{"role": "session_meta", "tools": [{"type": "function", "function": {"name": "clarify", "description": "Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:\n\n1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.\n2. **Open-ended** — omit choices entirely. The user types a free-form response.\n\nUse this tool when:\n- The task is ambiguous and you need the user to choose an approach\n- You want post-task feedback ('How did that work out?')\n- You want to offer to save a skill or update memory\n- A decision has meaningful trade-offs the user should weigh in on\n\nDo NOT use this tool for simple yes/no confirmation of dangerous commands (the terminal tool handles that). Prefer making a reasonable default choice yourself when the decision is low-stakes.", "parameters": {"type": "object", "properties": {"question": {"type": "string", "description": "The question to present to the user."}, "choices": {"type": "array", "items": {"type": "string"}, "maxItems": 4, "description": "Up to 4 answer choices. Omit this parameter entirely to ask an open-ended question. When provided, the UI automatically appends an 'Other (type your answer)' option."}}, "required": ["question"]}}}, {"type": "function", "function": {"name": "cronjob", "description": "Manage scheduled cron jobs with a single compressed tool.\n\nUse action='create' to schedule a new job from a prompt or one or more skills.\nUse action='list' to inspect jobs.\nUse action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.\n\nTo stop a job the user no longer wants: first action='list' to find the job_id, then action='remove' with that job_id. Never guess job IDs — always list first.\n\nJobs run in a fresh session with no current-chat context, so prompts must be self-contained.\nIf skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.\nOn update, passing skills=[] clears attached skills.\n\nNOTE: The agent's final response is auto-delivered to the target. Put the primary\nuser-facing content in the final response. Cron jobs run autonomously with no user\npresent — they cannot ask questions or request clarification.\n\nImportant safety rule: cron-run sessions should not recursively schedule more cron jobs.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "description": "One of: create, list, update, pause, resume, remove, run"}, "job_id": {"type": "string", "description": "Required for update/pause/resume/remove/run"}, "prompt": {"type": "string", "description": "For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills."}, "schedule": {"type": "string", "description": "For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp"}, "name": {"type": "string", "description": "Optional human-friendly name"}, "repeat": {"type": "integer", "description": "Optional repeat count. Omit for defaults (once for one-shot, forever for recurring)."}, "deliver": {"type": "string", "description": "Omit this parameter to auto-deliver back to the current chat and topic (recommended). Auto-detection preserves thread/topic context. Only set explicitly when the user asks to deliver somewhere OTHER than the current conversation. Values: 'origin' (same as omitting), 'local' (no delivery, save only), or platform:chat_id:thread_id for a specific destination. Examples: 'telegram:-1001234567890:17585', 'discord:#engineering', 'sms:+15551234567'. WARNING: 'platform:chat_id' without :thread_id loses topic targeting."}, "skills": {"type": "array", "items": {"type": "string"}, "description": "Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills."}, "model": {"type": "object", "description": "Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.", "properties": {"provider": {"type": "string", "description": "Provider name (e.g. 'openrouter', 'anthropic', or 'custom:<name>' for a provider defined in custom_providers config — always include the ':<name>' suffix, never pass the bare 'custom'). Omit to use and pin the current provider."}, "model": {"type": "string", "description": "Model name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')"}}, "required": ["model"]}, "script": {"type": "string", "description": "Optional path to a script that runs each tick. In the default mode its stdout is injected into the agent's prompt as context (data-collection / change-detection pattern). With no_agent=True, the script IS the job and its stdout is delivered verbatim (classic watchdog pattern). Relative paths resolve under ~/.hermes/scripts/. ``.sh``/``.bash`` extensions run via bash, everything else via Python. On update, pass empty string to clear."}, "no_agent": {"type": "boolean", "default": false, "description": "Default: False (LLM-driven job — the agent runs the prompt each tick). Set True to skip the LLM entirely: the scheduler just runs ``script`` on schedule and delivers its stdout verbatim. No tokens, no agent loop, no model override honoured. \n\nREQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored). \n\nDELIVERY SEMANTICS when True: (a) non-empty stdout is sent verbatim as the message; (b) EMPTY stdout means SILENT — nothing is sent to the user and they won't see anything happened, so design your script to stay quiet when there's nothing to report (the watchdog pattern); (c) non-zero exit / timeout sends an error alert so a broken watchdog can't fail silently. \n\nWHEN TO USE True: recurring script-only pings where the script itself produces the exact message text (memory/disk/GPU watchdogs, threshold alerts, heartbeats, CI notifications, API pollers with a fixed output shape). WHEN TO USE False (default): anything that needs reasoning — summarize a feed, draft a daily briefing, pick interesting items, rephrase data for a human, follow conditional logic based on content."}, "context_from": {"type": "array", "items": {"type": "string"}, "description": "Optional job ID or list of job IDs whose most recent completed output is injected into the prompt as context before each run. Use this to chain cron jobs: job A collects data, job B processes it. Each entry must be a valid job ID (from cronjob action='list'). Note: injects the most recent completed output — does not wait for upstream jobs running in the same tick. On update, pass an empty array to clear."}, "enabled_toolsets": {"type": "array", "items": {"type": "string"}, "description": "Optional list of toolset names to restrict the job's agent to (e.g. [\"web\", \"terminal\", \"file\", \"delegation\"]). When set, only tools from these toolsets are loaded, significantly reducing input token overhead. When omitted, all default tools are loaded. Infer from the job's prompt — e.g. use \"web\" if it calls web_search, \"terminal\" if it runs scripts, \"file\" if it reads files, \"delegation\" if it calls delegate_task. On update, pass an empty array to clear."}, "workdir": {"type": "string", "description": "Optional absolute path to run the job from. When set, AGENTS.md / CLAUDE.md / .cursorrules from that directory are injected into the system prompt, and the terminal/file/code_exec tools use it as their working directory — useful for running a job inside a specific project repo. Must be an absolute path that exists. When unset (default), preserves the original behaviour: no project context files, tools use the scheduler's cwd. On update, pass an empty string to clear. Jobs with workdir run sequentially (not parallel) to keep per-job directories isolated."}}, "required": ["action"]}}}, {"type": "function", "function": {"name": "delegate_task", "description": "Spawn one or more subagents to work on tasks in isolated contexts. Each subagent gets its own conversation, terminal session, and toolset. Only the final summary is returned -- intermediate tool results never enter your context window.\n\nTWO MODES (one of 'goal' or 'tasks' is required):\n1. Single task: provide 'goal' (+ optional context, toolsets)\n2. Batch (parallel): provide 'tasks' array with up to delegation.max_concurrent_children items (default 3, configurable via config.yaml, no hard ceiling). All run concurrently and results are returned together. Nested delegation requires role='orchestrator' and delegation.max_spawn_depth >= 2.\n\nWHEN TO USE delegate_task:\n- Reasoning-heavy subtasks (debugging, code review, research synthesis)\n- Tasks that would flood your context with intermediate data\n- Parallel independent workstreams (research A and B simultaneously)\n\nWHEN NOT TO USE (use these instead):\n- Mechanical multi-step work with no reasoning needed -> use execute_code\n- Single tool call -> just call the tool directly\n- Tasks needing user interaction -> subagents cannot use clarify\n- Durable long-running work that must outlive the current turn -> use cronjob (action='create') or terminal(background=True, notify_on_complete=True) instead. delegate_task runs SYNCHRONOUSLY inside the parent turn: if the parent is interrupted (user sends a new message, /stop, /new) the child is cancelled with status='interrupted' and its work is discarded. Children cannot continue in the background.\n\nIMPORTANT:\n- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.\n- If the user is writing in a non-English language, or asked for output in a specific language / tone / style, say so in 'context' (e.g. \"respond in Chinese\", \"return output in Japanese\"). Otherwise subagents default to English and their summaries will contaminate your final reply with the wrong language.\n- Subagent summaries are SELF-REPORTS, not verified facts. A subagent that claims \"uploaded successfully\" or \"file written\" may be wrong. For operations with external side-effects (HTTP POST/PUT, remote writes, file creation at shared paths, publishing), require the subagent to return a verifiable handle (URL, ID, absolute path, HTTP status) and verify it yourself — fetch the URL, stat the file, read back the content — before telling the user the operation succeeded.\n- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.\n- Orchestrator subagents (role='orchestrator') retain delegate_task so they can spawn their own workers, but still cannot use clarify, memory, send_message, or execute_code. Orchestrators are bounded by delegation.max_spawn_depth (default 2) and can be disabled globally via delegation.orchestrator_enabled=false.\n- Each subagent gets its own terminal session (separate working directory and state).\n- Results are always returned as an array, one entry per task.", "parameters": {"type": "object", "properties": {"goal": {"type": "string", "description": "What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history."}, "context": {"type": "string", "description": "Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs."}, "toolsets": {"type": "array", "items": {"type": "string"}, "description": "Toolsets to enable for this subagent. Default: inherits your enabled toolsets. Available toolsets: 'browser', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'vision', 'web', 'yuanbao'. Common patterns: ['terminal', 'file'] for code work, ['web'] for research, ['browser'] for web interaction, ['terminal', 'file', 'web'] for full-stack tasks."}, "tasks": {"type": "array", "items": {"type": "object", "properties": {"goal": {"type": "string", "description": "Task goal"}, "context": {"type": "string", "description": "Task-specific context"}, "toolsets": {"type": "array", "items": {"type": "string"}, "description": "Toolsets for this specific task. Available: 'browser', 'cronjob', 'discord', 'discord_admin', 'feishu_doc', 'feishu_drive', 'file', 'homeassistant', 'image_gen', 'kanban', 'search', 'session_search', 'skills', 'spotify', 'terminal', 'todo', 'tts', 'video', 'vision', 'web', 'yuanbao'. Use 'web' for network access, 'terminal' for shell, 'browser' for web interaction."}, "acp_command": {"type": "string", "description": "Per-task ACP command override (e.g. 'claude'). Overrides the top-level acp_command for this task only."}, "acp_args": {"type": "array", "items": {"type": "string"}, "description": "Per-task ACP args override."}, "role": {"type": "string", "enum": ["leaf", "orchestrator"], "description": "Per-task role override. See top-level 'role' for semantics."}}, "required": ["goal"]}, "description": "Batch mode: tasks to run in parallel (limit configurable via delegation.max_concurrent_children, default 3). Each gets its own subagent with isolated context and terminal session. When provided, top-level goal/context/toolsets are ignored."}, "role": {"type": "string", "enum": ["leaf", "orchestrator"], "description": "Role of the child agent. 'leaf' (default) = focused worker, cannot delegate further. 'orchestrator' = can use delegate_task to spawn its own workers. Requires delegation.max_spawn_depth >= 2 in config; ignored (treated as 'leaf') when the child would exceed max_spawn_depth or when delegation.orchestrator_enabled=false."}, "acp_command": {"type": "string", "description": "Override ACP command for child agents (e.g. 'claude', 'copilot'). When set, children use ACP subprocess transport instead of inheriting the parent's transport. Enables spawning Claude Code (claude --acp --stdio) or other ACP-capable agents from any parent, including Discord/Telegram/CLI."}, "acp_args": {"type": "array", "items": {"type": "string"}, "description": "Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Example: ['--acp', '--stdio', '--model', 'claude-opus-4-6']"}}}}}, {"type": "function", "function": {"name": "execute_code", "description": "Run a Python script that can call Hermes tools programmatically. Use this when you need 3+ tool calls with processing logic between them, need to filter/reduce large tool outputs before they enter your context, need conditional branching (if X then Y else Z), or need to loop (fetch N pages, process N files, retry on failure).\n\nUse normal tool calls instead when: single tool call with no processing, you need to see the full result and apply complex reasoning, or the task requires interactive user input.\n\nAvailable via `from hermes_tools import ...`:\n\n  read_file(path: str, offset: int = 1, limit: int = 500) -> dict\n    Lines are 1-indexed. Returns {\"content\": \"...\", \"total_lines\": N}\n  write_file(path: str, content: str) -> dict\n    Always overwrites the entire file.\n  search_files(pattern: str, target=\"content\", path=\".\", file_glob=None, limit=50) -> dict\n    target: \"content\" (search inside files) or \"files\" (find files by name). Returns {\"matches\": [...]}\n  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict\n    Replaces old_string with new_string in the file.\n  terminal(command: str, timeout=None, workdir=None) -> dict\n    Foreground only (no background/pty). Returns {\"output\": \"...\", \"exit_code\": N}\n\nLimits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).\n\nScripts run in the session's working directory with the active venv's python, so project deps (pandas, etc.) and relative paths work like in terminal().\n\nPrint your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.\n\nAlso available (no import needed — built into hermes_tools):\n  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars\n  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands\n  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures", "parameters": {"type": "object", "properties": {"code": {"type": "string", "description": "Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout."}}, "required": ["code"]}}}, {"type": "function", "function": {"name": "memory", "description": "Save durable information to persistent memory that survives across sessions. Memory is injected into future turns, so keep it compact and focused on facts that will still matter later.\n\nWHEN TO SAVE (do this proactively, don't wait to be asked):\n- User corrects you or says 'remember this' / 'don't do that again'\n- User shares a preference, habit, or personal detail (name, role, timezone, coding style)\n- You discover something about the environment (OS, installed tools, project structure)\n- You learn a convention, API quirk, or workflow specific to this user's setup\n- You identify a stable fact that will be useful again in future sessions\n\nPRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.\n\nDo NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.\nIf you've discovered a new way to do something, solved a problem that could be necessary later, save it as a skill with the skill tool.\n\nTWO TARGETS:\n- 'user': who the user is -- name, role, preferences, communication style, pet peeves\n- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned\n\nACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).\n\nSKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["add", "replace", "remove"], "description": "The action to perform."}, "target": {"type": "string", "enum": ["memory", "user"], "description": "Which memory store: 'memory' for personal notes, 'user' for user profile."}, "content": {"type": "string", "description": "The entry content. Required for 'add' and 'replace'."}, "old_text": {"type": "string", "description": "Short unique substring identifying the entry to replace or remove."}}, "required": ["action", "target"]}}}, {"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": "process", "description": "Manage background processes started with terminal(background=true). Actions: 'list' (show all), 'poll' (check status + new output), 'log' (full output with pagination), 'wait' (block until done or timeout), 'kill' (terminate), 'write' (send raw stdin data without newline), 'submit' (send data + Enter, for answering prompts), 'close' (close stdin/send EOF).", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["list", "poll", "log", "wait", "kill", "write", "submit", "close"], "description": "Action to perform on background processes"}, "session_id": {"type": "string", "description": "Process session ID (from terminal background output). Required for all actions except 'list'."}, "data": {"type": "string", "description": "Text to send to process stdin (for 'write' and 'submit' actions)"}, "timeout": {"type": "integer", "description": "Max seconds to block for 'wait' action. Returns partial output on timeout.", "minimum": 1}, "offset": {"type": "integer", "description": "Line offset for 'log' action (default: last 200 lines)"}, "limit": {"type": "integer", "description": "Max lines to return for 'log' action", "minimum": 1}}, "required": ["action"]}}}, {"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": "send_message", "description": "Send a message to a connected messaging platform, or list available targets.\n\nIMPORTANT: When the user asks to send to a specific channel or person (not just a bare platform name), call send_message(action='list') FIRST to see available targets, then send to the correct one.\nIf the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["send", "list"], "description": "Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms."}, "target": {"type": "string", "description": "Delivery target. Format: 'platform' (uses home channel), 'platform:#channel-name', 'platform:chat_id', or 'platform:chat_id:thread_id' for Telegram topics and Discord threads. Examples: 'telegram', 'telegram:-1001234567890:17585', 'discord:999888777:555444333', 'discord:#bot-home', 'slack:#engineering', 'signal:+155****4567', 'matrix:!roomid:server.org', 'matrix:@user:server.org', 'yuanbao:direct:<account_id>' (DM), 'yuanbao:group:<group_code>' (group chat)"}, "message": {"type": "string", "description": "The message text to send. To send an image or file, include MEDIA:<local_path> (e.g. 'MEDIA:/tmp/hermes/cache/img_xxx.jpg') in the message — the platform will deliver it as a native media attachment."}}}}}, {"type": "function", "function": {"name": "session_search", "description": "Search your long-term memory of past conversations, or browse recent sessions. This is your recall -- every past session is searchable, and this tool summarizes what happened.\n\nTWO MODES:\n1. Recent sessions (no query): Call with no arguments to see what was worked on recently. Returns titles, previews, and timestamps. Zero LLM cost, instant. Start here when the user asks what were we working on or what did we do recently.\n2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.\n\nUSE THIS PROACTIVELY when:\n- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'\n- The user asks about a topic you worked on before but don't have in current context\n- The user references a project, person, or concept that seems familiar but isn't in memory\n- You want to check if you've solved a similar problem before\n- The user asks 'what did we do about X?' or 'how did we fix Y?'\n\nDon't hesitate to search when it is actually cross-session -- it's fast and cheap. Better to search and confirm than to guess or ask the user to repeat themselves.\n\nSearch syntax: keywords joined with OR for broad recall (elevenlabs OR baseten OR funding), phrases for exact match (\"docker networking\"), boolean (python NOT java), prefix (deploy*). IMPORTANT: Use OR between keywords for best results — FTS5 defaults to AND which misses sessions that only mention some terms. If a broad OR query returns nothing, try individual keyword searches in parallel. Returns summaries of the top matching sessions.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query — keywords, phrases, or boolean expressions to find in past sessions. Omit this parameter entirely to browse recent sessions instead (returns titles, previews, timestamps with no LLM cost)."}, "role_filter": {"type": "string", "description": "Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs."}, "limit": {"type": "integer", "description": "Max sessions to summarize (default: 3, max: 5).", "default": 3}}}}}, {"type": "function", "function": {"name": "skill_manage", "description": "Manage skills (create, update, delete). Skills are your procedural memory — reusable approaches for recurring task types. New skills go to ~/.hermes/skills/; existing skills can be modified wherever they live.\n\nActions: create (full SKILL.md + optional category), patch (old_string/new_string — preferred for fixes), edit (full SKILL.md rewrite — major overhauls only), delete, write_file, remove_file.\n\nOn delete, pass `absorbed_into=<umbrella>` when you're merging this skill's content into another one, or `absorbed_into=\"\"` when you're pruning it with no forwarding target. This lets the curator tell consolidation from pruning without guessing, so downstream consumers (cron jobs that reference the old skill name, etc.) get updated correctly. The target you name in `absorbed_into` must already exist — create/patch the umbrella first, then delete.\n\nCreate when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.\nUpdate when: instructions stale/wrong, OS-specific failures, missing steps or pitfalls found during use. If you used a skill and hit issues not covered by it, patch it immediately.\n\nAfter difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.\n\nGood skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.\n\nPinned skills are protected from deletion only — skill_manage(action='delete') will refuse with a message pointing the user to `hermes curator unpin <name>`. Patches and edits go through on pinned skills so you can still improve them as pitfalls come up; pin only guards against irrecoverable loss.", "parameters": {"type": "object", "properties": {"action": {"type": "string", "enum": ["create", "patch", "edit", "delete", "write_file", "remove_file"], "description": "The action to perform."}, "name": {"type": "string", "description": "Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file."}, "content": {"type": "string", "description": "Full SKILL.md content (YAML frontmatter + markdown body). Required for 'create' and 'edit'. For 'edit', read the skill first with skill_view() and provide the complete updated text."}, "old_string": {"type": "string", "description": "Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness."}, "new_string": {"type": "string", "description": "Replacement text (required for 'patch'). Can be empty string to delete the matched text."}, "replace_all": {"type": "boolean", "description": "For 'patch': replace all occurrences instead of requiring a unique match (default: false)."}, "category": {"type": "string", "description": "Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'."}, "file_path": {"type": "string", "description": "Path to a supporting file within the skill directory. For 'write_file'/'remove_file': required, must be under references/, templates/, scripts/, or assets/. For 'patch': optional, defaults to SKILL.md if omitted."}, "file_content": {"type": "string", "description": "Content for the file. Required for 'write_file'."}, "absorbed_into": {"type": "string", "description": "For 'delete' only — declares intent so the curator can tell consolidation from pruning without guessing. Pass the umbrella skill name when this skill's content was merged into another (the target must already exist). Pass an empty string when the skill is truly stale and being pruned with no forwarding target. Omitting the arg on delete is supported for backward compatibility but downstream tooling (e.g. cron-job skill reference rewriting) will have to guess at intent."}}, "required": ["action", "name"]}}}, {"type": "function", "function": {"name": "skill_view", "description": "Skills allow for loading information about specific tasks and workflows, as well as scripts and templates. Load a skill's full content or access its linked files (references, templates, scripts). First call returns SKILL.md content plus a 'linked_files' dict showing available references/templates/scripts. To access those, call again with file_path parameter.", "parameters": {"type": "object", "properties": {"name": {"type": "string", "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')."}, "file_path": {"type": "string", "description": "OPTIONAL: Path to a linked file within the skill (e.g., 'references/api.md', 'templates/config.yaml', 'scripts/validate.py'). Omit to get the main SKILL.md content."}}, "required": ["name"]}}}, {"type": "function", "function": {"name": "skills_list", "description": "List available skills (name + description). Use skill_view(name) to load full content.", "parameters": {"type": "object", "properties": {"category": {"type": "string", "description": "Optional category filter to narrow results"}}}}}, {"type": "function", "function": {"name": "terminal", "description": "Execute shell commands on a Linux environment. Filesystem usually persists between calls.\n\nDo NOT use cat/head/tail to read files — use read_file instead.\nDo NOT use grep/rg/find to search — use search_files instead.\nDo NOT use ls to list directories — use search_files(target='files') instead.\nDo NOT use sed/awk to edit files — use patch instead.\nDo NOT use echo/cat heredoc to create files — use write_file instead.\nReserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.\n\nForeground (default): Commands return INSTANTLY when done, even if the timeout is high. Set timeout=300 for long builds/scripts — you'll still get the result in seconds if it's fast. Prefer foreground for short commands.\nBackground: Set background=true to get a session_id. Two patterns:\n  (1) Long-lived processes that never exit (servers, watchers).\n  (2) Long-running tasks with notify_on_complete=true — you can keep working on other things and the system auto-notifies you when the task finishes. Great for test suites, builds, deployments, or anything that takes more than a minute.\nFor servers/watchers, do NOT use shell-level background wrappers (nohup/disown/setsid/trailing '&') in foreground mode. Use background=true so Hermes can track lifecycle and output.\nAfter starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.\nUse process(action=\"poll\") for progress checks, process(action=\"wait\") to block until done.\nWorking directory: Use 'workdir' for per-command cwd.\nPTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).\n\nDo NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.\n", "parameters": {"type": "object", "properties": {"command": {"type": "string", "description": "The command to execute on the VM"}, "background": {"type": "boolean", "description": "Run the command in the background. Two patterns: (1) Long-lived processes that never exit (servers, watchers). (2) Long-running tasks paired with notify_on_complete=true — you can keep working and get notified when the task finishes. For short commands, prefer foreground with a generous timeout instead.", "default": false}, "timeout": {"type": "integer", "description": "Max seconds to wait (default: 180, foreground max: 600). Returns INSTANTLY when command finishes — set high for long tasks, you won't wait unnecessarily. Foreground timeout above 600s is rejected; use background=true for longer commands.", "minimum": 1}, "workdir": {"type": "string", "description": "Working directory for this command (absolute path). Defaults to the session working directory."}, "pty": {"type": "boolean", "description": "Run in pseudo-terminal (PTY) mode for interactive CLI tools like Codex, Claude Code, or Python REPL. Only works with local and SSH backends. Default: false.", "default": false}, "notify_on_complete": {"type": "boolean", "description": "When true (and background=true), you'll be automatically notified exactly once when the process finishes. **This is the right choice for almost every long-running task** — tests, builds, deployments, multi-item batch jobs, anything that takes over a minute and has a defined end. Use this and keep working on other things; the system notifies you on exit. MUTUALLY EXCLUSIVE with watch_patterns — when both are set, watch_patterns is dropped.", "default": false}, "watch_patterns": {"type": "array", "items": {"type": "string"}, "description": "Strings to watch for in background process output. HARD RATE LIMIT: at most 1 notification per 15 seconds per process — matches arriving inside the cooldown are dropped. After 3 consecutive 15-second windows with dropped matches, watch_patterns is automatically disabled for that process and promoted to notify_on_complete behavior (one notification on exit, no more mid-process spam). USE ONLY for truly rare, one-shot mid-process signals on LONG-LIVED processes that will never exit on their own — e.g. ['Application startup complete'] on a server so you know when to hit its endpoint, or ['migration done'] on a daemon. DO NOT use for: (1) end-of-run markers like 'DONE'/'PASS' — use notify_on_complete instead; (2) error patterns like 'ERROR'/'Traceback' in loops or multi-item batch jobs — they fire on every iteration and you'll hit the strike limit fast; (3) anything you'd ever combine with notify_on_complete. When in doubt, choose notify_on_complete. MUTUALLY EXCLUSIVE with notify_on_complete — set one, not both."}}, "required": ["command"]}}}, {"type": "function", "function": {"name": "text_to_speech", "description": "Convert text to speech audio. Returns a MEDIA: path that the platform delivers as native audio. Compatible providers render as a voice bubble on Telegram; otherwise audio is sent as a regular attachment. In CLI mode, saves to ~/voice-memos/. Voice and provider are user-configured (built-in providers like edge/openai or custom command providers under tts.providers.<name>), not model-selected.", "parameters": {"type": "object", "properties": {"text": {"type": "string", "description": "The text to convert to speech. Provider-specific character caps apply and are enforced automatically (OpenAI 4096, xAI 15000, MiniMax 10000, ElevenLabs 5k-40k depending on model); over-long input is truncated."}, "output_path": {"type": "string", "description": "Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3"}}, "required": ["text"]}}}, {"type": "function", "function": {"name": "todo", "description": "Manage your task list for the current session. Use for complex tasks with 3+ steps or when the user provides multiple tasks. Call with no parameters to read the current list.\n\nWriting:\n- Provide 'todos' array to create/update items\n- merge=false (default): replace the entire list with a fresh plan\n- merge=true: update existing items by id, add any new ones\n\nEach item: {id: string, content: string, status: pending|in_progress|completed|cancelled}\nList order is priority. Only ONE item in_progress at a time.\nMark items completed immediately when done. If something fails, cancel it and add a revised item.\n\nAlways returns the full current list.", "parameters": {"type": "object", "properties": {"todos": {"type": "array", "description": "Task items to write. Omit to read current list.", "items": {"type": "object", "properties": {"id": {"type": "string", "description": "Unique item identifier"}, "content": {"type": "string", "description": "Task description"}, "status": {"type": "string", "enum": ["pending", "in_progress", "completed", "cancelled"], "description": "Current status"}}, "required": ["id", "content", "status"]}}, "merge": {"type": "boolean", "description": "true: update existing items by id, add new ones. false (default): replace the entire list.", "default": false}}}}}, {"type": "function", "function": {"name": "vision_analyze", "description": "Inspect an image from a URL, file path, or tool output when you need closer detail than what's visible in the conversation. If the user's image is already attached to the conversation and you can see it, just answer directly — only call this tool for images referenced by URL/path, images returned inside other tool results (browser screenshots, search thumbnails), or when you need a deeper look at a specific region the main model's vision may have missed.", "parameters": {"type": "object", "properties": {"image_url": {"type": "string", "description": "Image URL (http/https) or local file path to analyze."}, "question": {"type": "string", "description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question."}}, "required": ["image_url", "question"]}}}, {"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. Auto-runs syntax checks on .py/.json/.yaml/.toml and other linted languages; only NEW errors introduced by this write are surfaced (pre-existing errors are filtered out).", "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"]}}}], "model": "deepseek-v4-pro", "platform": "telegram", "timestamp": "2026-05-24T09:06:17.578653"}
{"role": "user", "content": "Du kannst subagents spawnen oder? Erkläre mir das Konzept bitte", "timestamp": "2026-05-24T09:06:17.578653"}
{"role": "assistant", "content": "Ja, kann ich. Das Konzept ist einfach:\n\n**Was sind Subagents?**\n\nDas sind isolierte KI-Arbeiter, die ich parallel losschicken kann. Jeder bekommt eine eigene Session mit eigenem Terminal, eigenem Kontext — komplett getrennt von meinem.\n\n**Wie funktioniert's?**\n\nIch rufe `delegate_task` auf mit:\n- **goal** — was der Subagent tun soll (z.B. \"Finde alle Bugs in /app/src\")\n- **context** — Hintergrundinfos, die er braucht (Dateipfade, Fehler, Constraints)\n- **toolsets** — welche Werkzeuge er kriegt (terminal, web, file…)\n\nDer Subagent arbeitet dann autark, und am Ende kriege ich nur die Zusammenfassung zurück. Alle Zwischenschritte (Tool-Outputs, Logs) landen nie in meinem Kontextfenster.\n\n**Zwei Modi:**\n\n1. **Single** — ein Subagent, eine Aufgabe\n2. **Batch** — bis zu X Subagents parallel (standardmäßig 3), alle gleichzeitig\n\n**Wann sinnvoll?**\n- Schwere Denkarbeit, die viel Kontext frisst (Debugging, Code-Review)\n- Mehrere unabhängige Tasks parallel (Recherche A + Recherche B gleichzeitig)\n- Tasks, deren Zwischenergebnisse mich nicht interessieren\n\n**Wann NICHT?**\n- Einfache Tool-Calls (direkt aufrufen)\n- Tasks, die User-Feedback brauchen (Subagents können kein `clarify`)\n- Langlebige Dinge, die über den aktuellen Turn hinausgehen → dafür `cronjob`\n\n**Wichtige Einschränkung:** Subagents sind *synchron* — wenn der aktuelle Turn endet (du schreibst eine neue Nachricht), wird der Subagent abgebrochen. Nichts läuft im Hintergrund weiter.\n\nDas ist quasi \"Outsource & vergessen\" für Denkarbeit — ich krieg nur das Ergebnis, nicht den ganzen Rechenweg.", "reasoning": "The user is asking me to explain the concept of subagents (delegate_task). Let me load the relevant skill first since there's an autonomous-ai-agents category with relevant skills.\n\nActually, let me just explain it directly. I don't necessarily need a skill for this - it's a straightforward question about the tool's functionality. But let me check if there's a relevant skill that might have more context about how the user likes things explained.\n\nThe question is: \"Du kannst subagents spawnen oder? Erkläre mir das Konzept bitte\" - \"You can spawn subagents, right? Explain the concept to me please.\"\n\nLet me just explain it clearly and directly. The user prefers concise responses.", "finish_reason": "stop", "reasoning_content": "The user is asking me to explain the concept of subagents (delegate_task). Let me load the relevant skill first since there's an autonomous-ai-agents category with relevant skills.\n\nActually, let me just explain it directly. I don't necessarily need a skill for this - it's a straightforward question about the tool's functionality. But let me check if there's a relevant skill that might have more context about how the user likes things explained.\n\nThe question is: \"Du kannst subagents spawnen oder? Erkläre mir das Konzept bitte\" - \"You can spawn subagents, right? Explain the concept to me please.\"\n\nLet me just explain it clearly and directly. The user prefers concise responses.", "timestamp": "2026-05-24T09:06:17.578653"}
{"role": "user", "content": "Haben wir das schonmal genutzt?", "timestamp": "2026-05-24T09:10:41.237199"}
{"role": "assistant", "content": "", "reasoning": "Der Benutzer fragt, ob wir Subagents/delegate_task schon einmal genutzt haben. Ich sollte in den vergangenen Sessions danach suchen.", "finish_reason": "tool_calls", "reasoning_content": "Der Benutzer fragt, ob wir Subagents/delegate_task schon einmal genutzt haben. Ich sollte in den vergangenen Sessions danach suchen.", "tool_calls": [{"id": "call_44myfd0e", "call_id": "call_44myfd0e", "response_item_id": "fc_44myfd0e", "type": "function", "function": {"name": "session_search", "arguments": "{\"query\":\"delegate_task OR subagent OR parallel workers\",\"limit\":5}"}}], "timestamp": "2026-05-24T09:10:41.237199"}
{"role": "tool", "name": "session_search", "content": "{\"success\": true, \"query\": \"delegate_task OR subagent OR parallel workers\", \"results\": [{\"session_id\": \"20260520_184117_e83b963e\", \"when\": \"May 20, 2026 at 06:41 PM\", \"source\": \"telegram\", \"model\": \"deepseek-v4-pro\", \"summary\": \"**Summary**\\n\\nThe user sought to replace their Anthropic Codex subscription with a self-hosted stack by routing Claude Code through local models via the Claude Code Router (CCR) on ZimaOS. A key objective was enabling **subagent delegation** so the primary assistant (Hermes) could spawn Claude Code as a background worker for coding tasks.\\n\\n**What was accomplished**\\n\\n1. **Claude Code Router Installation & Setup** — Node.js v22.12.0, Claude Code v2.1.145, and CCR v1.0.51 were installed under `/DATA/.local/`. CCR was configured to proxy to Ollama Cloud models: `deepseek-v4-pro` (default), `kimi-k2.6` (reasoning), and `gemma3:4b` (background). The service was enabled as a systemd unit (`claude-code-router.service`) listening on `0.0.0.0:3456`.\\n\\n2. **Subagent/Delegation Attempts**\\n   - **First attempt via `delegate_task` failed.** The transcript shows a direct `delegate_task(acp_command='claude')` call returned: *“Could not start Copilot ACP command 'claude'. Install GitHub Copilot CLI or set HERMES_COPILOT_ACP_COMMAND/COPILOT_CLI_PATH.”* The subagent environment lacked the required `PATH` to the `claude` binary.\\n   - **Fallback to explicit background terminal subagent succeeded.** The assistant spawned Claude Code directly as a background process with environment overrides:\\n     ```bash\\n     export PATH=\\\"/DATA/.local/nodejs/bin:/DATA/.local/npm-global/bin:$PATH\\\"\\n     HOME=/DATA/.local TERM=dumb ccr code --add-dir /media/HDD_1TB/PrintVault \\\\\\n       --permission-mode bypassPermissions -p \\\"Implementiere Idee #7...\\\" < /dev/null\\n     ```\\n   - **First background process (proc_eed57d1437c4, pid 1940380)** stalled with a “no stdin data received in 3s” warning from Claude Code and produced no code changes.\\n   - **Second background process (proc_f5e13200b5e4, pid 1945529)** was launched with the stdin redirect (`< /dev/null`) and `TERM=dumb`, which completed successfully.\\n\\n3. **Work Delivered by the Subagent**\\n   - *Analysis phase:* The subagent read `/media/HDD_1TB/PrintVault/main.py` and `/media/HDD_1TB/PrintVault/frontend/index.html` and produced 10 improvement ideas (7 UI-focused, 3 backend).\\n   - *Implementation phase:* The user requested the subagent implement **Idea #7 (Trash UI / Papierkorb)**. The subagent edited the two existing files directly:\\n     - **Backend (`main.py`):** Added 4 new endpoints: `GET /api/trash`, `POST /api/trash/{filename}/restore`, `DELETE /api/trash/{filename}`, and `GET /api/trash-thumbs/{filename}`. It also created restore logic that moves files back from `/trash` to `/stls` and `/thumbs`, with collision handling via new UUID generation.\\n     - **Frontend (`index.html`):** Added a Trash icon button with badge counter in the header, view-toggle tabs (“Bibliothek | Papierkorb”), greyed-out trash cards with hover actions, a confirmation modal for permanent deletion, empty-state messaging, and dark-mode/mobile support.\\n\\n4. **Key Technical Details**\\n   - **File paths:** `/media/HDD_1TB/PrintVault/main.py`, `/media/HDD_1TB/PrintVault/frontend/index.html`\\n   - **CCR config:** Written to `/DATA/.local/.claude-code-router/config.json`\\n   - **Environment aliases:** `cc` → `HOME=/DATA/.local ccr code`; `ccr-start`/`ccr-stop`/`ccr-status` for service control\\n   - **Verification:** Logs confirmed the subagent used `kimi-k2.6` via CCR (`\\\"model\\\":\\\"kimi-k2.6\\\"`) rather than Anthropic models.\\n   - **Result sizes:** `main.py` grew from ~654 to 788 lines; `index.html` from ~1,584 to 1,935 lines.\\n\\n**What remained unresolved**\\n- The native `delegate_task` Hermes skill integration was **not fixed**; it still requires setting `HERMES_COPILOT_ACP_COMMAND` or ensuring the subagent environment inherits the custom Node/npm `PATH`. All successful delegation in this session was done via raw terminal background processes instead of the structured `delegate_task` tool.\\n- Minor `tput: No value for $TERM` noise appeared in subagent output due to the headless shell environment, though it did not block execution.\\n\\n**Conclusion**\\nThe user achieved a fully functional Claude Code subagent workflow backed by Ollama Cloud models. The Trash UI feature was successfully designed by the assistant and implemented by a background Claude Code subagent via CCR, proving the parallel-worker/subagent concept works end-to-end, even though the native `delegate_task` wrapper still needs environment wiring.\"}, {\"session_id\": \"20260521_095746_b3bf3204\", \"when\": \"May 21, 2026 at 09:57 AM\", \"source\": \"telegram\", \"model\": \"deepseek-v4-pro\", \"summary\": \"**Summary focused on `delegate_task`, subagents, and parallel workers**\\n\\n**1. User’s objective**\\nThe user (Ahmed, via Telegram) asked to self-host the Honcho memory provider for Hermes because he had no Honcho API key. This required researching the upstream `plastic-labs/honcho` repository and implementing a local Docker- or Python-based deployment on ZimaOS.\\n\\n**2. Subagent / `delegate_task` usage**\\nRather than performing all research inline, the assistant invoked `delegate_task` to spawn a synchronous subagent whose sole goal was to inspect the Honcho repo and extract the complete self-hosting specification. The subagent ran autonomously for ~61 seconds, executed 8 internal tool calls, and returned a structured summary covering:\\n- Required services (API, deriver, PostgreSQL/pgvector, Redis)\\n- Mandatory and optional environment variables (`.env.template`)\\n- Default ports (`8000`, `5432`, `6379`)\\n- LLM provider requirements (external key needed even for self-hosting)\\n- Quick-start commands and file layout\\n\\n**3. Parallel/multi-agent architecture referenced**\\nDuring the session the `hermes-agent` skill was loaded, which documents Hermes’ multi-worker systems:\\n- **`delegate_task`**: Synchronous child spawn; parent waits for a summary. In **batch mode** (`tasks=[...]`), children run in parallel up to `delegation.max_concurrent_children` (default 3). Roles are `leaf` (cannot re-delegate) or `orchestrator` (can spawn workers, bounded by `max_spawn_depth`). Not durable—parent interruption cancels the child.\\n- **Durable alternatives for true parallel/background work**:\\n  - `cronjob` (scheduled jobs with retry)\\n  - `terminal(background=True, notify_on_complete=True)` (fire-and-forget shell tasks)\\n  - **Kanban** (SQLite-backed multi-agent work queue with a dispatcher that claims tasks and spawns profiled workers)\\n\\n**4. Implementation outcomes and technical decisions**\\n- Based on the subagent’s findings, the assistant first attempted a full Docker Compose deployment (4 services). It created `/DATA/AppData/hermes/projects/honcho/` from the cloned repo and prepared `.env` with the user’s Ollama Cloud Pro key (`eaf9ec548a9f4f789cc2eeb0d7cc61e3.VI7OQTAxEEGB1fqyil_1oJob`) and `DB_CONNECTION_URI` pointing to the internal Docker network.\\n- **Docker blocked on ZimaOS**: Multiple failures forced abandonment of the Compose path:\\n  - User `az-a` was not in the `docker` group initially, causing `permission denied` on `/var/run/docker.sock`.\\n  - Read-only root filesystem prevented `sudo docker` from writing to `/root/.docker`.\\n  - Docker Compose v5 required **buildx 0.17+**, which was unavailable.\\n- **Pivot to hybrid native setup**: The assistant retained Postgres (`pgvector/pgvector:pg15`) and Redis (`redis:8.2`) as containers (already running on mapped ports `127.0.0.1:5433→5432` and `6380→6379`), then pivoted to installing Honcho natively via `uv` and Python 3.12.\\n\\n**5. Key commands, paths, and technical details**\\n- **Subagent specification**: `delegate_task` used model `deepseek-v4-pro`, completed in `61.37s`, consumed `61874` input / `2269` output tokens.\\n- **Repo clone path**: `/DATA/AppData/hermes/projects/honcho/`\\n- **Config/descriptor files read by subagent**:\\n  - `docker-compose.yml.example`\\n  - `.env.template`\\n  - `Dockerfile`\\n  - `CLAUDE.md` ( discovered as subdirectory context)\\n- **Terminal workaround for ZimaOS**:`sg docker -c \\\"...\\\"` and `DOCKER_CONFIG=/tmp/docker-config` were attempted to bypass permission and read-only filesystem issues.\\n- **Background process IDs spawned**: `proc_f2f1ea359e2f`, `proc_c001f308366a`, `proc_a43273d9a75e`, `proc_f72267717dfc`, `proc_27091f6f59dd` — all either exited with code 1 or disappeared due to host constraints.\\n\\n**6. Unresolved / notable**\\n- The transcript truncated before confirming whether native Honcho (API, deriver, and worker) were successfully started via `uv run fastapi dev` and `uv run python -m src.deriver`.\\n- A **separate gateway incident** was noted: `hermes-gateway.service` had entered a restart loop because `hermes_cli/codex_models.py` and ~17 related files were manually deleted from `/DATA/AppData/hermes-agent/`, causing `ModuleNotFoundError`. The user recovered with `git restore .` and `systemctl restart hermes-gateway.service`.\\n- Docker Compose was fully abandoned; the final plan was containers for data stores only, with Honcho application logic running natively under `uv`.\"}, {\"session_id\": \"20260511_150044_d89ceffa\", \"when\": \"May 11, 2026 at 03:00 PM\", \"source\": \"telegram\", \"model\": \"kimi-k2.6\", \"summary\": \"**Session Recap: 3D-Print Skill Creation Attempt (May 11, 2026)**\\n\\n**1. User Objective**\\nThe user requested a single, \\\"perfect\\\" 3D-design skill so they could describe objects and receive files printable 1:1 on their 3D printer. They instructed the assistant to research optimal LLM-to-3D-print workflows online, install tools if necessary (e.g., Blender), delete any existing 3D design skills, and take whatever time was needed.\\n\\n**2. Actions Taken and Outcomes**\\n- **Environment Audit:** The assistant checked for Blender, `bpy`, and OpenSCAD via `terminal` calls. None were found. A `search_files` returned zero existing skills.\\n- **Preliminary Research:** Using `execute_code`, the assistant scraped GitHub (for repos like `rnmourao/llm-3dprint-anything`), Reddit, and Hacker News to identify promising workflows. It identified OpenSCAD and CadQuery as the leading candidates.\\n- **Parallel Subagent Dispatch (`delegate_task`):** After discovering `web_search` was unavailable, the assistant invoked `delegate_task` to spin up three parallel subagents for deeper research. **All three subagents timed out after 600.0 seconds**:\\n  - Subagent 1: 7 API calls completed.\\n  - Subagent 2: 23 API calls completed.\\n  - Subagent 3: 19 API calls completed.\\n  - Total delegated duration: **600.15 seconds**. None returned usable summaries.\\n- **Fallback Direct Execution:** Following the subagent failures, the assistant switched to a direct approach and created a 5-step todo list (OpenSCAD install, CadQuery install, mesh validation, old skill cleanup, skill creation).\\n\\n**3. Key Decisions and Conclusions**\\n- **Pipeline Choice:** Based on the initial scraping results—particularly the `rnmourao/llm-3dprint-anything` repository README which described an end-to-end OpenSCAD → validators → PrusaSlicer → USB-serial workflow—the assistant decided the best stack was **OpenSCAD + CadQuery + trimesh/manifold**.\\n- **No Blender:** Blender was dropped from consideration in favor of a code-first CAD approach (OpenSCAD/CadQuery) after the research phase.\\n\\n**4. Important Technical Details**\\n- **System:** ZimaOS v1.6.1, x86_64. No standard package manager (`apt-get`, `pacman`, `apk`, `zypper`) available, but `pip`, `nix`, and AppImage worked.\\n- **Python Libraries:** CadQuery 2.7.0 and cadquery-ocp 7.8.1.1.post1 were already installed in the user Python 3.12 environment. The assistant additionally ran `pip install trimesh manifold3d pillow matplotlib`, successfully installing `trimesh 4.12.2` and `manifold3d 3.4.1`.\\n- **OpenSCAD AppImage:** Downloaded via curl (`OpenSCAD-2021.01-x86_64.AppImage`). Execution failed with:  \\n  `/tmp/.mount_openscKKtz2V/AppRun.wrapped: error while loading shared libraries: libGL.so.1: cannot open shared object file: No such file or directory` (exit code 127).\\n- **Sandbox Mismatch:** An `execute_code` attempt to import `cadquery` inside the sandbox failed because the sandbox used Python 3.11 (`/DATA/AppData/.local/share/uv/python/cpython-3.11.15-linux-x86_64-gnu`), whereas CadQuery and trimesh were installed in the Python 3.12 user site-packages (`/DATA/.local/lib/python3.12/site-packages`). `ENABLE_USER_SITE` was `False` in the sandbox.\\n- **Interrupt:** The session was interrupted by the user sending a new message during a pending terminal command.\\n\\n**5. Unresolved / Notable Issues**\\n- The three `delegate_task` subagents all timed out, yielding no research deliverables and forcing the assistant to proceed with limited preliminary data.\\n- The OpenSCAD AppImage was non-functional due to the missing `libGL.so.1` shared library.\\n- The CadQuery/trimesh toolchain was available in the user Python 3.12 environment but inaccessible from the sandboxed `execute_code` runtime (Python 3.11), complicating automated skill testing.\\n- No skill was actually created by the end of the transcript; the todo list remained partially in-progress.\"}, {\"session_id\": \"20260522_120546_e98931bf\", \"when\": \"May 22, 2026 at 12:05 PM\", \"source\": \"telegram\", \"model\": \"deepseek-v4-pro\", \"summary\": \"The user wanted to tag three German audiobook MP3s (*Schuld und Strafe*, *Karl Marx und der Fluch des Geldes*, *Maksym*) with Audible metadata and cover art, add them to Plex (later switched to Audiobookshelf), and edit the DailyDose PWA to remove a duplicate category and rename \\\"Work\\\" to \\\"YouTube\\\".\\n\\n**Parallel delegation attempts via `delegate_task` and external agents failed and were abandoned in favor of direct execution:**\\n\\n- **Audiobook metadata research:** The assistant launched two parallel `delegate_task` calls (model `deepseek-v4-pro`) to research the Karl Marx and Maksym audiobooks. Both subagents completed but returned only natural-language search suggestions (e.g. `<query>\\\"Karl Marx und der Fluch des Geldes\\\" Hörbuch</query>` and `<search term=\\\"Maksym Hörbuch deutsch Audible\\\">`) without executing any actual tool calls (`\\\"tool_trace\\\": []`). The assistant concluded: *\\\"Die Subagents haben nur Suchbegriffe generiert, nicht tatsächlich gesucht. Ich mach die Recherchen selbst\\\"*, and switched to direct `terminal` calls.\\n- **Multitool query:** A later `delegate_task` (22.5s, deepseek-v4-pro) for the *bestes Multitool* question returned an empty `<maketool_calls>` block with no actionable output; the assistant answered from internal knowledge.\\n- **DailyDose patch via CCR:** The assistant attempted to delegate the HTML patch to the Claude Code Router (`ccr`). This failed immediately because Node.js was absent on the ZimaOS host. `execute_code` returned `/usr/bin/env: 'node': No such file or directory`, and `terminal` checks confirmed `node: command not found` in PATH `/usr/bin:/usr/sbin:/DATA/bin...`. The assistant reverted to direct `patch` edits.\\n\\n**What succeeded with direct execution:**\\n\\n- **Metadata retrieval:** Direct `terminal` commands via the `audible` CLI replaced the subagents: `audible search \\\"Karl Marx und der Fluch des Geldes\\\" --num-results 1` returned ASIN `B07DDPBX36` (79 min, Audiolino OHG); `audible search \\\"Maksym\\\" --num-results 3` returned ASIN `B0B3JJ99FM` (527 min, Lübbe Audio). Audible API calls directly fetched *Verbrechen und Strafe* (ASIN `B0DQPGQ5VN`, 1476 min, Der Audio Verlag, narrated by Sylvester Groth).\\n- **Tagging script:** A Python script was written to `/DATA/AppData/hermes/work/tag_audiobooks.py`, using `mutagen` to embed ID3 tags (TIT2, TPE1, TALB, TDRC, TCON, COMM, APIC cover images). The initial run crashed with `PermissionError: [Errno 13] Permission denied: '/media/HDD_1TB/Medien/Audiobooks/Schuld_und_Suehne_komplett.mp3'`; the assistant fixed ownership/permissions (`chown az-a:samba` / `chmod`) on the audiobook directory, then re-ran successfully. Cover JPEGs were downloaded and embedded: `Verbrechen_und_Strafe.jpg` (45K), `Karl_Marx_Fluch_des_Geldes.jpg` (62K), `Maksym.jpg` (46K).\\n- **Plex probe:** Plex was running in Docker container `084bb91c8404` (`lscr.io/linuxserver/plex:1.41.3`). The assistant found the Plex token (`PlexOnlineToken=\\\"UZBRFc2Y1psjzPNvnycU\\\"`), listed existing libraries (Filme, Serien, Musik, YouTube), and cloned the `Audnexus.bundle` agent into `/DATA/AppData/plex/config/Library/Application Support/Plex Media Server/Plug-ins/`, but ultimately recommended Audiobookshelf instead due to Plex's weak native audiobook API support. The user confirmed the switch to Audiobookshelf.\\n- **DailyDose edits:** The assistant used `execute_code` to remove the duplicate custom `Körperpflege` from the server-side `dd_custom_cats` storage via API at `localhost:8765` (HTTP 200 `{\\\"ok\\\":true}`). Then two `patch` edits were applied to `/media/HDD_1TB/DailyDose/index.html`: line 881 in `BUILT_IN_CATS` changed `Work` (icon `💼`) to `YouTube` (icon `▶️`), and line 892 updated `CAT_ALIAS` to `{Buildtech:'Hausarbeit', Essen:'Kochen', Beten:'Islam', Work:'YouTube'}`.\\n\\n**Key decisions and unresolved items:**\\n\\n- Delegation/subagent approaches were abandoned as ineffective on this environment; direct tool use proved faster and more reliable.\\n- Audiobook chapter splitting was discussed (options: real chapter lookup, silence detection via ffmpeg, M4B conversion) but explicitly cancelled by the user: *\\\"Ok vergiss das\\\"*.\\n- Node.js, npm, and `ccr` remained absent from the system, blocking all future Claude Code CLI delegation attempts.\"}, {\"session_id\": \"20260523_091814_a3ce45e8\", \"when\": \"May 23, 2026 at 09:18 AM\", \"source\": \"telegram\", \"model\": \"deepseek-v4-pro\", \"summary\": \"**Factual Recap — May 23, 2026 at 09:18 AM (Telegram)**\\n\\n*Note: The conversation did not explicitly discuss \\\"subagent\\\" or \\\"parallel workers\\\" as architectural concepts. However, **task delegation** — both to the assistant and to background automated jobs — was the dominant operating pattern.*\\n\\n### 1. What the user asked about or wanted to accomplish\\n\\nThe user first requested a learning-progress check in the **Zeitgeist App**, then established a daily learning target with a reward contingency, and finally **delegated the full conceptualization and construction of a web application** (\\\"CallKeep\\\" / \\\"FriendPulse\\\") to the assistant, to be deployed as a Docker container on the ZimaOS server.\\n\\nSpecific deliverables requested:\\n- **Hourly learning reminders:** Track progress toward a 150-minute daily learning goal in Zeitgeist; if achieved, the user earns a headphone purchase.\\n- **CallKeep app:** A contact-relationship tracker to log phone calls (who, when, duration, topics, follow-ups), with a polished web UI and standardized input fields (contact names, call frequency).\\n\\n### 2. What actions were taken and what the outcomes were\\n\\n**Delegated monitoring task (background cronjob):**\\n- The assistant created a recurring cronjob (`job_id: 802d68c923d8`, name: *\\\"Lernziel 150min — Kopfhörer\\\"*) scheduled at `0 * * * *` for 16 repetitions (hourly until 23:00).\\n- The cronjob is configured to autonomously query the Zeitgeist backend (`curl -s http://localhost:8765/api/data`), compare accumulated minutes against the 150-minute target, and report remaining time back to the user via Telegram.\\n\\n**Data retrieval and reporting (assistant as delegated worker):**\\n- Queried the Zeitgeist JSON store via `terminal` and `execute_code` to extract:\\n  - **May 22 (yesterday):** 114 minutes (1.9 h) across five sessions (25+25+25+25+14 min).\\n  - **May 23 (today, at time of check):** 0 minutes tracked.\\n- Provided a 7-day learning history table (4.1 h weekly total).\\n\\n**App concept delegation (CallKeep):**\\n- The assistant produced a full concept document on request: app name options (**CallKeep** or **FriendPulse**), tagline, data model (contacts + calls), dashboard mockup, and proposed workflows.\\n- Upon the user's follow-up command (\\\"*Bau mal diese App*\\\"), the assistant immediately shifted to execution mode:\\n  - Loaded the `zimaos-web-app` skill (`/DATA/.hermes/skills/software-development/zimaos-web-app/SKILL.md`) to follow the standardized ZimaOS deployment pattern (FastAPI + SQLite + vanilla HTML/CSS/JS glass-morphism UI, CasaOS Docker labels, `x-casaos` or `docker run` integration).\\n  - Created a structured todo list (8 items) to manage build tasks: project directory creation, `main.py` backend, Dockerfile, `frontend/index.html` SPA, icon, Docker image build, container deployment, and CasaOS dashboard registration.\\n  - Created the project directory at `/media/HDD_1TB/CallKeep/`.\\n  - Checked port availability (`ss -tlnp | grep 300`) to determine a free port for the new container.\\n\\n### 3. Key decisions, solutions found, or conclusions reached\\n\\n- **Autonomous background delegation accepted:** The user approved delegating hourly status checks entirely to a cronjob rather than requiring manual interaction each hour.\\n- **App architecture decided:** Single-container FastAPI backend with embedded frontend; Dockerized on ZimaOS; data persisted on HDD (`/media/HDD_1TB/CallKeep/`); standardized inputs for contact names and frequency categories.\\n- **No authentication required:** Unless explicitly requested later, per the `zimaos-web-app` skill rules.\\n- **UI style:** Default blue glass-morphism system-aware theme (light/dark), unless user requests Stripe-Link or Apple-minimal variant.\\n\\n### 4. Specific commands, files, URLs, or technical details\\n\\n- **Zeitgeist API endpoint:** `http://localhost:8765/api/data` (serves keys including `zeitgeist-v2`, `zeitgeist-v2-notes`, `zeitgeist-anki-v2`).\\n- **Cronjob:** `802d68c923d8`, schedule `0 * * * *`, 16 repeats, deliver to origin (Telegram). Enabled toolset: `terminal`. Prompt preview references automated curl to the Zeitgeist API.\\n- **Yesterday's tracked data (May 22):** DailyDose category \\\"Lernen\\\": 1.9 h; Zeitgeist slots: `[25, 25, 25, 25, 14]` minutes, no topic labels set.\\n- **Project path:** `/media/HDD_1TB/CallKeep/` (created via `mkdir -p`).\\n- **Port check:** `ss -tlnp | grep 300` returned listeners on `:3000` and `:30000`; 3001–range still available (PrintVault skill notes indicate 3001 may be in use by PrintVault, but scan did not show it bound at that moment).\\n- **Skill reference:** `zimaos-web-app` — includes critical CasaOS deployment notes (CSP iframe headers, `casaos.*` labels, `hostname=\\\"\\\"` for VPN compatibility, `x-casaos` compose blocks vs. `docker run` patterns, icon path `http://<SERVER_IP>:<port>/icon.svg`).\\n\\n### 5. Anything left unresolved or notable\\n\\n- **App build incomplete:** The transcript truncates while the todo list shows item 1 (*Create project structure*) in progress and items 2–8 pending. No `main.py`, Dockerfile, or frontend files had been written yet by the cutoff point.\\n- **Port not finalized:** The assistant checked ports but had not yet assigned or exposed a definitive port for CallKeep in the truncated remainder.\\n- **No explicit discussion of multi-agent or parallel-worker architectures:** All delegation in this session was sequential (assistant executing steps one-by-one, plus a single background cronjob). No subagent spawning, parallel LLM workers, or distributed task queues were mentioned.\"}], \"count\": 5, \"sessions_searched\": 5}", "tool_call_id": "call_44myfd0e", "timestamp": "2026-05-24T09:10:41.237199"}
{"role": "assistant", "content": "Ja, mehrfach — mit gemischten Ergebnissen:\n\n**Funktioniert hat's:**\n- 21. Mai — Honcho-Selbsthosting-Recherche: Subagent lief 61s, 8 interne Tool-Calls, lieferte eine saubere Zusammenfassung der Deployment-Specs zurück\n- 20. Mai — PrintVault Trash-UI: Allerdings nicht via `delegate_task`, sondern als Workaround über `terminal(background=true)` mit CCR\n\n**Gescheitert ist's:**\n- 11. Mai — 3 parallele Subagents für 3D-Druck-Recherche: alle drei nach 600s Timeout abgebrochen\n- 20. Mai — `delegate_task(acp_command='claude')` schlug fehl wegen fehlendem PATH zur Claude-CLI\n- 22. Mai — 2 parallele Subagents für Hörbuch-Recherche: lieferten nur Suchbegriffe zurück, keine echten Ergebnisse\n\n**Fazit:** Das native `delegate_task` ist hit-or-miss. Der Workaround über `terminal(background=true)` + CCR funktioniert zuverlässiger, ist aber fummeliger einzurichten. Der PATH-Fix für `acp_command` steht immer noch aus.", "reasoning": "The user asked if we've used subagents before. Let me summarize the findings from session_search.\n\nKey findings:\n1. May 11 - 3 parallel subagents dispatched via delegate_task, but ALL THREE timed out after 600s. Failed.\n2. May 20 - delegate_task with acp_command='claude' failed (PATH issue). But background terminal subagent worked to implement PrintVault Trash UI feature.\n3. May 21 - delegate_task used to research Honcho self-hosting - worked (61s, 8 internal calls, returned structured summary).\n4. May 22 - Two parallel delegate_task calls for audiobook research returned only search suggestions without actual tool calls. Abandoned in favor of direct execution.\n5. May 23 - No subagent usage, just sequential execution + cronjob.\n\nSo yes, we've used it several times with mixed results. Let me give a concise summary.", "finish_reason": "stop", "reasoning_content": "The user asked if we've used subagents before. Let me summarize the findings from session_search.\n\nKey findings:\n1. May 11 - 3 parallel subagents dispatched via delegate_task, but ALL THREE timed out after 600s. Failed.\n2. May 20 - delegate_task with acp_command='claude' failed (PATH issue). But background terminal subagent worked to implement PrintVault Trash UI feature.\n3. May 21 - delegate_task used to research Honcho self-hosting - worked (61s, 8 internal calls, returned structured summary).\n4. May 22 - Two parallel delegate_task calls for audiobook research returned only search suggestions without actual tool calls. Abandoned in favor of direct execution.\n5. May 23 - No subagent usage, just sequential execution + cronjob.\n\nSo yes, we've used it several times with mixed results. Let me give a concise summary.", "timestamp": "2026-05-24T09:10:41.237199"}