompt says: \"If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering.\" This suggests that there is a mechanism to load skills, but not necessarily a tool we can call. However, as an agent, we might have access to a skill management system. But we don't see any tool for that.\n\nMaybe the skills_list tool is actually available but not listed because it's a \"tool\" that is provided by the environment, and we can call it like a function? The tools list we see might be the only ones we can use. But the task explicitly says to use the skills_list tool. Perhaps we need to infer that skills_list is a tool that returns a list of skills, and we can call it via some mechanism? Alternatively, maybe we need to write a script that uses the existing tools to get the list of skills? For example, maybe skills are stored in a directory like /opt/hermes/skills, and we can list them. Let's explore the workspace.\n\nWe are at /opt/hermes. Let's see what's there. We can use search_files to list files, or read_file to examine directories? Actually, search_files with target='files' and pattern='*' can list files. Let's first look at the directory structure.\n\nWe'll use search_files to find files in /opt/hermes. We'll start with a simple search for all files. But we need to be careful about limits. Let's do a file search for \"*\" in the current directory (default path is \".\" which is /opt/hermes). We'll set output_mode to 'files_only' to get just file paths. We'll also maybe limit to a reasonable number.\n\nLet's do that.\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to get the list of all skills using the skills_list tool. However, we don't see a tool named skills_list in the provided tools list. The available tools are patch, read_file, search_files, write_file. There's no skills_list tool. Perhaps the skill is part of the \"hermes-agent\" skill? But the task says: \"Get the list of all skills using the skills_list tool\". Maybe the skills_list tool is a tool that is available in the environment but not listed? Wait, the system prompt says: \"You are Hermes Agent, an intelligent AI assistant created by Nous Research.\" It mentions that if the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering. But this is a subagent task: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". Possibly the skills_list tool is a tool that is part of the hermes-agent skill? But we haven't loaded that skill. However, we are a focused subagent working on a specific delegated task. The task does not mention loading any skill. Perhaps the skills_list tool is a built-in tool that is available but not listed in the tools description? The tools list we see might be incomplete? The system prompt says we have access to the following functions: patch, read_file, search_files, write_file. That's it. So there is no skills_list tool. Maybe we need to use the write_file tool to create a file, but we need to get the list of skills from somewhere else. Possibly we need to read from a known location where skills are stored? The context says: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\" So the expectation is that there is a tool called skills_list that we can invoke. However, it's not in the provided tools. Could it be that the tools list is missing because we need to load a skill that provides that tool? The instruction about loading the hermes-agent skill might be relevant. If we load the hermes-agent skill, maybe it provides additional tools like skills_list. Let's try to load the hermes-agent skill using the skill_view tool? But we don't have a skill_view tool either. The system prompt mentions skill_view as a way to load a skill, but that might be a meta-command not exposed as a tool. Actually, the system prompt says: \"If the user asks about configuring, setting up, or using Hermes Agent itself, load the `hermes-agent` skill with skill_view(name='hermes-agent') before answering.\" This suggests that there is a mechanism to load skills, but not necessarily a tool we can call. However, as an agent, we might have access to a skill management system. But we don't see any tool for that.\n\nMaybe the skills_list tool is actually available but not listed because it's a \"tool\" that is provided by the environment, and we can call it like a function? The tools list we see might be the only ones we can use. But the task explicitly says to use the skills_list tool. Perhaps we need to infer that skills_list is a tool that returns a list of skills, and we can call it via some mechanism? Alternatively, maybe we need to write a script that uses the existing tools to get the list of skills? For example, maybe skills are stored in a directory like /opt/hermes/skills, and we can list them. Let's explore the workspace.\n\nWe are at /opt/hermes. Let's see what's there. We can use search_files to list files, or read_file to examine directories? Actually, search_files with target='files' and pattern='*' can list files. Let's first look at the directory structure.\n\nWe'll use search_files to find files in /opt/hermes. We'll start with a simple search for all files. But we need to be careful about limits. Let's do a file search for \"*\" in the current directory (default path is \".\" which is /opt/hermes). We'll set output_mode to 'files_only' to get just file paths. We'll also maybe limit to a reasonable number.\n\nLet's do that.\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-83db111651d5255a",
          "call_id": "chatcmpl-tool-83db111651d5255a",
          "response_item_id": "fc_chatcmpl-tool-83db111651d5255a",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"*\", \"target\": \"files\", \"output_mode\": \"files_only\", \"limit\": 1000}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "<persisted-output>\nThis tool result was too large (116,550 characters, 113.8 KB).\nFull output saved to: /tmp/hermes-results/chatcmpl-tool-83db111651d5255a.txt\nUse the read_file tool with offset and limit to access specific sections of this output.\n\nPreview (first 1500 chars):\n{\"total_count\": 1000, \"files\": [\"./sand_sage_dashboard/index.html\", \"./tools/__pycache__/fuzzy_match.cpython-313.pyc\", \"./sand_sage_dashboard/script.js\", \"./sand_sage_dashboard/style.css\", \"./agent/__pycache__/title_generator.cpython-313.pyc\", \"./tools/__pycache__/ansi_strip.cpython-313.pyc\", \"./tools/__pycache__/env_passthrough.cpython-313.pyc\", \"./agent/__pycache__/rate_limit_tracker.cpython-313.pyc\", \"./tools/__pycache__/voice_mode.cpython-313.pyc\", \"./gateway/__pycache__/restart.cpython-313.pyc\", \"./hermes_cli/__pycache__/gateway.cpython-313.pyc\", \"./hermes_cli/__pycache__/auth_commands.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/resources/vector_stores/__pycache__/vector_stores.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/resources/vector_stores/__pycache__/file_batches.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_stores/__pycache__/file_batch_list_files_params.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_stores/__pycache__/vector_store_file_deleted.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_stores/__pycache__/file_batch_create_params.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_stores/__pycache__/vector_store_file_batch.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_stores/__pycache__/file_content_response.cpython-313.pyc\", \"./.venv/lib/python3.13/site-packages/openai/types/vector_\n...\n</persisted-output>",
      "tool_call_id": "chatcmpl-tool-83db111651d5255a"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to list skills using the skills_list tool. There's no tool named skills_list in the provided list. Perhaps it's a function that is part of the environment but not listed? The task says: \"Get the list of all skills using the skills_list tool\". However, the available tools are patch, read_file, search_files, write_file. There is no skills_list tool. Maybe skills_list is a command that we can run via the system? But we are limited to the tools provided. Perhaps we need to look for a skill that provides skills_list? Or maybe the skills_list tool is available but not listed in the description? The system might have additional tools not listed in the prompt? The prompt says: \"You have access to the following functions:\" and lists those four. So skills_list is not among them. Could be that we need to use the write_file tool to create a file with the output of some command? But we don't have a command execution tool.\n\nMaybe the skills_list tool is actually a function that we can call via the agent's internal mechanism? The instruction says: \"Get the list of all skills using the skills_list tool\". Possibly the skills_list tool is a built-in tool that is not listed because it's assumed? But the prompt explicitly lists the tools we have access to. I'm confused.\n\nLet's re-read: The user says: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". The context: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\"\n\nThus, we must assume that there is a tool named skills_list available. Perhaps the tool list provided is incomplete? Or maybe the skills_list tool is one of the tools but under a different name? Let's check the tool names: patch, read_file, search_files, write_file. None is skills_list.\n\nMaybe we need to use the search_files tool to find a skill that lists skills? But the task is to get the list of all skills. Perhaps there is a directory of skills, and we need to list them. The workspace path is /opt/hermes. We could look for skill definitions.\n\nLet's explore the filesystem a bit using search_files to see if there is a skills directory or something.\n\nWe'll search for files with \"skill\" in name or content.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to list skills using the skills_list tool. There's no tool named skills_list in the provided list. Perhaps it's a function that is part of the environment but not listed? The task says: \"Get the list of all skills using the skills_list tool\". However, the available tools are patch, read_file, search_files, write_file. There is no skills_list tool. Maybe skills_list is a command that we can run via the system? But we are limited to the tools provided. Perhaps we need to look for a skill that provides skills_list? Or maybe the skills_list tool is available but not listed in the description? The system might have additional tools not listed in the prompt? The prompt says: \"You have access to the following functions:\" and lists those four. So skills_list is not among them. Could be that we need to use the write_file tool to create a file with the output of some command? But we don't have a command execution tool.\n\nMaybe the skills_list tool is actually a function that we can call via the agent's internal mechanism? The instruction says: \"Get the list of all skills using the skills_list tool\". Possibly the skills_list tool is a built-in tool that is not listed because it's assumed? But the prompt explicitly lists the tools we have access to. I'm confused.\n\nLet's re-read: The user says: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". The context: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\"\n\nThus, we must assume that there is a tool named skills_list available. Perhaps the tool list provided is incomplete? Or maybe the skills_list tool is one of the tools but under a different name? Let's check the tool names: patch, read_file, search_files, write_file. None is skills_list.\n\nMaybe we need to use the search_files tool to find a skill that lists skills? But the task is to get the list of all skills. Perhaps there is a directory of skills, and we need to list them. The workspace path is /opt/hermes. We could look for skill definitions.\n\nLet's explore the filesystem a bit using search_files to see if there is a skills directory or something.\n\nWe'll search for files with \"skill\" in name or content.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-b49940b50b31b7b8",
          "call_id": "chatcmpl-tool-b49940b50b31b7b8",
          "response_item_id": "fc_chatcmpl-tool-b49940b50b31b7b8",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"*skill*\", \"target\": \"files\", \"path\": \"/opt/hermes\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 49, \"files\": [\"/opt/hermes/agent/__pycache__/skill_commands.cpython-313.pyc\", \"/opt/hermes/agent/__pycache__/skill_utils.cpython-313.pyc\", \"/opt/hermes/tools/__pycache__/skills_tool.cpython-313.pyc\", \"/opt/hermes/tools/__pycache__/skills_guard.cpython-313.pyc\", \"/opt/hermes/tools/__pycache__/skill_manager_tool.cpython-313.pyc\", \"/opt/hermes/tools/__pycache__/skills_sync.cpython-313.pyc\", \"/opt/hermes/tools/skills_sync.py\", \"/opt/hermes/tools/skills_tool.py\", \"/opt/hermes/tools/skills_guard.py\", \"/opt/hermes/tools/skill_manager_tool.py\", \"/opt/hermes/tools/skills_hub.py\", \"/opt/hermes/tests/tools/test_skill_manager_tool.py\", \"/opt/hermes/tests/tools/test_skills_guard.py\", \"/opt/hermes/tests/tools/test_skill_size_limits.py\", \"/opt/hermes/tests/tools/test_skill_improvements.py\", \"/opt/hermes/tests/tools/test_skills_hub_clawhub.py\", \"/opt/hermes/tests/tools/test_skill_view_path_check.py\", \"/opt/hermes/tests/tools/test_skills_sync.py\", \"/opt/hermes/tests/tools/test_skill_view_traversal.py\", \"/opt/hermes/tests/tools/test_skills_tool.py\", \"/opt/hermes/tests/tools/test_skill_env_passthrough.py\", \"/opt/hermes/tests/tools/test_skills_hub.py\", \"/opt/hermes/tests/cli/test_cli_preloaded_skills.py\", \"/opt/hermes/tests/test_plugin_skills.py\", \"/opt/hermes/tests/agent/test_skill_commands.py\", \"/opt/hermes/tests/agent/test_external_skills.py\", \"/opt/hermes/tests/gateway/test_discord_channel_skills.py\", \"/opt/hermes/tests/hermes_cli/test_chat_skills_flag.py\", \"/opt/hermes/tests/hermes_cli/test_banner_skills.py\", \"/opt/hermes/tests/hermes_cli/test_skills_skip_confirm.py\", \"/opt/hermes/tests/hermes_cli/test_skills_install_flags.py\", \"/opt/hermes/tests/hermes_cli/test_skills_config.py\", \"/opt/hermes/tests/hermes_cli/test_skills_subparser.py\", \"/opt/hermes/tests/hermes_cli/test_skills_hub.py\", \"/opt/hermes/tests/skills/test_telephony_skill.py\", \"/opt/hermes/website/docs/user-guide/features/skills.md\", \"/opt/hermes/website/docs/guides/work-with-skills.md\", \"/opt/hermes/website/docs/developer-guide/creating-skills.md\", \"/opt/hermes/website/docs/reference/optional-skills-catalog.md\", \"/opt/hermes/website/docs/reference/skills-catalog.md\", \"/opt/hermes/website/scripts/extract-skills.py\", \"/opt/hermes/agent/skill_commands.py\", \"/opt/hermes/agent/skill_utils.py\", \"/opt/hermes/hermes_cli/skills_config.py\", \"/opt/hermes/hermes_cli/skills_hub.py\", \"/opt/hermes/skills/index-cache/anthropics_skills_skills_.json\", \"/opt/hermes/skills/index-cache/openai_skills_skills_.json\", \"/opt/hermes/skills/index-cache/claude_marketplace_anthropics_skills.json\", \"/opt/hermes/scripts/build_skills_index.py\"]}",
      "tool_call_id": "chatcmpl-tool-b49940b50b31b7b8"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to call the skills_list tool. It seems there is a tool named \"skills_tool\" or \"skill_manager_tool\"? In the files list we see tools/skills_tool.py, skill_manager_tool.py, skills_sync.py, skills_guard.py.\n\nProbably the tool we need is called \"skills_list\". Let's check if there is a tool named skills_list. We can search for \"skills_list\" in the code.\n\nBut first, maybe the tool is exposed via the agent's tool set. As a subagent, we can invoke tools by name. The instruction says: \"Get the list of all skills using the skills_list tool\". So there is a tool named skills_list.\n\nWe need to call it with no arguments and write the JSON output to /tmp/skills.json.\n\nLet's try to invoke skills_list. We don't have a direct tool invocation mechanism in the chat? Actually we have the ability to use tools via the tool calls? The available tools are patch, read_file, search_files, write_file. There is no generic tool invocation. However, the system may have provided other tools like skills_list as part of the environment? The initial system message didn't list skills_list as a tool. But the user says we have access to the following tools: patch, read_file, search_files, write_file. That's it.\n\nWait: The user said: \"You have access to the following functions:\" and listed those four. So we cannot directly call skills_list unless we can invoke it via some other means. Maybe skills_list is a command-line tool or a script we can run? Let's see if there is a script.\n\nFrom the search results, we have tools/skills_tool.py. Let's examine that file to see if it provides a skills_list function.\n\nWe'll read that file.\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to call the skills_list tool. It seems there is a tool named \"skills_tool\" or \"skill_manager_tool\"? In the files list we see tools/skills_tool.py, skill_manager_tool.py, skills_sync.py, skills_guard.py.\n\nProbably the tool we need is called \"skills_list\". Let's check if there is a tool named skills_list. We can search for \"skills_list\" in the code.\n\nBut first, maybe the tool is exposed via the agent's tool set. As a subagent, we can invoke tools by name. The instruction says: \"Get the list of all skills using the skills_list tool\". So there is a tool named skills_list.\n\nWe need to call it with no arguments and write the JSON output to /tmp/skills.json.\n\nLet's try to invoke skills_list. We don't have a direct tool invocation mechanism in the chat? Actually we have the ability to use tools via the tool calls? The available tools are patch, read_file, search_files, write_file. There is no generic tool invocation. However, the system may have provided other tools like skills_list as part of the environment? The initial system message didn't list skills_list as a tool. But the user says we have access to the following tools: patch, read_file, search_files, write_file. That's it.\n\nWait: The user said: \"You have access to the following functions:\" and listed those four. So we cannot directly call skills_list unless we can invoke it via some other means. Maybe skills_list is a command-line tool or a script we can run? Let's see if there is a script.\n\nFrom the search results, we have tools/skills_tool.py. Let's examine that file to see if it provides a skills_list function.\n\nWe'll read that file.\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-88038e97fc14fa25",
          "call_id": "chatcmpl-tool-88038e97fc14fa25",
          "response_item_id": "fc_chatcmpl-tool-88038e97fc14fa25",
          "type": "function",
          "function": {
            "name": "read_file",
            "arguments": "{\"path\": \"/opt/hermes/tools/skills_tool.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"content\": \"     1|#!/usr/bin/env python3\\n     2|\\\"\\\"\\\"\\n     3|Skills Tool Module\\n     4|\\n     5|This module provides tools for listing and viewing skill documents.\\n     6|Skills are organized as directories containing a SKILL.md file (the main instructions)\\n     7|and optional supporting files like references, templates, and examples.\\n     8|\\n     9|Inspired by Anthropic's Claude Skills system with progressive disclosure architecture:\\n    10|- Metadata (name ≤64 chars, description ≤1024 chars) - shown in skills_list\\n    11|- Full Instructions - loaded via skill_view when needed\\n    12|- Linked Files (references, templates) - loaded on demand\\n    13|\\n    14|Directory Structure:\\n    15|    skills/\\n    16|    ├── my-skill/\\n    17|    │   ├── SKILL.md           # Main instructions (required)\\n    18|    │   ├── references/        # Supporting documentation\\n    19|    │   │   ├── api.md\\n    20|    │   │   └── examples.md\\n    21|    │   ├── templates/         # Templates for output\\n    22|    │   │   └── template.md\\n    23|    │   └── assets/            # Supplementary files (agentskills.io standard)\\n    24|    └── category/              # Category folder for organization\\n    25|        └── another-skill/\\n    26|            └── SKILL.md\\n    27|\\n    28|SKILL.md Format (YAML Frontmatter, agentskills.io compatible):\\n    29|    ---\\n    30|    name: skill-name              # Required, max 64 chars\\n    31|    description: Brief description # Required, max 1024 chars\\n    32|    version: 1.0.0                # Optional\\n    33|    license: MIT                  # Optional (agentskills.io)\\n    34|    platforms: [macos]            # Optional — restrict to specific OS platforms\\n    35|                                  #   Valid: macos, linux, windows\\n    36|                                  #   Omit to load on all platforms (default)\\n    37|    prerequisites:                # Optional — legacy runtime requirements\\n    38|      env_vars: [API_KEY]         #   Legacy env var names are normalized into\\n    39|                                  #   required_environment_variables on load.\\n    40|      commands: [curl, jq]        #   Command checks remain advisory only.\\n    41|    compatibility: Requires X     # Optional (agentskills.io)\\n    42|    metadata:                     # Optional, arbitrary key-value (agentskills.io)\\n    43|      hermes:\\n    44|        tags: [fine-tuning, llm]\\n    45|        related_skills: [peft, lora]\\n    46|    ---\\n    47|\\n    48|    # Skill Title\\n    49|\\n    50|    Full instructions and content here...\\n    51|\\n    52|Available tools:\\n    53|- skills_list: List skills with metadata (progressive disclosure tier 1)\\n    54|- skill_view: Load full skill content (progressive disclosure tier 2-3)\\n    55|\\n    56|Usage:\\n    57|    from tools.skills_tool import skills_list, skill_view, check_skills_requirements\\n    58|\\n    59|    # List all skills (returns metadata only - token efficient)\\n    60|    result = skills_list()\\n    61|\\n    62|    # View a skill's main content (loads full instructions)\\n    63|    content = skill_view(\\\"axolotl\\\")\\n    64|\\n    65|    # View a reference file within a skill (loads linked file)\\n    66|    content = skill_view(\\\"axolotl\\\", \\\"references/dataset-formats.md\\\")\\n    67|\\\"\\\"\\\"\\n    68|\\n    69|import json\\n    70|import logging\\n    71|\\n    72|from hermes_constants import get_hermes_home, display_hermes_home\\n    73|import os\\n    74|import re\\n    75|from enum import Enum\\n    76|from pathlib import Path\\n    77|from typing import Dict, Any, List, Optional, Set, Tuple\\n    78|\\n    79|from tools.registry import registry, tool_error\\n    80|\\n    81|logger = logging.getLogger(__name__)\\n    82|\\n    83|\\n    84|# All skills live in ~/.hermes/skills/ (seeded from bundled skills/ on install).\\n    85|# This is the single source of truth -- agent edits, hub installs, and bundled\\n    86|# skills all coexist here without polluting the git repo.\\n    87|HERMES_HOME = get_hermes_home()\\n    88|SKILLS_DIR = HERMES_HOME / \\\"skills\\\"\\n    89|\\n    90|# Anthropic-recommended limits for progressive disclosure efficiency\\n    91|MAX_NAME_LENGTH = 64\\n    92|MAX_DESCRIPTION_LENGTH = 1024\\n    93|\\n    94|# Platform identifiers for the 'platforms' frontmatter field.\\n    95|# Maps user-friendly names to sys.platform prefixes.\\n    96|_PLATFORM_MAP = {\\n    97|    \\\"macos\\\": \\\"darwin\\\",\\n    98|    \\\"linux\\\": \\\"linux\\\",\\n    99|    \\\"windows\\\": \\\"win32\\\",\\n   100|}\\n   101|_ENV_VAR_NAME_RE = re.compile(r\\\"^[A-Za-z_][A-Za-z0-9_]*$\\\")\\n   102|_EXCLUDED_SKILL_DIRS = frozenset((\\\".git\\\", \\\".github\\\", \\\".hub\\\"))\\n   103|_REMOTE_ENV_BACKENDS = frozenset({\\\"docker\\\", \\\"singularity\\\", \\\"modal\\\", \\\"ssh\\\", \\\"daytona\\\"})\\n   104|_secret_capture_callback = None\\n   105|\\n   106|\\n   107|def load_env() -> Dict[str, str]:\\n   108|    \\\"\\\"\\\"Load profile-scoped environment variables from HERMES_HOME/.env.\\\"\\\"\\\"\\n   109|    env_path = get_hermes_home() / \\\".env\\\"\\n   110|    env_vars: Dict[str, str] = {}\\n   111|    if not env_path.exists():\\n   112|        return env_vars\\n   113|\\n   114|    with env_path.open() as f:\\n   115|        for line in f:\\n   116|            line = line.strip()\\n   117|            if line and not line.startswith(\\\"#\\\") and \\\"=\\\" in line:\\n   118|                key, _, value = line.partition(\\\"=\\\")\\n   119|                env_vars[key.strip()] = value.strip().strip(\\\"\\\\\\\"'\\\")\\n   120|    return env_vars\\n   121|\\n   122|\\n   123|class SkillReadinessStatus(str, Enum):\\n   124|    AVAILABLE = \\\"available\\\"\\n   125|    SETUP_NEEDED = \\\"setup_needed\\\"\\n   126|    UNSUPPORTED = \\\"unsupported\\\"\\n   127|\\n   128|\\n   129|# Prompt injection detection — shared by local-skill and plugin-skill paths.\\n   130|_INJECTION_PATTERNS: list = [\\n   131|    \\\"ignore previous instructions\\\",\\n   132|    \\\"ignore all previous\\\",\\n   133|    \\\"you are now\\\",\\n   134|    \\\"disregard your\\\",\\n   135|    \\\"forget your instructions\\\",\\n   136|    \\\"new instructions:\\\",\\n   137|    \\\"system prompt:\\\",\\n   138|    \\\"<system>\\\",\\n   139|    \\\"]]>\\\",\\n   140|]\\n   141|\\n   142|\\n   143|def set_secret_capture_callback(callback) -> None:\\n   144|    global _secret_capture_callback\\n   145|    _secret_capture_callback = callback\\n   146|\\n   147|\\n   148|def skill_matches_platform(frontmatter: Dict[str, Any]) -> bool:\\n   149|    \\\"\\\"\\\"Check if a skill is compatible with the current OS platform.\\n   150|\\n   151|    Delegates to ``agent.skill_utils.skill_matches_platform`` — kept here\\n   152|    as a public re-export so existing callers don't need updating.\\n   153|    \\\"\\\"\\\"\\n   154|    from agent.skill_utils import skill_matches_platform as _impl\\n   155|    return _impl(frontmatter)\\n   156|\\n   157|\\n   158|def _normalize_prerequisite_values(value: Any) -> List[str]:\\n   159|    if not value:\\n   160|        return []\\n   161|    if isinstance(value, str):\\n   162|        value = [value]\\n   163|    return [str(item) for item in value if str(item).strip()]\\n   164|\\n   165|\\n   166|def _collect_prerequisite_values(\\n   167|    frontmatter: Dict[str, Any],\\n   168|) -> Tuple[List[str], List[str]]:\\n   169|    prereqs = frontmatter.get(\\\"prerequisites\\\")\\n   170|    if not prereqs or not isinstance(prereqs, dict):\\n   171|        return [], []\\n   172|    return (\\n   173|        _normalize_prerequisite_values(prereqs.get(\\\"env_vars\\\")),\\n   174|        _normalize_prerequisite_values(prereqs.get(\\\"commands\\\")),\\n   175|    )\\n   176|\\n   177|\\n   178|def _normalize_setup_metadata(frontmatter: Dict[str, Any]) -> Dict[str, Any]:\\n   179|    setup = frontmatter.get(\\\"setup\\\")\\n   180|    if not isinstance(setup, dict):\\n   181|        return {\\\"help\\\": None, \\\"collect_secrets\\\": []}\\n   182|\\n   183|    help_text = setup.get(\\\"help\\\")\\n   184|    normalized_help = (\\n   185|        str(help_text).strip()\\n   186|        if isinstance(help_text, str) and help_text.strip()\\n   187|        else None\\n   188|    )\\n   189|\\n   190|    collect_secrets_raw = setup.get(\\\"collect_secrets\\\")\\n   191|    if isinstance(collect_secrets_raw, dict):\\n   192|        collect_secrets_raw = [collect_secrets_raw]\\n   193|    if not isinstance(collect_secrets_raw, list):\\n   194|        collect_secrets_raw = []\\n   195|\\n   196|    collect_secrets: List[Dict[str, Any]] = []\\n   197|    for item in collect_secrets_raw:\\n   198|        if not isinstance(item, dict):\\n   199|            continue\\n   200|\\n   201|        env_var = str(item.get(\\\"env_var\\\") or \\\"\\\").strip()\\n   202|        if not env_var:\\n   203|            continue\\n   204|\\n   205|        prompt = str(item.get(\\\"prompt\\\") or f\\\"Enter value for {env_var}\\\").strip()\\n   206|        provider_url = str(item.get(\\\"provider_url\\\") or item.get(\\\"url\\\") or \\\"\\\").strip()\\n   207|\\n   208|        entry: Dict[str, Any] = {\\n   209|            \\\"env_var\\\": env_var,\\n   210|            \\\"prompt\\\": prompt,\\n   211|            \\\"secret\\\": bool(item.get(\\\"secret\\\", True)),\\n   212|        }\\n   213|        if provider_url:\\n   214|            entry[\\\"provider_url\\\"] = provider_url\\n   215|        collect_secrets.append(entry)\\n   216|\\n   217|    return {\\n   218|        \\\"help\\\": normalized_help,\\n   219|        \\\"collect_secrets\\\": collect_secrets,\\n   220|    }\\n   221|\\n   222|\\n   223|def _get_required_environment_variables(\\n   224|    frontmatter: Dict[str, Any],\\n   225|    legacy_env_vars: List[str] | None = None,\\n   226|) -> List[Dict[str, Any]]:\\n   227|    setup = _normalize_setup_metadata(frontmatter)\\n   228|    required_raw = frontmatter.get(\\\"required_environment_variables\\\")\\n   229|    if isinstance(required_raw, dict):\\n   230|        required_raw = [required_raw]\\n   231|    if not isinstance(required_raw, list):\\n   232|        required_raw = []\\n   233|\\n   234|    required: List[Dict[str, Any]] = []\\n   235|    seen: set[str] = set()\\n   236|\\n   237|    def _append_required(entry: Dict[str, Any]) -> None:\\n   238|        env_name = str(entry.get(\\\"name\\\") or entry.get(\\\"env_var\\\") or \\\"\\\").strip()\\n   239|        if not env_name or env_name in seen:\\n   240|            return\\n   241|        if not _ENV_VAR_NAME_RE.match(env_name):\\n   242|            return\\n   243|\\n   244|        normalized: Dict[str, Any] = {\\n   245|            \\\"name\\\": env_name,\\n   246|            \\\"prompt\\\": str(entry.get(\\\"prompt\\\") or f\\\"Enter value for {env_name}\\\").strip(),\\n   247|        }\\n   248|\\n   249|        help_text = (\\n   250|            entry.get(\\\"help\\\")\\n   251|            or entry.get(\\\"provider_url\\\")\\n   252|            or entry.get(\\\"url\\\")\\n   253|            or setup.get(\\\"help\\\")\\n   254|        )\\n   255|        if isinstance(help_text, str) and help_text.strip():\\n   256|            normalized[\\\"help\\\"] = help_text.strip()\\n   257|\\n   258|        required_for = entry.get(\\\"required_for\\\")\\n   259|        if isinstance(required_for, str) and required_for.strip():\\n   260|            normalized[\\\"required_for\\\"] = required_for.strip()\\n   261|\\n   262|        if entry.get(\\\"optional\\\"):\\n   263|            normalized[\\\"optional\\\"] = True\\n   264|\\n   265|        seen.add(env_name)\\n   266|        required.append(normalized)\\n   267|\\n   268|    for item in required_raw:\\n   269|        if isinstance(item, str):\\n   270|            _append_required({\\\"name\\\": item})\\n   271|            continue\\n   272|        if isinstance(item, dict):\\n   273|            _append_required(item)\\n   274|\\n   275|    for item in setup[\\\"collect_secrets\\\"]:\\n   276|        _append_required(\\n   277|            {\\n   278|                \\\"name\\\": item.get(\\\"env_var\\\"),\\n   279|                \\\"prompt\\\": item.get(\\\"prompt\\\"),\\n   280|                \\\"help\\\": item.get(\\\"provider_url\\\") or setup.get(\\\"help\\\"),\\n   281|            }\\n   282|        )\\n   283|\\n   284|    if legacy_env_vars is None:\\n   285|        legacy_env_vars, _ = _collect_prerequisite_values(frontmatter)\\n   286|    for env_var in legacy_env_vars:\\n   287|        _append_required({\\\"name\\\": env_var})\\n   288|\\n   289|    return required\\n   290|\\n   291|\\n   292|def _capture_required_environment_variables(\\n   293|    skill_name: str,\\n   294|    missing_entries: List[Dict[str, Any]],\\n   295|) -> Dict[str, Any]:\\n   296|    if not missing_entries:\\n   297|        return {\\n   298|            \\\"missing_names\\\": [],\\n   299|            \\\"setup_skipped\\\": False,\\n   300|            \\\"gateway_setup_hint\\\": None,\\n   301|        }\\n   302|\\n   303|    missing_names = [entry[\\\"name\\\"] for entry in missing_entries]\\n   304|    if _is_gateway_surface():\\n   305|        return {\\n   306|            \\\"missing_names\\\": missing_names,\\n   307|            \\\"setup_skipped\\\": False,\\n   308|            \\\"gateway_setup_hint\\\": _gateway_setup_hint(),\\n   309|        }\\n   310|\\n   311|    if _secret_capture_callback is None:\\n   312|        return {\\n   313|            \\\"missing_names\\\": missing_names,\\n   314|            \\\"setup_skipped\\\": False,\\n   315|            \\\"gateway_setup_hint\\\": None,\\n   316|        }\\n   317|\\n   318|    setup_skipped = False\\n   319|    remaining_names: List[str] = []\\n   320|\\n   321|    for entry in missing_entries:\\n   322|        metadata = {\\\"skill_name\\\": skill_name}\\n   323|        if entry.get(\\\"help\\\"):\\n   324|            metadata[\\\"help\\\"] = entry[\\\"help\\\"]\\n   325|        if entry.get(\\\"required_for\\\"):\\n   326|            metadata[\\\"required_for\\\"] = entry[\\\"required_for\\\"]\\n   327|\\n   328|        try:\\n   329|            callback_result = _secret_capture_callback(\\n   330|                entry[\\\"name\\\"],\\n   331|                entry[\\\"prompt\\\"],\\n   332|                metadata,\\n   333|            )\\n   334|        except Exception:\\n   335|            logger.warning(\\n   336|                f\\\"Secret capture callback failed for {entry['name']}\\\", exc_info=True\\n   337|            )\\n   338|            callback_result = {\\n   339|                \\\"success\\\": False,\\n   340|                \\\"stored_as\\\": entry[\\\"name\\\"],\\n   341|                \\\"validated\\\": False,\\n   342|                \\\"skipped\\\": True,\\n   343|            }\\n   344|\\n   345|        success = isinstance(callback_result, dict) and bool(\\n   346|            callback_result.get(\\\"success\\\")\\n   347|        )\\n   348|        skipped = isinstance(callback_result, dict) and bool(\\n   349|            callback_result.get(\\\"skipped\\\")\\n   350|        )\\n   351|        if success and not skipped:\\n   352|            continue\\n   353|\\n   354|        setup_skipped = True\\n   355|        remaining_names.append(entry[\\\"name\\\"])\\n   356|\\n   357|    return {\\n   358|        \\\"missing_names\\\": remaining_names,\\n   359|        \\\"setup_skipped\\\": setup_skipped,\\n   360|        \\\"gateway_setup_hint\\\": None,\\n   361|    }\\n   362|\\n   363|\\n   364|def _is_gateway_surface() -> bool:\\n   365|    if os.getenv(\\\"HERMES_GATEWAY_SESSION\\\"):\\n   366|        return True\\n   367|    from gateway.session_context import get_session_env\\n   368|    return bool(get_session_env(\\\"HERMES_SESSION_PLATFORM\\\"))\\n   369|\\n   370|\\n   371|def _get_terminal_backend_name() -> str:\\n   372|    return str(os.getenv(\\\"TERMINAL_ENV\\\", \\\"local\\\")).strip().lower() or \\\"local\\\"\\n   373|\\n   374|\\n   375|def _is_env_var_persisted(\\n   376|    var_name: str, env_snapshot: Dict[str, str] | None = None\\n   377|) -> bool:\\n   378|    if env_snapshot is None:\\n   379|        env_snapshot = load_env()\\n   380|    if var_name in env_snapshot:\\n   381|        return bool(env_snapshot.get(var_name))\\n   382|    return bool(os.getenv(var_name))\\n   383|\\n   384|\\n   385|def _remaining_required_environment_names(\\n   386|    required_env_vars: List[Dict[str, Any]],\\n   387|    capture_result: Dict[str, Any],\\n   388|    *,\\n   389|    env_snapshot: Dict[str, str] | None = None,\\n   390|) -> List[str]:\\n   391|    missing_names = set(capture_result[\\\"missing_names\\\"])\\n   392|\\n   393|    if env_snapshot is None:\\n   394|        env_snapshot = load_env()\\n   395|    remaining = []\\n   396|    for entry in required_env_vars:\\n   397|        name = entry[\\\"name\\\"]\\n   398|        if entry.get(\\\"optional\\\"):\\n   399|            continue\\n   400|        if name in missing_names or not _is_env_var_persisted(name, env_snapshot):\\n   401|            remaining.append(name)\\n   402|    return remaining\\n   403|\\n   404|\\n   405|def _gateway_setup_hint() -> str:\\n   406|    try:\\n   407|        from gateway.platforms.base import GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE\\n   408|\\n   409|        return GATEWAY_SECRET_CAPTURE_UNSUPPORTED_MESSAGE\\n   410|    except Exception:\\n   411|        return f\\\"Secure secret entry is not available. Load this skill in the local CLI to be prompted, or add the key to {display_hermes_home()}/.env manually.\\\"\\n   412|\\n   413|\\n   414|def _build_setup_note(\\n   415|    readiness_status: SkillReadinessStatus,\\n   416|    missing: List[str],\\n   417|    setup_help: str | None = None,\\n   418|) -> str | None:\\n   419|    if readiness_status == SkillReadinessStatus.SETUP_NEEDED:\\n   420|        missing_str = \\\", \\\".join(missing) if missing else \\\"required prerequisites\\\"\\n   421|        note = f\\\"Setup needed before using this skill: missing {missing_str}.\\\"\\n   422|        if setup_help:\\n   423|            return f\\\"{note} {setup_help}\\\"\\n   424|        return note\\n   425|    return None\\n   426|\\n   427|\\n   428|def check_skills_requirements() -> bool:\\n   429|    \\\"\\\"\\\"Skills are always available -- the directory is created on first use if needed.\\\"\\\"\\\"\\n   430|    return True\\n   431|\\n   432|\\n   433|def _parse_frontmatter(content: str) -> Tuple[Dict[str, Any], str]:\\n   434|    \\\"\\\"\\\"Parse YAML frontmatter from markdown content.\\n   435|\\n   436|    Delegates to ``agent.skill_utils.parse_frontmatter`` — kept here\\n   437|    as a public re-export so existing callers don't need updating.\\n   438|    \\\"\\\"\\\"\\n   439|    from agent.skill_utils import parse_frontmatter\\n   440|    return parse_frontmatter(content)\\n   441|\\n   442|\\n   443|def _get_category_from_path(skill_path: Path) -> Optional[str]:\\n   444|    \\\"\\\"\\\"\\n   445|    Extract category from skill path based on directory structure.\\n   446|\\n   447|    For paths like: ~/.hermes/skills/mlops/axolotl/SKILL.md -> \\\"mlops\\\"\\n   448|    Also works for external skill dirs configured via skills.external_dirs.\\n   449|    \\\"\\\"\\\"\\n   450|    # Try the module-level SKILLS_DIR first (respects monkeypatching in tests),\\n   451|    # then fall back to external dirs from config.\\n   452|    dirs_to_check = [SKILLS_DIR]\\n   453|    try:\\n   454|        from agent.skill_utils import get_external_skills_dirs\\n   455|        dirs_to_check.extend(get_external_skills_dirs())\\n   456|    except Exception:\\n   457|        pass\\n   458|    for skills_dir in dirs_to_check:\\n   459|        try:\\n   460|            rel_path = skill_path.relative_to(skills_dir)\\n   461|            parts = rel_path.parts\\n   462|            if len(parts) >= 3:\\n   463|                return parts[0]\\n   464|        except ValueError:\\n   465|            continue\\n   466|    return None\\n   467|\\n   468|\\n   469|def _parse_tags(tags_value) -> List[str]:\\n   470|    \\\"\\\"\\\"\\n   471|    Parse tags from frontmatter value.\\n   472|\\n   473|    Handles:\\n   474|    - Already-parsed list (from yaml.safe_load): [tag1, tag2]\\n   475|    - String with brackets: \\\"[tag1, tag2]\\\"\\n   476|    - Comma-separated string: \\\"tag1, tag2\\\"\\n   477|\\n   478|    Args:\\n   479|        tags_value: Raw tags value — may be a list or string\\n   480|\\n   481|    Returns:\\n   482|        List of tag strings\\n   483|    \\\"\\\"\\\"\\n   484|    if not tags_value:\\n   485|        return []\\n   486|\\n   487|    # yaml.safe_load already returns a list for [tag1, tag2]\\n   488|    if isinstance(tags_value, list):\\n   489|        return [str(t).strip() for t in tags_value if t]\\n   490|\\n   491|    # String fallback — handle bracket-wrapped or comma-separated\\n   492|    tags_value = str(tags_value).strip()\\n   493|    if tags_value.startswith(\\\"[\\\") and tags_value.endswith(\\\"]\\\"):\\n   494|        tags_value = tags_value[1:-1]\\n   495|\\n   496|    return [t.strip().strip(\\\"\\\\\\\"'\\\") for t in tags_value.split(\\\",\\\") if t.strip()]\\n   497|\\n   498|\\n   499|\\n   500|def _get_disabled_skill_names() -> Set[str]:\\n   501|\", \"total_lines\": 1419, \"file_size\": 51465, \"truncated\": true, \"hint\": \"Use offset=501 to continue reading (showing 1-500 of 1419 lines)\", \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "chatcmpl-tool-88038e97fc14fa25"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to call the skills_list tool. It seems the tools module provides a tool called skills_list that we can call? However, we don't have a direct tool invocation mechanism; we have functions like patch, read_file, search_files, write_file. There's no explicit \"invoke a tool\" function. But perhaps the skills_list tool is exposed via the tools registry and we can call it via the tools.skills_tool.skills_list function? However we cannot execute arbitrary Python code? We can maybe call the tool via the tools.skills_tool.skills_list function by importing? Not sure.\n\nBut the instruction: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". Possibly we need to invoke the skills_list tool via the tool system. In the Hermes Agent environment, there is a tool registry; we can call tools by name. However, the provided functions don't include a generic \"invoke tool\" function. Perhaps we can use the \"tools.skills_tool.skills_list\" function by reading its source and simulating? That seems overkill.\n\nMaybe there is a command-line tool? The skills_list might be a CLI command? Let's search for skills_list in the files.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to call the skills_list tool. It seems the tools module provides a tool called skills_list that we can call? However, we don't have a direct tool invocation mechanism; we have functions like patch, read_file, search_files, write_file. There's no explicit \"invoke a tool\" function. But perhaps the skills_list tool is exposed via the tools registry and we can call it via the tools.skills_tool.skills_list function? However we cannot execute arbitrary Python code? We can maybe call the tool via the tools.skills_tool.skills_list function by importing? Not sure.\n\nBut the instruction: \"Get the list of all skills using the skills_list tool and save it to /tmp/skills.json\". Possibly we need to invoke the skills_list tool via the tool system. In the Hermes Agent environment, there is a tool registry; we can call tools by name. However, the provided functions don't include a generic \"invoke tool\" function. Perhaps we can use the \"tools.skills_tool.skills_list\" function by reading its source and simulating? That seems overkill.\n\nMaybe there is a command-line tool? The skills_list might be a CLI command? Let's search for skills_list in the files.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-a978b454645363eb",
          "call_id": "chatcmpl-tool-a978b454645363eb",
          "response_item_id": "fc_chatcmpl-tool-a978b454645363eb",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"skills_list\", \"target\": \"content\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 48, \"matches\": [{\"path\": \"./agent/display.py\", \"line\": 187, \"content\": \"        \\\"skill_view\\\": \\\"name\\\", \\\"skills_list\\\": \\\"category\\\",\"}, {\"path\": \"./agent/display.py\", \"line\": 907, \"content\": \"    if tool_name == \\\"skills_list\\\":\"}, {\"path\": \"./agent/context_compressor.py\", \"line\": 146, \"content\": \"    if tool_name in (\\\"skill_view\\\", \\\"skills_list\\\", \\\"skill_manage\\\"):\"}, {\"path\": \"./cli-config.yaml.example\", \"line\": 584, \"content\": \"#   skills       - skills_list, skill_view\"}, {\"path\": \"./cli-config.yaml.example\", \"line\": 612, \"content\": \"#   skills       - Load skill documents (skills_list, skill_view)\"}, {\"path\": \"./website/docs/reference/toolsets-reference.md\", \"line\": 69, \"content\": \"| `skills` | `skill_manage`, `skill_view`, `skills_list` | Skill CRUD and browsing. |\"}, {\"path\": \"./website/docs/reference/tools-reference.md\", \"line\": 129, \"content\": \"| `skills_list` | List available skills (name + description). Use skill_view(name) to load full content. | — |\"}, {\"path\": \"./website/docs/developer-guide/creating-skills.md\", \"line\": 106, \"content\": \"When set, the skill is automatically hidden from the system prompt, `skills_list()`, and slash commands on incompatible platforms. If omitted or empty, the skill loads on all platforms (backward compatible).\"}, {\"path\": \"./website/docs/guides/work-with-skills.md\", \"line\": 79, \"content\": \"1. **`skills_list()`** — compact list of all skills (~3k tokens). Loaded at session start.\"}, {\"path\": \"./website/docs/guides/work-with-skills.md\", \"line\": 101, \"content\": \"2. It appears in your `skills_list` output\"}, {\"path\": \"./website/docs/guides/work-with-skills.md\", \"line\": 132, \"content\": \"Plugin skills are **not** listed in the system prompt and don't appear in `skills_list`. They're opt-in — load them explicitly when you know a plugin provides one. When loaded, the agent sees a banner listing sibling skills from the same plugin.\"}, {\"path\": \"./website/docs/user-guide/features/skills.md\", \"line\": 49, \"content\": \"Level 0: skills_list()           → [{name, description, category}, ...]   (~3k tokens)\"}, {\"path\": \"./website/docs/user-guide/features/skills.md\", \"line\": 108, \"content\": \"When set, the skill is automatically hidden from the system prompt, `skills_list()`, and slash commands on incompatible platforms. If omitted, the skill loads on all platforms.\"}, {\"path\": \"./website/docs/user-guide/features/skills.md\", \"line\": 212, \"content\": \"- **Full integration**: External skills appear in the system prompt index, `skills_list`, `skill_view`, and as `/skill-name` slash commands — no different from local skills.\"}, {\"path\": \"./tests/run_agent/test_run_agent.py\", \"line\": 677, \"content\": \"        tools = _make_tool_defs(\\\"web_search\\\", \\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\")\"}, {\"path\": \"./tests/run_agent/test_run_agent.py\", \"line\": 680, \"content\": \"            \\\"skills_list\\\": \\\"skills\\\",\"}, {\"path\": \"./tests/hermes_cli/test_web_server.py\", \"line\": 544, \"content\": \"    def test_skills_list(self):\"}, {\"path\": \"./tests/hermes_cli/test_web_server.py\", \"line\": 553, \"content\": \"    def test_skills_list_includes_disabled_skills(self, monkeypatch):\"}, {\"path\": \"./tests/hermes_cli/test_web_server.py\", \"line\": 629, \"content\": \"                \\\"skills\\\": [\\\"skills_list\\\", \\\"skill_view\\\"],\"}, {\"path\": \"./tests/hermes_cli/test_web_server.py\", \"line\": 655, \"content\": \"                \\\"tools\\\": [\\\"skill_view\\\", \\\"skills_list\\\"],\"}, {\"path\": \"./tests/tools/test_skills_tool.py\", \"line\": 18, \"content\": \"    skills_list,\"}, {\"path\": \"./tests/tools/test_skills_tool.py\", \"line\": 260, \"content\": \"# skills_list\"}, {\"path\": \"./tests/tools/test_skills_tool.py\", \"line\": 268, \"content\": \"            raw = skills_list()\"}, {\"path\": \"./tests/tools/test_skills_tool.py\", \"line\": 278, \"content\": \"            raw = skills_list()\"}, {\"path\": \"./tests/tools/test_skills_tool.py\", \"line\": 286, \"content\": \"            raw = skills_list(category=\\\"devops\\\")\"}, {\"path\": \"./model_tools.py\", \"line\": 172, \"content\": \"    \\\"skills_tools\\\": [\\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\"],\"}, {\"path\": \"./tools/skill_manager_tool.py\", \"line\": 361, \"content\": \"        return {\\\"success\\\": False, \\\"error\\\": f\\\"Skill '{name}' not found. Use skills_list() to see available skills.\\\"}\"}, {\"path\": \"./tools/file_tools.py\", \"line\": 324, \"content\": \"                        \\\"Use the skills_list or skill_view tools instead.\\\"\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 10, \"content\": \"- Metadata (name ≤64 chars, description ≤1024 chars) - shown in skills_list\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 53, \"content\": \"- skills_list: List skills with metadata (progressive disclosure tier 1)\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 57, \"content\": \"    from tools.skills_tool import skills_list, skill_view, check_skills_requirements\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 60, \"content\": \"    result = skills_list()\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 647, \"content\": \"def skills_list(category: str = None, task_id: str = None) -> str:\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 935, \"content\": \"                    \\\"hint\\\": \\\"Use skills_list to see all available skills\\\",\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 1326, \"content\": \"    result = json.loads(skills_list())\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 1367, \"content\": \"    \\\"name\\\": \\\"skills_list\\\",\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 1389, \"content\": \"                \\\"description\\\": \\\"The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans').\\\",\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 1401, \"content\": \"    name=\\\"skills_list\\\",\"}, {\"path\": \"./tools/skills_tool.py\", \"line\": 1404, \"content\": \"    handler=lambda args, **kw: skills_list(\"}, {\"path\": \"./hermes_cli/main.py\", \"line\": 5237, \"content\": \"    skills_list = skills_subparsers.add_parser(\\\"list\\\", help=\\\"List installed skills\\\")\"}, {\"path\": \"./hermes_cli/main.py\", \"line\": 5238, \"content\": \"    skills_list.add_argument(\\\"--source\\\", default=\\\"all\\\", choices=[\\\"all\\\", \\\"hub\\\", \\\"builtin\\\", \\\"local\\\"])\"}, {\"path\": \"./environments/tool_context.py\", \"line\": 428, \"content\": \"            tool_name: Name of the tool (e.g., \\\"vision_analyze\\\", \\\"skills_list\\\")\"}, {\"path\": \"./run_agent.py\", \"line\": 227, \"content\": \"    \\\"skills_list\\\",\"}, {\"path\": \"./run_agent.py\", \"line\": 3250, \"content\": \"        has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view', 'skill_manage'])\"}, {\"path\": \"./toolsets.py\", \"line\": 41, \"content\": \"    \\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\",\"}, {\"path\": \"./toolsets.py\", \"line\": 108, \"content\": \"        \\\"tools\\\": [\\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\"],\"}, {\"path\": \"./toolsets.py\", \"line\": 233, \"content\": \"            \\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\",\"}, {\"path\": \"./toolsets.py\", \"line\": 257, \"content\": \"            \\\"skills_list\\\", \\\"skill_view\\\", \\\"skill_manage\\\",\"}]}",
      "tool_call_id": "chatcmpl-tool-a978b454645363eb"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to actually call the skills_list tool. We haven't seen a tool invocation mechanism. The assistant can call tools via the functions namespace? Actually the tools listed are file manipulation tools. There's no direct tool invocation for skills_list. But the instruction says: \"Get the list of all skills using the skills_list tool\". Perhaps we need to import and call the function from the skills_tool module? We can write a small Python script to import and call skills_list() and then write the output to /tmp/skills.json.\n\nWe have the skills_tool.py file at /opt/hermes/tools/skills_tool.py. We can write a script that imports it and calls skills_list().\n\nLet's first check if there's a __init__ or if we need to adjust sys.path. We'll create a temporary Python script in /tmp, run it, capture output, and write to /tmp/skills.json.\n\nAlternatively, we could directly read the skills index cache? But the proper way is to call the function.\n\nLet's do:\n\n1. Create a script that imports skills_list from tools.skills_tool (need to add /opt/hermes to sys.path).\n2. Call skills_list() (no arguments) and get the JSON string.\n3. Write that string to /tmp/skills.json.\n\nWe'll need to ensure we are in the right environment. We can just run python3.\n\nLet's proceed.\n\nFirst, let's check the current directory: we are likely at /opt/hermes? The workspace path is /opt/hermes. We'll cd there.\n\nWe'll create a temporary script.\n\nLet's do it step by step using the tools: we can write_file to create the script, then we need to execute it. However we don't have an exec tool. We can use the patch tool? No.\n\nWe might need to rely on the assistant's ability to run commands? Not provided. The available tools are only file operations. We cannot run arbitrary code. Hmm.\n\nBut maybe the skills_list tool is actually accessible via the assistant's tool set? The initial system message said we have access to tools: patch, read_file, search_files, write_file. No execution tool.\n\nThus we cannot call skills_list directly. However we could read the pre-built index cache files? The skills_list function likely reads from the skills directory and builds a list. But we could simulate by reading the SKILL.md files? That's complex.\n\nMaybe there is a pre-existing JSON file that stores the list? Look at the search results: there are index-cache files like \"/opt/hermes/skills/index-cache/anthropics_skills_skills_.json\". Those might be the cached list.\n\nLet's examine one of those.\n\nWe'll read that file to see its format.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to actually call the skills_list tool. We haven't seen a tool invocation mechanism. The assistant can call tools via the functions namespace? Actually the tools listed are file manipulation tools. There's no direct tool invocation for skills_list. But the instruction says: \"Get the list of all skills using the skills_list tool\". Perhaps we need to import and call the function from the skills_tool module? We can write a small Python script to import and call skills_list() and then write the output to /tmp/skills.json.\n\nWe have the skills_tool.py file at /opt/hermes/tools/skills_tool.py. We can write a script that imports it and calls skills_list().\n\nLet's first check if there's a __init__ or if we need to adjust sys.path. We'll create a temporary Python script in /tmp, run it, capture output, and write to /tmp/skills.json.\n\nAlternatively, we could directly read the skills index cache? But the proper way is to call the function.\n\nLet's do:\n\n1. Create a script that imports skills_list from tools.skills_tool (need to add /opt/hermes to sys.path).\n2. Call skills_list() (no arguments) and get the JSON string.\n3. Write that string to /tmp/skills.json.\n\nWe'll need to ensure we are in the right environment. We can just run python3.\n\nLet's proceed.\n\nFirst, let's check the current directory: we are likely at /opt/hermes? The workspace path is /opt/hermes. We'll cd there.\n\nWe'll create a temporary script.\n\nLet's do it step by step using the tools: we can write_file to create the script, then we need to execute it. However we don't have an exec tool. We can use the patch tool? No.\n\nWe might need to rely on the assistant's ability to run commands? Not provided. The available tools are only file operations. We cannot run arbitrary code. Hmm.\n\nBut maybe the skills_list tool is actually accessible via the assistant's tool set? The initial system message said we have access to tools: patch, read_file, search_files, write_file. No execution tool.\n\nThus we cannot call skills_list directly. However we could read the pre-built index cache files? The skills_list function likely reads from the skills directory and builds a list. But we could simulate by reading the SKILL.md files? That's complex.\n\nMaybe there is a pre-existing JSON file that stores the list? Look at the search results: there are index-cache files like \"/opt/hermes/skills/index-cache/anthropics_skills_skills_.json\". Those might be the cached list.\n\nLet's examine one of those.\n\nWe'll read that file to see its format.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-a1c58a9dcc377add",
          "call_id": "chatcmpl-tool-a1c58a9dcc377add",
          "response_item_id": "fc_chatcmpl-tool-a1c58a9dcc377add",
          "type": "function",
          "function": {
            "name": "read_file",
            "arguments": "{\"path\": \"/opt/hermes/skills/index-cache/anthropics_skills_skills_.json\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"content\": \"     1|[{\\\"name\\\": \\\"algorithmic-art\\\", \\\"description\\\": \\\"Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.\\\", \\\"source\\\": \\\"github\\\", \\\"identifier\\\": \\\"anthropics/skills/skills/algorithmic-art\\\", \\\"trust_level\\\": \\\"trusted\\\", \\\"repo\\\": \\\"anthropics/skills\\\", \\\"path\\\": \\\"skills/algorithmic-art\\\", \\\"tags\\\": []}, {\\\"name\\\": \\\"brand-guidelines\\\", \\\"description\\\": \\\"Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.\\\", \\\"source\\\": \\\"github\\\", \\\"identifier\\\": \\\"anthropics/skills/skills/brand-guidelines\\\", \\\"trust_level\\\": \\\"trusted\\\", \\\"repo\\\": \\\"anthropics/skills\\\", \\\"path\\\": \\\"skills/brand-guidelines\\\", \\\"tags\\\": []}, {\\\"name\\\": \\\"canvas-design\\\", \\\"description\\\": \\\"Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.\\\", \\\"source\\\": \\\"github\\\", \\\"identifier\\\": \\\"anthropics/skills/skills/canvas-design\\\", \\\"trust_level\\\": \\\"trusted\\\", \\\"repo\\\": \\\"anthropics/skills\\\", \\\"path\\\": \\\"skills/canvas-design\\\", \\\"tags\\\": []}, {\\\"name\\\": \\\"doc-coauthoring\\\", \\\"description\\\": \\\"Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.... [truncated]\", \"total_lines\": 0, \"file_size\": 9858, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "chatcmpl-tool-a1c58a9dcc377add"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to get the list of all skills using the skills_list tool and save to /tmp/skills.json.\nWe have the skills_tool module that defines skills_list function. We can call it directly via Python.\n\nLet's create a small Python script to call skills_list() and write the result to /tmp/skills.json.\nWe'll need to import from tools.skills_tool.\n\nWe are in /opt/hermes workspace. So we can run python -c or write a script.\n\nLet's do it step by step.\n\nFirst, check if we can import. We'll write a temporary script and execute.\n\nWe'll use the write_file tool to create a script, then run it with python.\n\nBut we can also directly call the function via python -c with proper imports.\n\nLet's try:\n\npython3 -c \"from tools.skills_tool import skills_list; import json; print(skills_list())\"\n\nBut we need to ensure the module is in the Python path. Since we are in /opt/hermes, and the tools module is there, it should work.\n\nLet's test with a quick command using the tool? We don't have a direct tool to run shell commands, but we can use write_file and then maybe use the agent to execute? Actually we can only use the provided tools. There's no tool to run arbitrary shell commands. However we can use the skills_list tool itself? Wait, the skills_list is a tool that the agent can call. Indeed, the agent has access to the skills_list tool (as defined in the tools). The user asked to use the skills_list tool. So we can invoke the tool via the agent's tool calling mechanism? In this environment, we can call tools by name? The system provides a set of tools we can invoke: patch, read_file, search_files, write_file. There's no generic \"invoke tool\" tool. However the skills_list tool is defined in the tools.skills_tool module, but we cannot directly call it unless we write code and execute it.\n\nBut we can use the write_file tool to create a Python script that imports and calls skills_list, then we need to execute it. How to execute? We don't have a tool to run python scripts. However we can maybe use the agent's own capability? The agent is a language model that can run Python code? Not directly.\n\nWait, maybe we can use the skills_list tool via the agent's internal tool calling? In the Hermes agent system, the agent can call tools like skills_list, skill_view, etc. But in this specific chat interface, we are the agent? Actually we are the Hermes Agent assistant, and we have access to the tools listed (patch, read_file, search_files, write_file). We do not have direct access to skills_list as a tool we can call. However the task says: \"Get the list of all skills using the skills_list tool\". That implies we should invoke the skills_list tool. But we don't have a tool to invoke arbitrary tools. However we can simulate by calling the function via Python if we can run Python.\n\nWe can write a Python script and then maybe use the read_file to read the output? No.\n\nAlternative: We can search for where skills_list is implemented and maybe we can just copy the output from a test? But better to actually call it.\n\nWe can use the write_file tool to create a script that calls skills_list and writes the result to /tmp/skills.json. Then we can perhaps use the read_file tool to read that file? But we need to execute the script to produce the file. How to execute? We don't have an execute tool.\n\nWait, maybe we can use the agent's own ability to run Python code because the agent is a language model that can execute code? Not in this interface.\n\nLet's think: The tools we have are limited to file operations. However we might be able to use the skills_list tool by invoking it via the agent's internal tool calling mechanism if we format our request appropriately? In the typical agent loops, the agent can decide to call a tool by outputting a specific JSON. But here we are the agent, and we are supposed to use the tools provided. The tools provided are only the six listed. There's no \"invoke_skill_tool\". So maybe we misinterpret: The skills_list tool is actually accessible via the tools namespace? Let's check if there is a tool named \"skills_list\" among the tools we have? No, we have patch, read_file, search_files, write_file. No.\n\nThus we need to call the function via Python and we need to execute Python. Perhaps we can use the write_file tool to create a script and then rely on the fact that the agent can run Python code? Not sure.\n\nMaybe we can use the read_file tool to read the output of skills_list from somewhere else? For example, maybe there is a cached index? We saw index-cache files. Those are for external skills (anthropics, openai, etc). But we need all skills, including local ones.\n\nAlternatively, we can look at the skills_tool.py file and see how skills_list works, then replicate its logic? That's possible but tedious.\n\nBetter: Let's try to see if we can import and call the function using the python interpreter via the write_file and then maybe we can use the read_file to get the output if we write the script to output to a file and then we can read that file? But we still need to execute the script.\n\nWait, we can use the write_file tool to create a Python script that, when read by the agent, the agent could execute? Not.\n\nMaybe we can use the search_files tool to find a test that calls skills_list and prints output, and then we can copy that output? For example, in tests/tools/test_skills_tool.py there are calls to skills_list(). We could read that test file and see if there is any example output? Not.\n\nLet's examine the test file to see if it prints something.\n\nWe'll read the test file.\n\nBut we need to produce the actual list.\n\nMaybe we can use the agent's own ability to call tools: In the initial system prompt, it says we are Hermes Agent, an intelligent AI assistant created by Nous Research. It says we assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. It says we have tools. The tools listed are the ones we have. So we cannot call skills_list directly.\n\nThus we must implement the functionality ourselves using file operations.\n\nLet's examine the skills_tool.py to understand how skills_list works, then we can replicate by scanning the skills directory.\n\nWe saw that skills_list reads from SKILLS_DIR = HERMES_HOME / \"skills\". And HERMES_HOME = get_hermes_home().\n\nWe need to know what HERMES_HOME is. Likely it's ~/.hermes or /opt/hermes? Let's check constants.\n\nWe can search for get_hermes_home.\n\nLet's do that.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to get the list of all skills using the skills_list tool and save to /tmp/skills.json.\nWe have the skills_tool module that defines skills_list function. We can call it directly via Python.\n\nLet's create a small Python script to call skills_list() and write the result to /tmp/skills.json.\nWe'll need to import from tools.skills_tool.\n\nWe are in /opt/hermes workspace. So we can run python -c or write a script.\n\nLet's do it step by step.\n\nFirst, check if we can import. We'll write a temporary script and execute.\n\nWe'll use the write_file tool to create a script, then run it with python.\n\nBut we can also directly call the function via python -c with proper imports.\n\nLet's try:\n\npython3 -c \"from tools.skills_tool import skills_list; import json; print(skills_list())\"\n\nBut we need to ensure the module is in the Python path. Since we are in /opt/hermes, and the tools module is there, it should work.\n\nLet's test with a quick command using the tool? We don't have a direct tool to run shell commands, but we can use write_file and then maybe use the agent to execute? Actually we can only use the provided tools. There's no tool to run arbitrary shell commands. However we can use the skills_list tool itself? Wait, the skills_list is a tool that the agent can call. Indeed, the agent has access to the skills_list tool (as defined in the tools). The user asked to use the skills_list tool. So we can invoke the tool via the agent's tool calling mechanism? In this environment, we can call tools by name? The system provides a set of tools we can invoke: patch, read_file, search_files, write_file. There's no generic \"invoke tool\" tool. However the skills_list tool is defined in the tools.skills_tool module, but we cannot directly call it unless we write code and execute it.\n\nBut we can use the write_file tool to create a Python script that imports and calls skills_list, then we need to execute it. How to execute? We don't have a tool to run python scripts. However we can maybe use the agent's own capability? The agent is a language model that can run Python code? Not directly.\n\nWait, maybe we can use the skills_list tool via the agent's internal tool calling? In the Hermes agent system, the agent can call tools like skills_list, skill_view, etc. But in this specific chat interface, we are the agent? Actually we are the Hermes Agent assistant, and we have access to the tools listed (patch, read_file, search_files, write_file). We do not have direct access to skills_list as a tool we can call. However the task says: \"Get the list of all skills using the skills_list tool\". That implies we should invoke the skills_list tool. But we don't have a tool to invoke arbitrary tools. However we can simulate by calling the function via Python if we can run Python.\n\nWe can write a Python script and then maybe use the read_file to read the output? No.\n\nAlternative: We can search for where skills_list is implemented and maybe we can just copy the output from a test? But better to actually call it.\n\nWe can use the write_file tool to create a script that calls skills_list and writes the result to /tmp/skills.json. Then we can perhaps use the read_file tool to read that file? But we need to execute the script to produce the file. How to execute? We don't have an execute tool.\n\nWait, maybe we can use the agent's own ability to run Python code because the agent is a language model that can execute code? Not in this interface.\n\nLet's think: The tools we have are limited to file operations. However we might be able to use the skills_list tool by invoking it via the agent's internal tool calling mechanism if we format our request appropriately? In the typical agent loops, the agent can decide to call a tool by outputting a specific JSON. But here we are the agent, and we are supposed to use the tools provided. The tools provided are only the six listed. There's no \"invoke_skill_tool\". So maybe we misinterpret: The skills_list tool is actually accessible via the tools namespace? Let's check if there is a tool named \"skills_list\" among the tools we have? No, we have patch, read_file, search_files, write_file. No.\n\nThus we need to call the function via Python and we need to execute Python. Perhaps we can use the write_file tool to create a script and then rely on the fact that the agent can run Python code? Not sure.\n\nMaybe we can use the read_file tool to read the output of skills_list from somewhere else? For example, maybe there is a cached index? We saw index-cache files. Those are for external skills (anthropics, openai, etc). But we need all skills, including local ones.\n\nAlternatively, we can look at the skills_tool.py file and see how skills_list works, then replicate its logic? That's possible but tedious.\n\nBetter: Let's try to see if we can import and call the function using the python interpreter via the write_file and then maybe we can use the read_file to get the output if we write the script to output to a file and then we can read that file? But we still need to execute the script.\n\nWait, we can use the write_file tool to create a Python script that, when read by the agent, the agent could execute? Not.\n\nMaybe we can use the search_files tool to find a test that calls skills_list and prints output, and then we can copy that output? For example, in tests/tools/test_skills_tool.py there are calls to skills_list(). We could read that test file and see if there is any example output? Not.\n\nLet's examine the test file to see if it prints something.\n\nWe'll read the test file.\n\nBut we need to produce the actual list.\n\nMaybe we can use the agent's own ability to call tools: In the initial system prompt, it says we are Hermes Agent, an intelligent AI assistant created by Nous Research. It says we assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools. It says we have tools. The tools listed are the ones we have. So we cannot call skills_list directly.\n\nThus we must implement the functionality ourselves using file operations.\n\nLet's examine the skills_tool.py to understand how skills_list works, then we can replicate by scanning the skills directory.\n\nWe saw that skills_list reads from SKILLS_DIR = HERMES_HOME / \"skills\". And HERMES_HOME = get_hermes_home().\n\nWe need to know what HERMES_HOME is. Likely it's ~/.hermes or /opt/hermes? Let's check constants.\n\nWe can search for get_hermes_home.\n\nLet's do that.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-9f1f9e58c7846013",
          "call_id": "chatcmpl-tool-9f1f9e58c7846013",
          "response_item_id": "fc_chatcmpl-tool-9f1f9e58c7846013",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"get_hermes_home\", \"target\": \"content\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 50, \"matches\": [{\"path\": \"./agent/auxiliary_client.py\", \"line\": 49, \"content\": \"from hermes_cli.config import get_hermes_home\"}, {\"path\": \"./agent/auxiliary_client.py\", \"line\": 140, \"content\": \"_AUTH_JSON_PATH=*** / \\\"auth.json\\\"\"}, {\"path\": \"./agent/models_dev.py\", \"line\": 181, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/models_dev.py\", \"line\": 182, \"content\": \"    return get_hermes_home() / \\\"models_dev_cache.json\\\"\"}, {\"path\": \"./agent/model_metadata.py\", \"line\": 568, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/model_metadata.py\", \"line\": 569, \"content\": \"    return get_hermes_home() / \\\"context_length_cache.yaml\\\"\"}, {\"path\": \"./agent/context_references.py\", \"line\": 343, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/context_references.py\", \"line\": 345, \"content\": \"    hermes_home = get_hermes_home().resolve()\"}, {\"path\": \"./agent/memory_manager.py\", \"line\": 349, \"content\": \"        ``get_hermes_home()`` themselves.\"}, {\"path\": \"./agent/memory_manager.py\", \"line\": 352, \"content\": \"            from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/memory_manager.py\", \"line\": 353, \"content\": \"            kwargs[\\\"hermes_home\\\"] = str(get_hermes_home())\"}, {\"path\": \"./agent/anthropic_adapter.py\", \"line\": 19, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/anthropic_adapter.py\", \"line\": 617, \"content\": \"_HERMES_OAUTH_FILE=*** / \\\".anthropic_oauth.json\\\"\"}, {\"path\": \"./agent/prompt_builder.py\", \"line\": 15, \"content\": \"from hermes_constants import get_hermes_home, get_skills_dir, is_wsl\"}, {\"path\": \"./agent/prompt_builder.py\", \"line\": 433, \"content\": \"    return get_hermes_home() / \\\".skills_prompt_snapshot.json\\\"\"}, {\"path\": \"./agent/prompt_builder.py\", \"line\": 904, \"content\": \"    soul_path = get_hermes_home() / \\\"SOUL.md\\\"\"}, {\"path\": \"./rl_cli.py\", \"line\": 32, \"content\": \"_hermes_home = get_hermes_home()\"}, {\"path\": \"./rl_cli.py\", \"line\": 63, \"content\": \"from hermes_constants import get_hermes_home, OPENROUTER_BASE_URL\"}, {\"path\": \"./hermes_state.py\", \"line\": 25, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./hermes_state.py\", \"line\": 32, \"content\": \"DEFAULT_DB_PATH = get_hermes_home() / \\\"state.db\\\"\"}, {\"path\": \"./mcp_serve.py\", \"line\": 65, \"content\": \"        from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 66, \"content\": \"        return get_hermes_home() / \\\"sessions\\\"\"}, {\"path\": \"./mcp_serve.py\", \"line\": 101, \"content\": \"        from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 102, \"content\": \"        directory_file = get_hermes_home() / \\\"channel_directory.json\\\"\"}, {\"path\": \"./mcp_serve.py\", \"line\": 346, \"content\": \"            from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 347, \"content\": \"            db_file = get_hermes_home() / \\\"state.db\\\"\"}, {\"path\": \"./acp_adapter/session.py\", \"line\": 11, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./acp_adapter/session.py\", \"line\": 266, \"content\": \"            hermes_home = get_hermes_home()\"}, {\"path\": \"./acp_adapter/entry.py\", \"line\": 20, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./acp_adapter/entry.py\", \"line\": 47, \"content\": \"    hermes_home = get_hermes_home()\"}, {\"path\": \"./website/docs/developer-guide/session-storage.md\", \"line\": 384, \"content\": \"This is derived from `hermes_constants.get_hermes_home()` which resolves to\"}, {\"path\": \"./website/docs/developer-guide/contributing.md\", \"line\": 93, \"content\": \"- **Profile-safe paths**: Never hardcode `~/.hermes` — use `get_hermes_home()` from `hermes_constants` for code paths and `display_hermes_home()` for user-facing messages. See [AGENTS.md](https://github.com/NousResearch/hermes-agent/blob/main/AGENTS.md#profiles-multi-instance-support) for full rules.\"}, {\"path\": \"./website/docs/developer-guide/memory-provider-plugin.md\", \"line\": 176, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./website/docs/developer-guide/memory-provider-plugin.md\", \"line\": 177, \"content\": \"data_dir = get_hermes_home() / \\\"my-provider\\\"\"}, {\"path\": \"./website/docs/developer-guide/prompt-assembly.md\", \"line\": 126, \"content\": \"    soul_path = get_hermes_home() / \\\"SOUL.md\\\"\"}, {\"path\": \"./website/docs/user-guide/profiles.md\", \"line\": 204, \"content\": \"Profiles use the `HERMES_HOME` environment variable. When you run `coder chat`, the wrapper script sets `HERMES_HOME=~/.hermes/profiles/coder` before launching hermes. Since 119+ files in the codebase resolve paths via `get_hermes_home()`, everything automatically scopes to the profile's directory — config, sessions, memory, skills, state database, gateway PID, logs, and cron jobs.\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 75, \"content\": \"        \\\"plugins.memory.hindsight.get_hermes_home\\\", lambda: tmp_path\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 102, \"content\": \"            \\\"plugins.memory.hindsight.get_hermes_home\\\", lambda: tmp_path\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 194, \"content\": \"            \\\"plugins.memory.hindsight.get_hermes_home\\\",\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 576, \"content\": \"            \\\"plugins.memory.hindsight.get_hermes_home\\\",\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 585, \"content\": \"            \\\"plugins.memory.hindsight.get_hermes_home\\\",\"}, {\"path\": \"./tests/plugins/memory/test_hindsight_provider.py\", \"line\": 593, \"content\": \"            \\\"plugins.memory.hindsight.get_hermes_home\\\",\"}, {\"path\": \"./tests/honcho_plugin/test_session.py\", \"line\": 316, \"content\": \"             patch(\\\"hermes_constants.get_hermes_home\\\", return_value=MagicMock()):\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 611, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 621, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 631, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 642, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 677, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 697, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}, {\"path\": \"./tests/hermes_cli/test_api_key_providers.py\", \"line\": 716, \"content\": \"        monkeypatch.setattr(config_module, \\\"get_hermes_home\\\", lambda: hermes_home)\"}]}",
      "tool_call_id": "chatcmpl-tool-9f1f9e58c7846013"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to call the skills_list tool (no arguments) and save the output to /tmp/skills.json.\n\nWe haven't seen a tool called skills_list defined yet. It's likely available via the tools.registry. Let's check the tool definitions.\n\nSearch for \"skills_list\" in the tools registry or look for where tools are registered.\n\nWe can search for \"tools.registry\" or look at the tools directory.\n\nLet's first look at the tools/skills_tool.py we saw earlier, which contains the skills_list function.\n\nWe need to invoke that function. Since we are in a tool-calling environment, we can likely call it via the registry? Actually the assistant can call functions via the tools namespace? The available tools are patch, read_file, search_files, write_file. There's no direct way to call arbitrary Python functions. However, the task says \"Get the list of all skills using the skills_list tool\". That likely means there is a tool named skills_list that we can invoke via the tool calling system? But the provided tools list does not include skills_list. However, the system might have additional tools not listed? The initial description said we have access to a list of tools, and those are the ones we can use. But the task mentions using skills_list tool, which might be one of the tools available in the environment but not listed in the tools array? Possibly the skills_list tool is available as a function we can call via the same interface? Let's check if there is a tool named skills_list in the tool registry by looking at the code.\n\nWe can search for \"skills_list\" in the context of tool definitions.\n\nLet's search for \"skills_list\" in the tools directory or in the registry.\n\nWe'll search for \"skills_list\" in the codebase, maybe in toolsets.py or toolsets.\n\nLet's do a search for \"skills_list\" in the toolsets or in the registry.\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to call the skills_list tool (no arguments) and save the output to /tmp/skills.json.\n\nWe haven't seen a tool called skills_list defined yet. It's likely available via the tools.registry. Let's check the tool definitions.\n\nSearch for \"skills_list\" in the tools registry or look for where tools are registered.\n\nWe can search for \"tools.registry\" or look at the tools directory.\n\nLet's first look at the tools/skills_tool.py we saw earlier, which contains the skills_list function.\n\nWe need to invoke that function. Since we are in a tool-calling environment, we can likely call it via the registry? Actually the assistant can call functions via the tools namespace? The available tools are patch, read_file, search_files, write_file. There's no direct way to call arbitrary Python functions. However, the task says \"Get the list of all skills using the skills_list tool\". That likely means there is a tool named skills_list that we can invoke via the tool calling system? But the provided tools list does not include skills_list. However, the system might have additional tools not listed? The initial description said we have access to a list of tools, and those are the ones we can use. But the task mentions using skills_list tool, which might be one of the tools available in the environment but not listed in the tools array? Possibly the skills_list tool is available as a function we can call via the same interface? Let's check if there is a tool named skills_list in the tool registry by looking at the code.\n\nWe can search for \"skills_list\" in the context of tool definitions.\n\nLet's search for \"skills_list\" in the tools directory or in the registry.\n\nWe'll search for \"skills_list\" in the codebase, maybe in toolsets.py or toolsets.\n\nLet's do a search for \"skills_list\" in the toolsets or in the registry.\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-9f6cbab40a2ed782",
          "call_id": "chatcmpl-tool-9f6cbab40a2ed782",
          "response_item_id": "fc_chatcmpl-tool-9f6cbab40a2ed782",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"skills_list\", \"target\": \"content\", \"path\": \"/opt/hermes/tools\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 13, \"matches\": [{\"path\": \"/opt/hermes/tools/skill_manager_tool.py\", \"line\": 361, \"content\": \"        return {\\\"success\\\": False, \\\"error\\\": f\\\"Skill '{name}' not found. Use skills_list() to see available skills.\\\"}\"}, {\"path\": \"/opt/hermes/tools/file_tools.py\", \"line\": 324, \"content\": \"                        \\\"Use the skills_list or skill_view tools instead.\\\"\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 10, \"content\": \"- Metadata (name ≤64 chars, description ≤1024 chars) - shown in skills_list\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 53, \"content\": \"- skills_list: List skills with metadata (progressive disclosure tier 1)\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 57, \"content\": \"    from tools.skills_tool import skills_list, skill_view, check_skills_requirements\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 60, \"content\": \"    result = skills_list()\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 647, \"content\": \"def skills_list(category: str = None, task_id: str = None) -> str:\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 935, \"content\": \"                    \\\"hint\\\": \\\"Use skills_list to see all available skills\\\",\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 1326, \"content\": \"    result = json.loads(skills_list())\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 1367, \"content\": \"    \\\"name\\\": \\\"skills_list\\\",\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 1389, \"content\": \"                \\\"description\\\": \\\"The skill name (use skills_list to see available skills). For plugin-provided skills, use the qualified form 'plugin:skill' (e.g. 'superpowers:writing-plans').\\\",\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 1401, \"content\": \"    name=\\\"skills_list\\\",\"}, {\"path\": \"/opt/hermes/tools/skills_tool.py\", \"line\": 1404, \"content\": \"    handler=lambda args, **kw: skills_list(\"}]}",
      "tool_call_id": "chatcmpl-tool-9f6cbab40a2ed782"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "I need to look at the actual skills_list function implementation in /opt/hermes/tools/skills_tool.py to understand how it works, then call it and save the output to /tmp/skills.json.\n\nLet me read the skills_list function specifically:\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "I need to look at the actual skills_list function implementation in /opt/hermes/tools/skills_tool.py to understand how it works, then call it and save the output to /tmp/skills.json.\n\nLet me read the skills_list function specifically:\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-956283b1ce06d8e8",
          "call_id": "chatcmpl-tool-956283b1ce06d8e8",
          "response_item_id": "fc_chatcmpl-tool-956283b1ce06d8e8",
          "type": "function",
          "function": {
            "name": "read_file",
            "arguments": "{\"path\": \"/opt/hermes/tools/skills_tool.py\", \"offset\": 640, \"limit\": 50}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"content\": \"   640|    except Exception as e:\\n   641|        logger.warning(\\n   642|            \\\"Error parsing category description %s: %s\\\", desc_file, e, exc_info=True\\n   643|        )\\n   644|        return None\\n   645|\\n   646|\\n   647|def skills_list(category: str = None, task_id: str = None) -> str:\\n   648|    \\\"\\\"\\\"\\n   649|    List all available skills (progressive disclosure tier 1 - minimal metadata).\\n   650|\\n   651|    Returns only name + description to minimize token usage. Use skill_view() to\\n   652|    load full content, tags, related files, etc.\\n   653|\\n   654|    Args:\\n   655|        category: Optional category filter (e.g., \\\"mlops\\\")\\n   656|        task_id: Optional task identifier used to probe the active backend\\n   657|\\n   658|    Returns:\\n   659|        JSON string with minimal skill info: name, description, category\\n   660|    \\\"\\\"\\\"\\n   661|    try:\\n   662|        if not SKILLS_DIR.exists():\\n   663|            SKILLS_DIR.mkdir(parents=True, exist_ok=True)\\n   664|            return json.dumps(\\n   665|                {\\n   666|                    \\\"success\\\": True,\\n   667|                    \\\"skills\\\": [],\\n   668|                    \\\"categories\\\": [],\\n   669|                    \\\"message\\\": f\\\"No skills found. Skills directory created at {display_hermes_home()}/skills/\\\",\\n   670|                },\\n   671|                ensure_ascii=False,\\n   672|            )\\n   673|\\n   674|        # Find all skills\\n   675|        all_skills = _find_all_skills()\\n   676|\\n   677|        if not all_skills:\\n   678|            return json.dumps(\\n   679|                {\\n   680|                    \\\"success\\\": True,\\n   681|                    \\\"skills\\\": [],\\n   682|                    \\\"categories\\\": [],\\n   683|                    \\\"message\\\": \\\"No skills found in skills/ directory.\\\",\\n   684|                },\\n   685|                ensure_ascii=False,\\n   686|            )\\n   687|\\n   688|        # Filter by category if specified\\n   689|        if category:\\n   690|\", \"total_lines\": 1419, \"file_size\": 51465, \"truncated\": true, \"hint\": \"Use offset=690 to continue reading (showing 640-689 of 1419 lines)\", \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "chatcmpl-tool-956283b1ce06d8e8"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to get the list of all skills using the skills_list tool. According to the context, we can call skills_list with no arguments. The tool is available in the tools.skills_tool module. However, we are in a subagent environment and we can directly call the function via the tool system? Actually, the instructions say we have access to tools like patch, read_file, etc. There's no explicit \"skills_list\" tool in the provided tool list. But the task says \"Get the list of all skills using the skills_list tool\". Possibly we need to invoke the skills_list function via the agent's tool system? However, we only have the listed tools (patch, read_file, search_files, write_file). There is no generic \"call_function\" tool. But maybe we can call skills_list by using the agent's own tool system? Wait, the subagent is working within the Hermes Agent environment, and we have access to the tools defined in the initial system message. Those are the only tools we can use. However, the task description says we need to call the skills_list tool (no arguments). That suggests that skills_list is one of the tools available to us. But looking at the tool list, we don't see it. Perhaps the tools list is incomplete? Actually, the initial system message says: \"You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools.\" Then it lists the tools we have access to. Those are the tools we can use. However, the task says we need to use the skills_list tool. Possibly the skills_list tool is available via the agent's internal tool set, but not exposed in the subagent tool list? Alternatively, maybe we can call it by using the \"patch\" or \"read_file\" on some internal representation? That seems unlikely.\n\nWait, maybe the subagent can invoke tools by name using a special mechanism? The provided tools are for file operations. However, the task might be expecting us to use the skills_list tool that is part of the Hermes Agent's toolset, and we as the subagent can call it because we are operating within the same agent? Actually, the subagent is a focused subagent working on a specific delegated task. The subagent likely has access to the same tools as the main agent? The initial message says: \"You are a focused subagent working on a specific delegated task.\" and then lists the tools we have access to. It lists only those four tools. So we cannot directly call skills_list unless we can simulate it via reading files.\n\nBut we can read the skills directory and construct the list ourselves. The skills_list tool essentially returns a JSON string with skill metadata. We can replicate that by scanning the skills directory and reading SKILL.md files.\n\nWe have the path to the skills directory: from earlier, we saw HERMES_HOME = get_hermes_home() and SKILLS_DIR = HERMES_HOME / \"skills\". We need to find HERMES_HOME. We saw in the skills_tool.py that HERMES_HOME is obtained from get_hermes_home(). We can try to find where get_hermes_home is defined. We saw references to hermes_constants. Let's look for that file.\n\nSearch for hermes_constants.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to get the list of all skills using the skills_list tool. According to the context, we can call skills_list with no arguments. The tool is available in the tools.skills_tool module. However, we are in a subagent environment and we can directly call the function via the tool system? Actually, the instructions say we have access to tools like patch, read_file, etc. There's no explicit \"skills_list\" tool in the provided tool list. But the task says \"Get the list of all skills using the skills_list tool\". Possibly we need to invoke the skills_list function via the agent's tool system? However, we only have the listed tools (patch, read_file, search_files, write_file). There is no generic \"call_function\" tool. But maybe we can call skills_list by using the agent's own tool system? Wait, the subagent is working within the Hermes Agent environment, and we have access to the tools defined in the initial system message. Those are the only tools we can use. However, the task description says we need to call the skills_list tool (no arguments). That suggests that skills_list is one of the tools available to us. But looking at the tool list, we don't see it. Perhaps the tools list is incomplete? Actually, the initial system message says: \"You are Hermes Agent, an intelligent AI assistant created by Nous Research. You are helpful, knowledgeable, and direct. You assist users with a wide range of tasks including answering questions, writing and editing code, analyzing information, creative work, and executing actions via your tools.\" Then it lists the tools we have access to. Those are the tools we can use. However, the task says we need to use the skills_list tool. Possibly the skills_list tool is available via the agent's internal tool set, but not exposed in the subagent tool list? Alternatively, maybe we can call it by using the \"patch\" or \"read_file\" on some internal representation? That seems unlikely.\n\nWait, maybe the subagent can invoke tools by name using a special mechanism? The provided tools are for file operations. However, the task might be expecting us to use the skills_list tool that is part of the Hermes Agent's toolset, and we as the subagent can call it because we are operating within the same agent? Actually, the subagent is a focused subagent working on a specific delegated task. The subagent likely has access to the same tools as the main agent? The initial message says: \"You are a focused subagent working on a specific delegated task.\" and then lists the tools we have access to. It lists only those four tools. So we cannot directly call skills_list unless we can simulate it via reading files.\n\nBut we can read the skills directory and construct the list ourselves. The skills_list tool essentially returns a JSON string with skill metadata. We can replicate that by scanning the skills directory and reading SKILL.md files.\n\nWe have the path to the skills directory: from earlier, we saw HERMES_HOME = get_hermes_home() and SKILLS_DIR = HERMES_HOME / \"skills\". We need to find HERMES_HOME. We saw in the skills_tool.py that HERMES_HOME is obtained from get_hermes_home(). We can try to find where get_hermes_home is defined. We saw references to hermes_constants. Let's look for that file.\n\nSearch for hermes_constants.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-9f6c88d42a1b6f68",
          "call_id": "chatcmpl-tool-9f6c88d42a1b6f68",
          "response_item_id": "fc_chatcmpl-tool-9f6c88d42a1b6f68",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"pattern\": \"hermes_constants\", \"target\": \"content\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 50, \"matches\": [{\"path\": \"./agent/auxiliary_client.py\", \"line\": 50, \"content\": \"from hermes_constants import OPENROUTER_BASE_URL\"}, {\"path\": \"./agent/models_dev.py\", \"line\": 181, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/model_metadata.py\", \"line\": 17, \"content\": \"from hermes_constants import OPENROUTER_MODELS_URL\"}, {\"path\": \"./agent/model_metadata.py\", \"line\": 568, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/context_references.py\", \"line\": 343, \"content\": \"    from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/credential_pool.py\", \"line\": 16, \"content\": \"from hermes_constants import OPENROUTER_BASE_URL\"}, {\"path\": \"./agent/skill_utils.py\", \"line\": 15, \"content\": \"from hermes_constants import get_config_path, get_skills_dir\"}, {\"path\": \"./agent/memory_manager.py\", \"line\": 352, \"content\": \"            from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/anthropic_adapter.py\", \"line\": 19, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./agent/skill_commands.py\", \"line\": 15, \"content\": \"from hermes_constants import display_hermes_home\"}, {\"path\": \"./agent/prompt_builder.py\", \"line\": 15, \"content\": \"from hermes_constants import get_hermes_home, get_skills_dir, is_wsl\"}, {\"path\": \"./rl_cli.py\", \"line\": 63, \"content\": \"from hermes_constants import get_hermes_home, OPENROUTER_BASE_URL\"}, {\"path\": \"./hermes_state.py\", \"line\": 25, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 65, \"content\": \"        from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 101, \"content\": \"        from hermes_constants import get_hermes_home\"}, {\"path\": \"./mcp_serve.py\", \"line\": 346, \"content\": \"            from hermes_constants import get_hermes_home\"}, {\"path\": \"./hermes_time.py\", \"line\": 19, \"content\": \"from hermes_constants import get_config_path\"}, {\"path\": \"./acp_adapter/session.py\", \"line\": 11, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./acp_adapter/entry.py\", \"line\": 20, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./website/docs/developer-guide/session-storage.md\", \"line\": 384, \"content\": \"This is derived from `hermes_constants.get_hermes_home()` which resolves to\"}, {\"path\": \"./website/docs/developer-guide/contributing.md\", \"line\": 93, \"content\": \"- **Profile-safe paths**: Never hardcode `~/.hermes` — use `get_hermes_home()` from `hermes_constants` for code paths and `display_hermes_home()` for user-facing messages. See [AGENTS.md](https://github.com/NousResearch/hermes-agent/blob/main/AGENTS.md#profiles-multi-instance-support) for full rules.\"}, {\"path\": \"./website/docs/developer-guide/memory-provider-plugin.md\", \"line\": 176, \"content\": \"from hermes_constants import get_hermes_home\"}, {\"path\": \"./website/docs/developer-guide/architecture.md\", \"line\": 60, \"content\": \"├── hermes_constants.py       # HERMES_HOME, profile-aware paths\"}, {\"path\": \"./tests/honcho_plugin/test_session.py\", \"line\": 316, \"content\": \"             patch(\\\"hermes_constants.get_hermes_home\\\", return_value=MagicMock()):\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 12, \"content\": \"import hermes_constants\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 16, \"content\": \"# is_wsl() in hermes_constants\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 24, \"content\": \"        hermes_constants._wsl_detected = None\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 32, \"content\": \"            assert hermes_constants.is_wsl() is True\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 40, \"content\": \"            assert hermes_constants.is_wsl() is True\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 48, \"content\": \"            assert hermes_constants.is_wsl() is False\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 52, \"content\": \"            assert hermes_constants.is_wsl() is False\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 56, \"content\": \"        hermes_constants._wsl_detected = True\"}, {\"path\": \"./tests/hermes_cli/test_gateway_wsl.py\", \"line\": 59, \"content\": \"            assert hermes_constants.is_wsl() is True\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 45, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 57, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=True):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 70, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 80, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 90, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 107, \"content\": \"        with patch(\\\"hermes_constants.is_container\\\", return_value=False), \\\\\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 129, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False):\"}, {\"path\": \"./tests/hermes_cli/test_container_aware_cli.py\", \"line\": 140, \"content\": \"    with patch(\\\"hermes_constants.is_container\\\", return_value=False), \\\\\"}, {\"path\": \"./tests/hermes_cli/test_setup.py\", \"line\": 215, \"content\": \"    import hermes_constants\"}, {\"path\": \"./tests/hermes_cli/test_setup.py\", \"line\": 216, \"content\": \"    monkeypatch.setattr(hermes_constants, \\\"is_container\\\", lambda: True)\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 22, \"content\": \"    \\\"\\\"\\\"Unit tests for hermes_constants.get_subprocess_home().\\\"\\\"\\\"\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 26, \"content\": \"        from hermes_constants import get_subprocess_home\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 34, \"content\": \"        from hermes_constants import get_subprocess_home\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 43, \"content\": \"        from hermes_constants import get_subprocess_home\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 53, \"content\": \"        from hermes_constants import get_subprocess_home\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 63, \"content\": \"        from hermes_constants import get_subprocess_home\"}, {\"path\": \"./tests/test_subprocess_home_isolation.py\", \"line\": 192, \"content\": \"        from hermes_constants import get_subprocess_home\"}]}",
      "tool_call_id": "chatcmpl-tool-9f6c88d42a1b6f68"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to find the hermes_constants module to see get_hermes_home definition.\nLet's read hermes_constants.py.\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to find the hermes_constants module to see get_hermes_home definition.\nLet's read hermes_constants.py.\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-bd40786a53a6b024",
          "call_id": "chatcmpl-tool-bd40786a53a6b024",
          "response_item_id": "fc_chatcmpl-tool-bd40786a53a6b024",
          "type": "function",
          "function": {
            "name": "search_files",
            "arguments": "{\"path\": \"/opt/hermes\", \"pattern\": \"hermes_constants.py\", \"target\": \"files\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"total_count\": 2, \"files\": [\"/opt/hermes/hermes_constants.py\", \"/opt/hermes/tests/test_hermes_constants.py\"]}",
      "tool_call_id": "chatcmpl-tool-bd40786a53a6b024"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to get the list of all skills using the skills_list tool and save it to /tmp/skills.json.\n\nFirst, we need to check if there's a tool named `skills_list` that we can call. Since we are in a subagent environment, we likely have access to the same tools as the main agent, including the ability to call tools via the `tools` namespace? However, the available tools we have are patch, read_file, search_files, write_file. There's no direct tool calling mechanism shown. But the task says: \"Get the list of all skills using the skills_list tool\". This implies that there is a tool called `skills_list` that we can invoke. However, in the list of tools we have, there is no such tool. Wait, maybe we are supposed to use the `skills_tool.py` module and call its `skills_list` function? But we don't have a tool to execute Python code.\n\nAlternatively, maybe the `skills_list` tool is available as one of the tools we can call? The initial system message says we have access to a set of tools (patch, read_file, search_files, write_file). It doesn't list `skills_list`. However, the context says: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\"\n\nPerhaps we are meant to use the `agent`'s tool calling capability? But as a subagent, we might not have that. However, the task is to use the `skills_list` tool, which is likely a tool provided by the environment. Maybe we need to look for a tool definition? Let's check if there's a tool called `skills_list` in the tools directory? We saw references to `skills_tool.py` which contains a function `skills_list`. But we cannot directly call Python functions.\n\nWait, maybe the subagent can invoke tools by name? The initial system message didn't list any tools besides the four we have. But perhaps the tools are available via a different mechanism? Let's re-read the initial system message: \"You have access to the following functions:\" and then lists patch, read_file, search_files, write_file. So only those four.\n\nThus, we cannot directly call `skills_list` as a tool. However, we can read the output of `skills_list` by perhaps looking at where it's stored? Or maybe we can run a command? But we don't have a shell tool.\n\nAlternatively, maybe we can use the `skills_tool.py` module to compute the list ourselves by reading the skills directory and parsing SKILL.md files? That seems complex.\n\nBut note: the task says \"Get the list of all skills using the skills_list tool\". This might be a misunderstanding: perhaps we are supposed to use the `skills_list` tool that is available in the environment, and we as the subagent can invoke it? However, the tool list we were given does not include it. Could it be that the tool list is incomplete? Or maybe we are supposed to use the `agent`'s tool calling via some other means? Let's check if there is a way to call tools via the `agent` namespace? Not provided.\n\nWait, maybe the subagent can use the same tool calling mechanism as the main agent? The main agent likely has a way to call tools. But as a subagent, we might be able to call tools by using the `agent` tool? Actually, there is no such tool described.\n\nLet's think differently: maybe the `skills_list` tool is just a command that we can run via the CLI? For example, there is a hermes CLI. We saw references to `hermes_cli`. We could potentially run `hermes skills list` or something. But we don't have a shell tool.\n\nHowever, we do have the ability to read files. Perhaps the output of `skills_list` is already cached somewhere? We saw index-cache files. Those are likely the list of skills from external sources (like Anthropic's skills). But we need all skills, including local ones.\n\nMaybe we can look at the skills directory and generate the list ourselves by mimicking what `skills_list` does. That might be easier: we can read the skills directory, find all SKILL.md files, parse the frontmatter to get name and description, and output JSON.\n\nBut we need to be careful about the exact format that `skills_list` returns. From the code snippet we saw in skills_tool.py, the function returns a JSON string with keys: success, skills (list of objects with name, description, category), categories (list of category names), and maybe a message.\n\nLet's look at the full function. We saw lines 647-... but we didn't see the whole function. Let's read more of the skills_tool.py file around that area.\n\nWe'll read from offset 647 to maybe 750.\n\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to get the list of all skills using the skills_list tool and save it to /tmp/skills.json.\n\nFirst, we need to check if there's a tool named `skills_list` that we can call. Since we are in a subagent environment, we likely have access to the same tools as the main agent, including the ability to call tools via the `tools` namespace? However, the available tools we have are patch, read_file, search_files, write_file. There's no direct tool calling mechanism shown. But the task says: \"Get the list of all skills using the skills_list tool\". This implies that there is a tool called `skills_list` that we can invoke. However, in the list of tools we have, there is no such tool. Wait, maybe we are supposed to use the `skills_tool.py` module and call its `skills_list` function? But we don't have a tool to execute Python code.\n\nAlternatively, maybe the `skills_list` tool is available as one of the tools we can call? The initial system message says we have access to a set of tools (patch, read_file, search_files, write_file). It doesn't list `skills_list`. However, the context says: \"We need to call the skills_list tool (no arguments) and write the JSON output to /tmp/skills.json.\"\n\nPerhaps we are meant to use the `agent`'s tool calling capability? But as a subagent, we might not have that. However, the task is to use the `skills_list` tool, which is likely a tool provided by the environment. Maybe we need to look for a tool definition? Let's check if there's a tool called `skills_list` in the tools directory? We saw references to `skills_tool.py` which contains a function `skills_list`. But we cannot directly call Python functions.\n\nWait, maybe the subagent can invoke tools by name? The initial system message didn't list any tools besides the four we have. But perhaps the tools are available via a different mechanism? Let's re-read the initial system message: \"You have access to the following functions:\" and then lists patch, read_file, search_files, write_file. So only those four.\n\nThus, we cannot directly call `skills_list` as a tool. However, we can read the output of `skills_list` by perhaps looking at where it's stored? Or maybe we can run a command? But we don't have a shell tool.\n\nAlternatively, maybe we can use the `skills_tool.py` module to compute the list ourselves by reading the skills directory and parsing SKILL.md files? That seems complex.\n\nBut note: the task says \"Get the list of all skills using the skills_list tool\". This might be a misunderstanding: perhaps we are supposed to use the `skills_list` tool that is available in the environment, and we as the subagent can invoke it? However, the tool list we were given does not include it. Could it be that the tool list is incomplete? Or maybe we are supposed to use the `agent`'s tool calling via some other means? Let's check if there is a way to call tools via the `agent` namespace? Not provided.\n\nWait, maybe the subagent can use the same tool calling mechanism as the main agent? The main agent likely has a way to call tools. But as a subagent, we might be able to call tools by using the `agent` tool? Actually, there is no such tool described.\n\nLet's think differently: maybe the `skills_list` tool is just a command that we can run via the CLI? For example, there is a hermes CLI. We saw references to `hermes_cli`. We could potentially run `hermes skills list` or something. But we don't have a shell tool.\n\nHowever, we do have the ability to read files. Perhaps the output of `skills_list` is already cached somewhere? We saw index-cache files. Those are likely the list of skills from external sources (like Anthropic's skills). But we need all skills, including local ones.\n\nMaybe we can look at the skills directory and generate the list ourselves by mimicking what `skills_list` does. That might be easier: we can read the skills directory, find all SKILL.md files, parse the frontmatter to get name and description, and output JSON.\n\nBut we need to be careful about the exact format that `skills_list` returns. From the code snippet we saw in skills_tool.py, the function returns a JSON string with keys: success, skills (list of objects with name, description, category), categories (list of category names), and maybe a message.\n\nLet's look at the full function. We saw lines 647-... but we didn't see the whole function. Let's read more of the skills_tool.py file around that area.\n\nWe'll read from offset 647 to maybe 750.\n\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-a579aad434c7dbf8",
          "call_id": "chatcmpl-tool-a579aad434c7dbf8",
          "response_item_id": "fc_chatcmpl-tool-a579aad434c7dbf8",
          "type": "function",
          "function": {
            "name": "read_file",
            "arguments": "{\"path\": \"/opt/hermes/tools/skills_tool.py\", \"offset\": 647, \"limit\": 100}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"content\": \"   647|def skills_list(category: str = None, task_id: str = None) -> str:\\n   648|    \\\"\\\"\\\"\\n   649|    List all available skills (progressive disclosure tier 1 - minimal metadata).\\n   650|\\n   651|    Returns only name + description to minimize token usage. Use skill_view() to\\n   652|    load full content, tags, related files, etc.\\n   653|\\n   654|    Args:\\n   655|        category: Optional category filter (e.g., \\\"mlops\\\")\\n   656|        task_id: Optional task identifier used to probe the active backend\\n   657|\\n   658|    Returns:\\n   659|        JSON string with minimal skill info: name, description, category\\n   660|    \\\"\\\"\\\"\\n   661|    try:\\n   662|        if not SKILLS_DIR.exists():\\n   663|            SKILLS_DIR.mkdir(parents=True, exist_ok=True)\\n   664|            return json.dumps(\\n   665|                {\\n   666|                    \\\"success\\\": True,\\n   667|                    \\\"skills\\\": [],\\n   668|                    \\\"categories\\\": [],\\n   669|                    \\\"message\\\": f\\\"No skills found. Skills directory created at {display_hermes_home()}/skills/\\\",\\n   670|                },\\n   671|                ensure_ascii=False,\\n   672|            )\\n   673|\\n   674|        # Find all skills\\n   675|        all_skills = _find_all_skills()\\n   676|\\n   677|        if not all_skills:\\n   678|            return json.dumps(\\n   679|                {\\n   680|                    \\\"success\\\": True,\\n   681|                    \\\"skills\\\": [],\\n   682|                    \\\"categories\\\": [],\\n   683|                    \\\"message\\\": \\\"No skills found in skills/ directory.\\\",\\n   684|                },\\n   685|                ensure_ascii=False,\\n   686|            )\\n   687|\\n   688|        # Filter by category if specified\\n   689|        if category:\\n   690|            all_skills = [s for s in all_skills if s.get(\\\"category\\\") == category]\\n   691|\\n   692|        # Sort by category then name\\n   693|        all_skills.sort(key=lambda s: (s.get(\\\"category\\\") or \\\"\\\", s[\\\"name\\\"]))\\n   694|\\n   695|        # Extract unique categories\\n   696|        categories = sorted(\\n   697|            set(s.get(\\\"category\\\") for s in all_skills if s.get(\\\"category\\\"))\\n   698|        )\\n   699|\\n   700|        return json.dumps(\\n   701|            {\\n   702|                \\\"success\\\": True,\\n   703|                \\\"skills\\\": all_skills,\\n   704|                \\\"categories\\\": categories,\\n   705|                \\\"count\\\": len(all_skills),\\n   706|                \\\"hint\\\": \\\"Use skill_view(name) to see full content, tags, and linked files\\\",\\n   707|            },\\n   708|            ensure_ascii=False,\\n   709|        )\\n   710|\\n   711|    except Exception as e:\\n   712|        return tool_error(str(e), success=False)\\n   713|\\n   714|\\n   715|# ── Plugin skill serving ──────────────────────────────────────────────────\\n   716|\\n   717|\\n   718|def _serve_plugin_skill(\\n   719|    skill_md: Path,\\n   720|    namespace: str,\\n   721|    bare: str,\\n   722|) -> str:\\n   723|    \\\"\\\"\\\"Read a plugin-provided skill, apply guards, return JSON.\\\"\\\"\\\"\\n   724|    from hermes_cli.plugins import _get_disabled_plugins, get_plugin_manager\\n   725|\\n   726|    if namespace in _get_disabled_plugins():\\n   727|        return json.dumps(\\n   728|            {\\n   729|                \\\"success\\\": False,\\n   730|                \\\"error\\\": (\\n   731|                    f\\\"Plugin '{namespace}' is disabled. \\\"\\n   732|                    f\\\"Re-enable with: hermes plugins enable {namespace}\\\"\\n   733|                ),\\n   734|            },\\n   735|            ensure_ascii=False,\\n   736|        )\\n   737|\\n   738|    try:\\n   739|        content = skill_md.read_text(encoding=\\\"utf-8\\\")\\n   740|    except Exception as e:\\n   741|        return json.dumps(\\n   742|            {\\\"success\\\": False, \\\"error\\\": f\\\"Failed to read skill '{namespace}:{bare}': {e}\\\"},\\n   743|            ensure_ascii=False,\\n   744|        )\\n   745|\\n   746|    parsed_frontmatter: Dict[str, Any] = {}\\n   747|\", \"total_lines\": 1419, \"file_size\": 51465, \"truncated\": true, \"hint\": \"Use offset=747 to continue reading (showing 647-746 of 1419 lines)\", \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "chatcmpl-tool-a579aad434c7dbf8"
    },
    {
      "role": "assistant",
      "content": "",
      "reasoning": "We need to call the skills_list tool and save its output to /tmp/skills.json.\n Since we cannot directly call the tool as a function, we need to see if there's a way to invoke it.\n However, note that the tools are available as functions in the agent's environment. We are the agent and we can call the skills_list function directly.\n\n Let's check if we can call skills_list() from the tools.skills_tool module.\n\n We are in a Python environment? We can try to import and call it.\n\n But note: the agent is running in a specific environment and we have access to the tools via the tool system.\n\n However, the task says: \"Get the list of all skills using the skills_list tool\"\n\n We are allowed to use the tools available to us. We have a tool called `skills_list`? Actually, we have the function `skills_list` in the tools.\n\n We can try to call it by using the agent's tool invocation mechanism? But we are the agent and we can run Python code.\n\n Let's try to import the skills_tool and call skills_list.\n\n But note: we are in a chat and we don't have a direct Python REPL. However, we can use the agent's ability to run code? Not exactly.\n\n Alternatively, we can look for a CLI command. There is a hermes CLI that has a skills list command.\n\n We saw in the search results: \n   \"/opt/hermes/hermes_cli/main.py\", line 5237: \"    skills_list = skills_subparsers.add_parser(\\\"list\\\", help=\\\"List installed skills\\\")\"\n\n So we can run: `hermes skills list` (or maybe `hermes skills list --source all`).\n\n However, we are in the agent's environment and we can run shell commands? We don't have a tool for running shell commands.\n\n We have the tools: patch, read_file, write_file, search_files. We don't have a tool for executing arbitrary shell commands.\n\n But we can write a Python script and then try to run it? We don't have a tool to run Python code.\n\n Alternatively, we can use the agent's internal tool system by invoking the tool via the agent's tool call mechanism? \n However, we are the agent and we are allowed to use the tools that are provided to us. The skills_list tool is one of the tools we have.\n\n Let me check: in the tools we have, we don't see a tool named \"skills_list\". We see the function in the skills_tool.py.\n\n But note: the agent's tool system exposes functions as tools. We can call a tool by name? We are not sure.\n\n Given the constraints, let's try to read the skills directory and construct the list ourselves? \n But the task says to use the skills_list tool.\n\n We can try to import the module and call the function in a Python script and then write the output.\n\n Steps:\n 1. Write a small Python script that imports the skills_list function from tools.skills_tool and calls it.\n 2. Run the script and capture the output.\n 3. Write the output to /tmp/skills.json.\n\n However, we don't have a tool to run Python code. But we can use the agent's ability to run code? \n We are the agent and we can run Python code in our own reasoning? Actually, we are a language model and we cannot run arbitrary code.\n\n We must use the available tools. We have the ability to read and write files, and to search.\n\n We can try to see if the skills_list function is available as a tool in the agent's toolset. \n Let's look at the tool definitions in the agent. We don't have a tool for listing tools.\n\n Alternatively, we can look at the agent's tool registry. We don't have a tool for that.\n\n Given the difficulty, let's try to see if we can call the skills_list function by using the agent's tool call mechanism in the chat? \n We are not sure how to do that.\n\n Let's re-examine the task: we are a subagent working on a specific delegated task. We have access to the tools: patch, read_file, write_file, search_files.\n\n We don't have a tool to run the skills_list function.\n\n However, note that the skills_list function is defined in the tools/skills_tool.py file and we can read it. \n We can try to understand what it does and then replicate its logic by reading the skills directory.\n\n But the task says to use the skills_list tool. We must use the tool.\n\n Let's check if there is a way to invoke a tool by name. We don't see a tool for that.\n\n Alternatively, we can use the agent's tool call system by sending a message that invokes the tool? \n We are the agent and we are in a chat with the user. We can invoke tools by using the tool call format? \n We are not sure.\n\n Given the time, let's try to write a Python script that uses the same logic as the skills_list function and then run it by writing to a file and then using the Python interpreter? \n We don't have a tool to run the Python interpreter.\n\n We are stuck.\n\n Let's think: we are the agent and we are running in an environment that has the Hermes agent installed. \n We can try to run the hermes CLI command by using the write_file tool to write a script and then somehow run it? \n We don't have a way to run the script.\n\n Wait, we have the ability to read and write files, but not to execute.\n\n However, note that the agent might have a tool for running shell commands? We don't see it in the list.\n\n Let's search for a tool that runs commands.\n\n We can use the search_files tool to look for \"run\" or \"execute\" in the tools directory.\n\n But we are limited to the tools we have.\n\n Alternatively, we can use the agent's own capability to run the skills_list function by importing it in our own code? \n We are the agent and we are running in a Python environment? We don't know.\n\n Let's assume that we can run Python code because we are the agent and we are implemented in Python. \n We can try to do:\n\n   from tools.skills_tool import skills_list\n   result = skills_list()\n\n and then write the result to a file.\n\n But we don't know if we can do that in this chat.\n\n We are going to try to do that by writing a note to ourselves and then acting on it? \n We are not sure.\n\n Let's try to see if we can get the agent to run a tool by name. We don't have a tool for that.\n\n Given the constraints, I think we have to replicate the function by reading the skills directory.\n\n Let's do that:\n\n Steps:\n 1. Find the skills directory. From the skills_tool.py, we see:\n        HERMES_HOME = get_hermes_home()\n        SKILLS_DIR = HERMES_HOME / \"skills\"\n\n    We need to get HERMES_HOME. We can read the hermes_constants.py to see how get_hermes_home works.\n\n    Alternatively, we can use the environment variable HERMES_HOME? Or we can use the function.\n\n    We can try to read the hermes_constants.py and see if we can replicate the get_hermes_home function.\n\n    But note: we are allowed to use the tools, and we can read the hermes_constants.py and then use the information to compute the path.\n\n    However, we are running in the same environment as the agent, so the environment variable HERMES_HOME should be set.\n\n    Let's check the environment by reading the environment? We don't have a tool for that.\n\n    We can try to read the file that contains the environment? Not sure.\n\n    Alternatively, we can look at the hermes_constants.py and see that it uses the HERMES_HOME environment variable.\n\n    Let's read hermes_constants.py.\n\n 2. Once we have the skills directory, we can list all the subdirectories that contain a SKILL.md file.\n\n 3. For each skill, we read the SKILL.md, parse the frontmatter to get the name and description.\n\n 4. Then we build the list of skills in the same format as the skills_list function.\n\n 5. Write the JSON string to /tmp/skills.json.\n\n This is a lot of work, but we can do it step by step.\n\n Let's start by reading the hermes_constants.py to understand get_hermes_home.\n\n We'll use the read_file tool.\n",
      "finish_reason": "tool_calls",
      "reasoning_content": "We need to call the skills_list tool and save its output to /tmp/skills.json.\n Since we cannot directly call the tool as a function, we need to see if there's a way to invoke it.\n However, note that the tools are available as functions in the agent's environment. We are the agent and we can call the skills_list function directly.\n\n Let's check if we can call skills_list() from the tools.skills_tool module.\n\n We are in a Python environment? We can try to import and call it.\n\n But note: the agent is running in a specific environment and we have access to the tools via the tool system.\n\n However, the task says: \"Get the list of all skills using the skills_list tool\"\n\n We are allowed to use the tools available to us. We have a tool called `skills_list`? Actually, we have the function `skills_list` in the tools.\n\n We can try to call it by using the agent's tool invocation mechanism? But we are the agent and we can run Python code.\n\n Let's try to import the skills_tool and call skills_list.\n\n But note: we are in a chat and we don't have a direct Python REPL. However, we can use the agent's ability to run code? Not exactly.\n\n Alternatively, we can look for a CLI command. There is a hermes CLI that has a skills list command.\n\n We saw in the search results: \n   \"/opt/hermes/hermes_cli/main.py\", line 5237: \"    skills_list = skills_subparsers.add_parser(\\\"list\\\", help=\\\"List installed skills\\\")\"\n\n So we can run: `hermes skills list` (or maybe `hermes skills list --source all`).\n\n However, we are in the agent's environment and we can run shell commands? We don't have a tool for running shell commands.\n\n We have the tools: patch, read_file, write_file, search_files. We don't have a tool for executing arbitrary shell commands.\n\n But we can write a Python script and then try to run it? We don't have a tool to run Python code.\n\n Alternatively, we can use the agent's internal tool system by invoking the tool via the agent's tool call mechanism? \n However, we are the agent and we are allowed to use the tools that are provided to us. The skills_list tool is one of the tools we have.\n\n Let me check: in the tools we have, we don't see a tool named \"skills_list\". We see the function in the skills_tool.py.\n\n But note: the agent's tool system exposes functions as tools. We can call a tool by name? We are not sure.\n\n Given the constraints, let's try to read the skills directory and construct the list ourselves? \n But the task says to use the skills_list tool.\n\n We can try to import the module and call the function in a Python script and then write the output.\n\n Steps:\n 1. Write a small Python script that imports the skills_list function from tools.skills_tool and calls it.\n 2. Run the script and capture the output.\n 3. Write the output to /tmp/skills.json.\n\n However, we don't have a tool to run Python code. But we can use the agent's ability to run code? \n We are the agent and we can run Python code in our own reasoning? Actually, we are a language model and we cannot run arbitrary code.\n\n We must use the available tools. We have the ability to read and write files, and to search.\n\n We can try to see if the skills_list function is available as a tool in the agent's toolset. \n Let's look at the tool definitions in the agent. We don't have a tool for listing tools.\n\n Alternatively, we can look at the agent's tool registry. We don't have a tool for that.\n\n Given the difficulty, let's try to see if we can call the skills_list function by using the agent's tool call mechanism in the chat? \n We are not sure how to do that.\n\n Let's re-examine the task: we are a subagent working on a specific delegated task. We have access to the tools: patch, read_file, write_file, search_files.\n\n We don't have a tool to run the skills_list function.\n\n However, note that the skills_list function is defined in the tools/skills_tool.py file and we can read it. \n We can try to understand what it does and then replicate its logic by reading the skills directory.\n\n But the task says to use the skills_list tool. We must use the tool.\n\n Let's check if there is a way to invoke a tool by name. We don't see a tool for that.\n\n Alternatively, we can use the agent's tool call system by sending a message that invokes the tool? \n We are the agent and we are in a chat with the user. We can invoke tools by using the tool call format? \n We are not sure.\n\n Given the time, let's try to write a Python script that uses the same logic as the skills_list function and then run it by writing to a file and then using the Python interpreter? \n We don't have a tool to run the Python interpreter.\n\n We are stuck.\n\n Let's think: we are the agent and we are running in an environment that has the Hermes agent installed. \n We can try to run the hermes CLI command by using the write_file tool to write a script and then somehow run it? \n We don't have a way to run the script.\n\n Wait, we have the ability to read and write files, but not to execute.\n\n However, note that the agent might have a tool for running shell commands? We don't see it in the list.\n\n Let's search for a tool that runs commands.\n\n We can use the search_files tool to look for \"run\" or \"execute\" in the tools directory.\n\n But we are limited to the tools we have.\n\n Alternatively, we can use the agent's own capability to run the skills_list function by importing it in our own code? \n We are the agent and we are running in a Python environment? We don't know.\n\n Let's assume that we can run Python code because we are the agent and we are implemented in Python. \n We can try to do:\n\n   from tools.skills_tool import skills_list\n   result = skills_list()\n\n and then write the result to a file.\n\n But we don't know if we can do that in this chat.\n\n We are going to try to do that by writing a note to ourselves and then acting on it? \n We are not sure.\n\n Let's try to see if we can get the agent to run a tool by name. We don't have a tool for that.\n\n Given the constraints, I think we have to replicate the function by reading the skills directory.\n\n Let's do that:\n\n Steps:\n 1. Find the skills directory. From the skills_tool.py, we see:\n        HERMES_HOME = get_hermes_home()\n        SKILLS_DIR = HERMES_HOME / \"skills\"\n\n    We need to get HERMES_HOME. We can read the hermes_constants.py to see how get_hermes_home works.\n\n    Alternatively, we can use the environment variable HERMES_HOME? Or we can use the function.\n\n    We can try to read the hermes_constants.py and see if we can replicate the get_hermes_home function.\n\n    But note: we are allowed to use the tools, and we can read the hermes_constants.py and then use the information to compute the path.\n\n    However, we are running in the same environment as the agent, so the environment variable HERMES_HOME should be set.\n\n    Let's check the environment by reading the environment? We don't have a tool for that.\n\n    We can try to read the file that contains the environment? Not sure.\n\n    Alternatively, we can look at the hermes_constants.py and see that it uses the HERMES_HOME environment variable.\n\n    Let's read hermes_constants.py.\n\n 2. Once we have the skills directory, we can list all the subdirectories that contain a SKILL.md file.\n\n 3. For each skill, we read the SKILL.md, parse the frontmatter to get the name and description.\n\n 4. Then we build the list of skills in the same format as the skills_list function.\n\n 5. Write the JSON string to /tmp/skills.json.\n\n This is a lot of work, but we can do it step by step.\n\n Let's start by reading the hermes_constants.py to understand get_hermes_home.\n\n We'll use the read_file tool.\n",
      "tool_calls": [
        {
          "id": "chatcmpl-tool-82b76914102f45be",
          "call_id": "chatcmpl-tool-82b76914102f45be",
          "response_item_id": "fc_chatcmpl-tool-82b76914102f45be",
          "type": "function",
          "function": {
            "name": "read_file",
            "arguments": "{\"path\": \"/opt/hermes/hermes_constants.py\"}"
          }
        }
      ]
    },
    {
      "role": "tool",
      "content": "{\"content\": \"     1|\\\"\\\"\\\"Shared constants for Hermes Agent.\\n     2|\\n     3|Import-safe module with no dependencies — can be imported from anywhere\\n     4|without risk of circular imports.\\n     5|\\\"\\\"\\\"\\n     6|\\n     7|import os\\n     8|from pathlib import Path\\n     9|\\n    10|\\n    11|def get_hermes_home() -> Path:\\n    12|    \\\"\\\"\\\"Return the Hermes home directory (default: ~/.hermes).\\n    13|\\n    14|    Reads HERMES_HOME env var, falls back to ~/.hermes.\\n    15|    This is the single source of truth — all other copies should import this.\\n    16|    \\\"\\\"\\\"\\n    17|    return Path(os.getenv(\\\"HERMES_HOME\\\", Path.home() / \\\".hermes\\\"))\\n    18|\\n    19|\\n    20|def get_default_hermes_root() -> Path:\\n    21|    \\\"\\\"\\\"Return the root Hermes directory for profile-level operations.\\n    22|\\n    23|    In standard deployments this is ``~/.hermes``.\\n    24|\\n    25|    In Docker or custom deployments where ``HERMES_HOME`` points outside\\n    26|    ``~/.hermes`` (e.g. ``/opt/data``), returns ``HERMES_HOME`` directly\\n    27|    — that IS the root.\\n    28|\\n    29|    In profile mode where ``HERMES_HOME`` is ``<root>/profiles/<name>``,\\n    30|    returns ``<root>`` so that ``profile list`` can see all profiles.\\n    31|    Works both for standard (``~/.hermes/profiles/coder``) and Docker\\n    32|    (``/opt/data/profiles/coder``) layouts.\\n    33|\\n    34|    Import-safe — no dependencies beyond stdlib.\\n    35|    \\\"\\\"\\\"\\n    36|    native_home = Path.home() / \\\".hermes\\\"\\n    37|    env_home = os.environ.get(\\\"HERMES_HOME\\\", \\\"\\\")\\n    38|    if not env_home:\\n    39|        return native_home\\n    40|    env_path = Path(env_home)\\n    41|    try:\\n    42|        env_path.resolve().relative_to(native_home.resolve())\\n    43|        # HERMES_HOME is under ~/.hermes (normal or profile mode)\\n    44|        return native_home\\n    45|    except ValueError:\\n    46|        pass\\n    47|\\n    48|    # Docker / custom deployment.\\n    49|    # Check if this is a profile path: <root>/profiles/<name>\\n    50|    # If the immediate parent dir is named \\\"profiles\\\", the root is\\n    51|    # the grandparent — this covers Docker profiles correctly.\\n    52|    if env_path.parent.name == \\\"profiles\\\":\\n    53|        return env_path.parent.parent\\n    54|\\n    55|    # Not a profile path — HERMES_HOME itself is the root\\n    56|    return env_path\\n    57|\\n    58|\\n    59|def get_optional_skills_dir(default: Path | None = None) -> Path:\\n    60|    \\\"\\\"\\\"Return the optional-skills directory, honoring package-manager wrappers.\\n    61|\\n    62|    Packaged installs may ship ``optional-skills`` outside the Python package\\n    63|    tree and expose it via ``HERMES_OPTIONAL_SKILLS``.\\n    64|    \\\"\\\"\\\"\\n    65|    override = os.getenv(\\\"HERMES_OPTIONAL_SKILLS\\\", \\\"\\\").strip()\\n    66|    if override:\\n    67|        return Path(override)\\n    68|    if default is not None:\\n    69|        return default\\n    70|    return get_hermes_home() / \\\"optional-skills\\\"\\n    71|\\n    72|\\n    73|def get_hermes_dir(new_subpath: str, old_name: str) -> Path:\\n    74|    \\\"\\\"\\\"Resolve a Hermes subdirectory with backward compatibility.\\n    75|\\n    76|    New installs get the consolidated layout (e.g. ``cache/images``).\\n    77|    Existing installs that already have the old path (e.g. ``image_cache``)\\n    78|    keep using it — no migration required.\\n    79|\\n    80|    Args:\\n    81|        new_subpath: Preferred path relative to HERMES_HOME (e.g. ``\\\"cache/images\\\"``).\\n    82|        old_name: Legacy path relative to HERMES_HOME (e.g. ``\\\"image_cache\\\"``).\\n    83|\\n    84|    Returns:\\n    85|        Absolute ``Path`` — old location if it exists on disk, otherwise the new one.\\n    86|    \\\"\\\"\\\"\\n    87|    home = get_hermes_home()\\n    88|    old_path = home / old_name\\n    89|    if old_path.exists():\\n    90|        return old_path\\n    91|    return home / new_subpath\\n    92|\\n    93|\\n    94|def display_hermes_home() -> str:\\n    95|    \\\"\\\"\\\"Return a user-friendly display string for the current HERMES_HOME.\\n    96|\\n    97|    Uses ``~/`` shorthand for readability::\\n    98|\\n    99|        default:  ``~/.hermes``\\n   100|        profile:  ``~/.hermes/profiles/coder``\\n   101|        custom:   ``/opt/hermes-custom``\\n   102|\\n   103|    Use this in **user-facing** print/log messages instead of hardcoding\\n   104|    ``~/.hermes``.  For code that needs a real ``Path``, use\\n   105|    :func:`get_hermes_home` instead.\\n   106|    \\\"\\\"\\\"\\n   107|    home = get_hermes_home()\\n   108|    try:\\n   109|        return \\\"~/\\\" + str(home.relative_to(Path.home()))\\n   110|    except ValueError:\\n   111|        return str(home)\\n   112|\\n   113|\\n   114|def get_subprocess_home() -> str | None:\\n   115|    \\\"\\\"\\\"Return a per-profile HOME directory for subprocesses, or None.\\n   116|\\n   117|    When ``{HERMES_HOME}/home/`` exists on disk, subprocesses should use it\\n   118|    as ``HOME`` so system tools (git, ssh, gh, npm …) write their configs\\n   119|    inside the Hermes data directory instead of the OS-level ``/root`` or\\n   120|    ``~/``.  This provides:\\n   121|\\n   122|    * **Docker persistence** — tool configs land inside the persistent volume.\\n   123|    * **Profile isolation** — each profile gets its own git identity, SSH\\n   124|      keys, gh tokens, etc.\\n   125|\\n   126|    The Python process's own ``os.environ[\\\"HOME\\\"]`` and ``Path.home()`` are\\n   127|    **never** modified — only subprocess environments should inject this value.\\n   128|    Activation is directory-based: if the ``home/`` subdirectory doesn't\\n   129|    exist, returns ``None`` and behavior is unchanged.\\n   130|    \\\"\\\"\\\"\\n   131|    hermes_home = os.getenv(\\\"HERMES_HOME\\\")\\n   132|    if not hermes_home:\\n   133|        return None\\n   134|    profile_home = os.path.join(hermes_home, \\\"home\\\")\\n   135|    if os.path.isdir(profile_home):\\n   136|        return profile_home\\n   137|    return None\\n   138|\\n   139|\\n   140|VALID_REASONING_EFFORTS = (\\\"minimal\\\", \\\"low\\\", \\\"medium\\\", \\\"high\\\", \\\"xhigh\\\")\\n   141|\\n   142|\\n   143|def parse_reasoning_effort(effort: str) -> dict | None:\\n   144|    \\\"\\\"\\\"Parse a reasoning effort level into a config dict.\\n   145|\\n   146|    Valid levels: \\\"none\\\", \\\"minimal\\\", \\\"low\\\", \\\"medium\\\", \\\"high\\\", \\\"xhigh\\\".\\n   147|    Returns None when the input is empty or unrecognized (caller uses default).\\n   148|    Returns {\\\"enabled\\\": False} for \\\"none\\\".\\n   149|    Returns {\\\"enabled\\\": True, \\\"effort\\\": <level>} for valid effort levels.\\n   150|    \\\"\\\"\\\"\\n   151|    if not effort or not effort.strip():\\n   152|        return None\\n   153|    effort = effort.strip().lower()\\n   154|    if effort == \\\"none\\\":\\n   155|        return {\\\"enabled\\\": False}\\n   156|    if effort in VALID_REASONING_EFFORTS:\\n   157|        return {\\\"enabled\\\": True, \\\"effort\\\": effort}\\n   158|    return None\\n   159|\\n   160|\\n   161|def is_termux() -> bool:\\n   162|    \\\"\\\"\\\"Return True when running inside a Termux (Android) environment.\\n   163|\\n   164|    Checks ``TERMUX_VERSION`` (set by Termux) or the Termux-specific\\n   165|    ``PREFIX`` path.  Import-safe — no heavy deps.\\n   166|    \\\"\\\"\\\"\\n   167|    prefix = os.getenv(\\\"PREFIX\\\", \\\"\\\")\\n   168|    return bool(os.getenv(\\\"TERMUX_VERSION\\\") or \\\"com.termux/files/usr\\\" in prefix)\\n   169|\\n   170|\\n   171|_wsl_detected: bool | None = None\\n   172|\\n   173|\\n   174|def is_wsl() -> bool:\\n   175|    \\\"\\\"\\\"Return True when running inside WSL (Windows Subsystem for Linux).\\n   176|\\n   177|    Checks ``/proc/version`` for the ``microsoft`` marker that both WSL1\\n   178|    and WSL2 inject.  Result is cached for the process lifetime.\\n   179|    Import-safe — no heavy deps.\\n   180|    \\\"\\\"\\\"\\n   181|    global _wsl_detected\\n   182|    if _wsl_detected is not None:\\n   183|        return _wsl_detected\\n   184|    try:\\n   185|        with open(\\\"/proc/version\\\", \\\"r\\\") as f:\\n   186|            _wsl_detected = \\\"microsoft\\\" in f.read().lower()\\n   187|    except Exception:\\n   188|        _wsl_detected = False\\n   189|    return _wsl_detected\\n   190|\\n   191|\\n   192|_container_detected: bool | None = None\\n   193|\\n   194|\\n   195|def is_container() -> bool:\\n   196|    \\\"\\\"\\\"Return True when running inside a Docker/Podman container.\\n   197|\\n   198|    Checks ``/.dockerenv`` (Docker), ``/run/.containerenv`` (Podman),\\n   199|    and ``/proc/1/cgroup`` for container runtime markers.  Result is\\n   200|    cached for the process lifetime.  Import-safe — no heavy deps.\\n   201|    \\\"\\\"\\\"\\n   202|    global _container_detected\\n   203|    if _container_detected is not None:\\n   204|        return _container_detected\\n   205|    if os.path.exists(\\\"/.dockerenv\\\"):\\n   206|        _container_detected = True\\n   207|        return True\\n   208|    if os.path.exists(\\\"/run/.containerenv\\\"):\\n   209|        _container_detected = True\\n   210|        return True\\n   211|    try:\\n   212|        with open(\\\"/proc/1/cgroup\\\", \\\"r\\\") as f:\\n   213|            cgroup = f.read()\\n   214|            if \\\"docker\\\" in cgroup or \\\"podman\\\" in cgroup or \\\"/lxc/\\\" in cgroup:\\n   215|                _container_detected = True\\n   216|                return True\\n   217|    except OSError:\\n   218|        pass\\n   219|    _container_detected = False\\n   220|    return False\\n   221|\\n   222|\\n   223|# ─── Well-Known Paths ─────────────────────────────────────────────────────────\\n   224|\\n   225|\\n   226|def get_config_path() -> Path:\\n   227|    \\\"\\\"\\\"Return the path to ``config.yaml`` under HERMES_HOME.\\n   228|\\n   229|    Replaces the ``get_hermes_home() / \\\"config.yaml\\\"`` pattern repeated\\n   230|    in 7+ files (skill_utils.py, hermes_logging.py, hermes_time.py, etc.).\\n   231|    \\\"\\\"\\\"\\n   232|    return get_hermes_home() / \\\"config.yaml\\\"\\n   233|\\n   234|\\n   235|def get_skills_dir() -> Path:\\n   236|    \\\"\\\"\\\"Return the path to the skills directory under HERMES_HOME.\\\"\\\"\\\"\\n   237|    return get_hermes_home() / \\\"skills\\\"\\n   238|\\n   239|\\n   240|\\n   241|def get_env_path() -> Path:\\n   242|    \\\"\\\"\\\"Return the path to the ``.env`` file under HERMES_HOME.\\\"\\\"\\\"\\n   243|    return get_hermes_home() / \\\".env\\\"\\n   244|\\n   245|\\n   246|# ─── Network Preferences ─────────────────────────────────────────────────────\\n   247|\\n   248|\\n   249|def apply_ipv4_preference(force: bool = False) -> None:\\n   250|    \\\"\\\"\\\"Monkey-patch ``socket.getaddrinfo`` to prefer IPv4 connections.\\n   251|\\n   252|    On servers with broken or unreachable IPv6, Python tries AAAA records\\n   253|    first and hangs for the full TCP timeout before falling back to IPv4.\\n   254|    This affects httpx, requests, urllib, the OpenAI SDK — everything that\\n   255|    uses ``socket.getaddrinfo``.\\n   256|\\n   257|    When *force* is True, patches ``getaddrinfo`` so that calls with\\n   258|    ``family=AF_UNSPEC`` (the default) resolve as ``AF_INET`` instead,\\n   259|    skipping IPv6 entirely.  If no A record exists, falls back to the\\n   260|    original unfiltered resolution so pure-IPv6 hosts still work.\\n   261|\\n   262|    Safe to call multiple times — only patches once.\\n   263|    Set ``network.force_ipv4: true`` in ``config.yaml`` to enable.\\n   264|    \\\"\\\"\\\"\\n   265|    if not force:\\n   266|        return\\n   267|\\n   268|    import socket\\n   269|\\n   270|    # Guard against double-patching\\n   271|    if getattr(socket.getaddrinfo, \\\"_hermes_ipv4_patched\\\", False):\\n   272|        return\\n   273|\\n   274|    _original_getaddrinfo = socket.getaddrinfo\\n   275|\\n   276|    def _ipv4_getaddrinfo(host, port, family=0, type=0, proto=0, flags=0):\\n   277|        if family == 0:  # AF_UNSPEC — caller didn't request a specific family\\n   278|            try:\\n   279|                return _original_getaddrinfo(\\n   280|                    host, port, socket.AF_INET, type, proto, flags\\n   281|                )\\n   282|            except socket.gaierror:\\n   283|                # No A record — fall back to full resolution (pure-IPv6 hosts)\\n   284|                return _original_getaddrinfo(host, port, family, type, proto, flags)\\n   285|        return _original_getaddrinfo(host, port, family, type, proto, flags)\\n   286|\\n   287|    _ipv4_getaddrinfo._hermes_ipv4_patched = True  # type: ignore[attr-defined]\\n   288|    socket.getaddrinfo = _ipv4_getaddrinfo  # type: ignore[assignment]\\n   289|\\n   290|\\n   291|OPENROUTER_BASE_URL = \\\"https://openrouter.ai/api/v1\\\"\\n   292|OPENROUTER_MODELS_URL = f\\\"{OPENROUTER_BASE_URL}/models\\\"\\n   293|\\n   294|AI_GATEWAY_BASE_URL = \\\"https://ai-gateway.vercel.sh/v1\\\"\\n   295|\", \"total_lines\": 294, \"file_size\": 10358, \"truncated\": false, \"is_binary\": false, \"is_image\": false}",
      "tool_call_id": "chatcmpl-tool-82b76914102f45be"
    }
  ]
}