clarify

Ask the user a question when you need clarification, feedback, or a decision before proceeding. Supports two modes:

1. **Multiple choice** — provide up to 4 choices. The user picks one or types their own answer via a 5th 'Other' option.
2. **Open-ended** — omit choices entirely. The user types a free-form response.

Use this tool when:
- The task is ambiguous and you need the user to choose an approach
- You want post-task feedback ('How did that work out?')
- You want to offer to save a skill or update memory
- A decision has meaningful trade-offs the user should weigh in on

Do 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.

The question to present to the user.

Choices:
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.

cronjob

Manage scheduled cron jobs with a single compressed tool.

Use action='create' to schedule a new job from a prompt or one or more skills.
Use action='list' to inspect jobs.
Use action='update', 'pause', 'resume', 'remove', or 'run' to manage an existing job.

To 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.

Jobs run in a fresh session with no current-chat context, so prompts must be self-contained.
If skills are provided on create, the future cron run loads those skills in order, then follows the prompt as the task instruction.
On update, passing skills=[] clears attached skills.

NOTE: The agent's final response is auto-delivered to the target. Put the primary
user-facing content in the final response. Cron jobs run autonomously with no user
present — they cannot ask questions or request clarification.

Important safety rule: cron-run sessions should not recursively schedule more cron jobs.

One of: create, list, update, pause, resume, remove, run

Required for update/pause/resume/remove/run

For create: the full self-contained prompt. If skills are also provided, this becomes the task instruction paired with those skills.

For create/update: '30m', 'every 2h', '0 9 * * *', or ISO timestamp

Optional human-friendly name

Optional repeat count. Omit for defaults (once for one-shot, forever for recurring).

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.

Optional ordered list of skill names to load before executing the cron prompt. On update, pass an empty array to clear attached skills.

Optional per-job model override. If provider is omitted, the current main provider is pinned at creation time so the job stays stable.

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 name (e.g. 'anthropic/claude-sonnet-4', 'claude-sonnet-4')

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.

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.

REQUIREMENTS when True: ``script`` MUST be set (``prompt`` and ``skills`` are ignored).

DELIVERY 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.

WHEN 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.

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.

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.

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.

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.

TWO MODES (one of 'goal' or 'tasks' is required):
1. Single task: provide 'goal' (+ optional context, toolsets)
2. 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.

WHEN TO USE delegate_task:
- Reasoning-heavy subtasks (debugging, code review, research synthesis)
- Tasks that would flood your context with intermediate data
- Parallel independent workstreams (research A and B simultaneously)

WHEN NOT TO USE (use these instead):
- Mechanical multi-step work with no reasoning needed -> use execute_code
- Single tool call -> just call the tool directly
- Tasks needing user interaction -> subagents cannot use clarify
- 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.

IMPORTANT:
- Subagents have NO memory of your conversation. Pass all relevant info (file paths, error messages, constraints) via the 'context' field.
- 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.
- 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.
- Leaf subagents (role='leaf', the default) CANNOT call: delegate_task, clarify, memory, send_message, execute_code.
- 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.
- Each subagent gets its own terminal session (separate working directory and state).
- Results are always returned as an array, one entry per task.

What the subagent should accomplish. Be specific and self-contained -- the subagent knows nothing about your conversation history.

Background information the subagent needs: file paths, error messages, project structure, constraints. The more specific you are, the better the subagent performs.

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.

Task goal

Task-specific context

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.

Per-task ACP command override (e.g. 'claude'). Overrides the top-level acp_command for this task only.

Per-task ACP args override.

Per-task role override. See top-level 'role' for semantics.

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 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.

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.

Arguments for the ACP command (default: ['--acp', '--stdio']). Only used when acp_command is set. Example: ['--acp', '--stdio', '--model', 'claude-opus-4-6']

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).

Use 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.

Available via `from hermes_tools import ...`:

  read_file(path: str, offset: int = 1, limit: int = 500) -> dict
    Lines are 1-indexed. Returns {"content": "...", "total_lines": N}
  write_file(path: str, content: str) -> dict
    Always overwrites the entire file.
  search_files(pattern: str, target="content", path=".", file_glob=None, limit=50) -> dict
    target: "content" (search inside files) or "files" (find files by name). Returns {"matches": [...]}
  patch(path: str, old_string: str, new_string: str, replace_all: bool = False) -> dict
    Replaces old_string with new_string in the file.
  terminal(command: str, timeout=None, workdir=None) -> dict
    Foreground only (no background/pty). Returns {"output": "...", "exit_code": N}

Limits: 5-minute timeout, 50KB stdout cap, max 50 tool calls per script. terminal() is foreground-only (no background or pty).

Scripts 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().

Print your final result to stdout. Use Python stdlib (json, re, math, csv, datetime, collections, etc.) for processing between tool calls.

Also available (no import needed — built into hermes_tools):
  json_parse(text: str) — json.loads with strict=False; use for terminal() output with control chars
  shell_quote(s: str) — shlex.quote(); use when interpolating dynamic strings into shell commands
  retry(fn, max_attempts=3, delay=2) — retry with exponential backoff for transient failures

Python code to execute. Import tools with `from hermes_tools import terminal, ...` and print your final result to stdout.

memory

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.

WHEN TO SAVE (do this proactively, don't wait to be asked):
- User corrects you or says 'remember this' / 'don't do that again'
- User shares a preference, habit, or personal detail (name, role, timezone, coding style)
- You discover something about the environment (OS, installed tools, project structure)
- You learn a convention, API quirk, or workflow specific to this user's setup
- You identify a stable fact that will be useful again in future sessions

PRIORITY: User preferences and corrections > environment facts > procedural knowledge. The most valuable memory prevents the user from having to repeat themselves.

Do NOT save task progress, session outcomes, completed-work logs, or temporary TODO state to memory; use session_search to recall those from past transcripts.
If 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.

TWO TARGETS:
- 'user': who the user is -- name, role, preferences, communication style, pet peeves
- 'memory': your notes -- environment facts, project conventions, tool quirks, lessons learned

ACTIONS: add (new entry), replace (update existing -- old_text identifies it), remove (delete -- old_text identifies it).

SKIP: trivial/obvious info, things easily re-discovered, raw data dumps, and temporary task state.

The action to perform.

Which memory store: 'memory' for personal notes, 'user' for user profile.

The entry content. Required for 'add' and 'replace'.

Short unique substring identifying the entry to replace or remove.

patch

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.

Replace mode (default): find a unique string and replace it.
Patch mode: apply V4A multi-file patches for bulk changes.

Edit mode: 'replace' for targeted find-and-replace, 'patch' for V4A multi-file patches

File path to edit (required for 'replace' mode)

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.

Replacement text (required for 'replace' mode). Can be empty string to delete the matched text.

Replace all occurrences instead of requiring a unique match (default: false)

V4A format patch content (required for 'patch' mode). Format:
*** Begin Patch
*** Update File: path/to/file
@@ context hint @@
 context line
-removed line
+added line
*** End Patch

process

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).

list poll log wait kill write submit close

Action to perform on background processes

Process session ID (from terminal background output). Required for all actions except 'list'.

Text to send to process stdin (for 'write' and 'submit' actions)

Max seconds to block for 'wait' action. Returns partial output on timeout.

Line offset for 'log' action (default: last 200 lines)

Max lines to return for 'log' action

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.

Path to the file to read (absolute, relative, or ~/path)

Line number to start reading from (1-indexed, default: 1)

Maximum number of lines to read (default: 500, max: 2000)

Search file contents or find files by name. Use this instead of grep/rg/find/ls in terminal. Ripgrep-backed, faster than shell equivalents.

Content search (target='content'): Regex search inside files. Output modes: full matches with line numbers, file paths only, or match counts.

File search (target='files'): Find files by glob pattern (e.g., '*.py', '*config*'). Also use this instead of ls — results sorted by modification time.

Regex pattern for content search, or glob pattern (e.g., '*.py') for file search

'content' searches inside file contents, 'files' searches for files by name

Directory or file to search in (default: current working directory)

Filter files by pattern in grep mode (e.g., '*.py' to only search Python files)

Maximum number of results to return (default: 50)

Skip first N results for pagination (default: 0)

Output format for grep mode: 'content' shows matching lines with line numbers, 'files_only' lists file paths, 'count' shows match counts per file

Number of context lines before and after each match (grep mode only)

Send a message to a connected messaging platform, or list available targets.

IMPORTANT: 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.
If the user just says a platform name like 'send to telegram', send directly to the home channel without listing first.

Action to perform. 'send' (default) sends a message. 'list' returns all available channels/contacts across connected platforms.

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)

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.

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.

TWO MODES:
1. 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.
2. Keyword search (with query): Search for specific topics across all past sessions. Returns LLM-generated summaries of matching sessions.

USE THIS PROACTIVELY when:
- The user says 'we did this before', 'remember when', 'last time', 'as I mentioned'
- The user asks about a topic you worked on before but don't have in current context
- The user references a project, person, or concept that seems familiar but isn't in memory
- You want to check if you've solved a similar problem before
- The user asks 'what did we do about X?' or 'how did we fix Y?'

Don'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.

Search 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.

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).

Optional: only search messages from specific roles (comma-separated). E.g. 'user,assistant' to skip tool outputs.

Max sessions to summarize (default: 3, max: 5).

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.

Actions: 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.

On 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.

Create when: complex task succeeded (5+ calls), errors overcome, user-corrected approach worked, non-trivial workflow discovered, or user asks you to remember a procedure.
Update 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.

After difficult/iterative tasks, offer to save as a skill. Skip for simple one-offs. Confirm with user before creating/deleting.

Good skills: trigger conditions, numbered steps with exact commands, pitfalls section, verification steps. Use skill_view() to see format examples.

Pinned 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.

create patch edit delete write_file remove_file

The action to perform.

Skill name (lowercase, hyphens/underscores, max 64 chars). Must match an existing skill for patch/edit/delete/write_file/remove_file.

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.

Text to find in the file (required for 'patch'). Must be unique unless replace_all=true. Include enough surrounding context to ensure uniqueness.

Replacement text (required for 'patch'). Can be empty string to delete the matched text.

For 'patch': replace all occurrences instead of requiring a unique match (default: false).

Optional category/domain for organizing the skill (e.g., 'devops', 'data-science', 'mlops'). Creates a subdirectory grouping. Only used with 'create'.

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.

Content for the file. Required for 'write_file'.

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.

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.

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').

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.

List available skills (name + description). Use skill_view(name) to load full content.

Optional category filter to narrow results

terminal

Execute shell commands on a Linux environment. Filesystem usually persists between calls.

Do NOT use cat/head/tail to read files — use read_file instead.
Do NOT use grep/rg/find to search — use search_files instead.
Do NOT use ls to list directories — use search_files(target='files') instead.
Do NOT use sed/awk to edit files — use patch instead.
Do NOT use echo/cat heredoc to create files — use write_file instead.
Reserve terminal for: builds, installs, git, processes, scripts, network, package managers, and anything that needs a shell.

Foreground (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.
Background: Set background=true to get a session_id. Two patterns:
  (1) Long-lived processes that never exit (servers, watchers).
  (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.
For 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.
After starting a server, verify readiness with a health check or log signal, then run tests in a separate terminal() call. Avoid blind sleep loops.
Use process(action="poll") for progress checks, process(action="wait") to block until done.
Working directory: Use 'workdir' for per-command cwd.
PTY mode: Set pty=true for interactive CLI tools (Codex, Claude Code, Python REPL).

Do NOT use vim/nano/interactive tools without pty=true — they hang without a pseudo-terminal. Pipe git output to cat if it might page.

The command to execute on the VM

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.

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.

Working directory for this command (absolute path). Defaults to the session working directory.

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.

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.

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.

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.

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.

Optional custom file path to save the audio. Defaults to ~/.hermes/audio_cache/<timestamp>.mp3

todo

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.

Writing:
- Provide 'todos' array to create/update items
- merge=false (default): replace the entire list with a fresh plan
- merge=true: update existing items by id, add any new ones

Each item: {id: string, content: string, status: pending|in_progress|completed|cancelled}
List order is priority. Only ONE item in_progress at a time.
Mark items completed immediately when done. If something fails, cancel it and add a revised item.

Always returns the full current list.

Task items to write. Omit to read current list.

Unique item identifier

Task description

Current status

true: update existing items by id, add new ones. false (default): replace the entire list.

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.

Image URL (http/https) or local file path to analyze.

Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question.

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).

Path to the file to write (will be created if it doesn't exist, overwritten if it does)

Complete content to write to the file


Hallo Kiwi


Hallo Ahmed! 👋

Alles klar bei dir? Was haben wir heute auf dem Zettel?

Explanation:
The user greeted me with "Hallo Kiwi". Looking at my memory, there's a very important note:

"User is Ahmed Zeyd Aytac. He refers to me as 'Kiwi' and explicitly corrected me when I mistakenly called him 'Kiwi' — that is not his name."

So the user (Ahmed Zeyd Aytac) is calling ME "Kiwi". That's my nickname/what he calls me. I should respond naturally to this greeting.

I should say hello back. I don't need to load any skills for a simple greeting.


Wie ist das Wetter heute?


Für welchen Ort soll ich das Wetter raussuchen? Ich habe keinen Standort von dir gespeichert.

Sag mir einfach die Stadt, dann kann ich dir die aktuelle Vorhersage holen! 🌤

Explanation:
Der Nutzer fragt nach dem Wetter. Ich habe kein direktes Tool für Wetterabfragen, aber ich könnte:
1. Web-Suche nutzen, um aktuelle Wetterdaten zu finden
2. Den Nutzer fragen, wo er ist (da ich seinen Standort nicht kenne)
3. Nach seinem Standort in der memory suchen

Lass mich in der memory nachsehen, ob ich seinen Standort kenne.

Aus der memory sehe ich:
- Er hat eine Bambu Lab 3D-Drucker
- Project directories auf ZimaOS
- Kein expliziter Standort/Wohnort gespeichert

Ich sollte ihn fragen, für welchen Ort er das Wetter wissen möchte, oder eine allgemeine Wetterabfrage für Deutschland machen (da er Deutsch spricht). Da ich keinen spezifischen Standort habe, frage ich am besten nach dem Ort.

Warte - ich habe kein web_search Tool. Lass mich die verfügbaren Tools checken... Ich habe tatsächlich kein web_search in meiner Tool-Liste. Die verfügbaren Tools sind: clarify, cronjob, delegate_task, execute_code, memory, patch, read_file, search_files, send_message, skill_manage, skill_view, skills_list, terminal, text_to_speech, todo, vision_analyze, write_file, process, session_search.

Ich könnte execute_code oder terminal nutzen, um eine Wetter-API oder curl zu verwenden. Aber das ist etwas umständlich. Ehrlicherweise sollte ich sagen, dass ich keinen direkten Wetterdienst habe und ihn nach seinem Standort fragen, damit ich helfen kann - oder vorschlagen, dass er einen spezifischen Dienst nutzt.

Aber warte - ich kann auch versuchen, über terminal mit curl eine Wetter-API abzufragen, z.B. wttr.in, die sehr zuverlässig ist und keinen API-Key braucht.

Aber ich kenne seinen Standort nicht. Ich sollte ihn fragen.


Mein Standort in Wien, bitte speichern!


Explanation: Der Nutzer möchte, dass ich seinen Standort "Wien" speichere. Ich sollte dies in der user-profile memory speichern.

memory

User's location is Wien (Vienna), Austria. When asked about weather, use this location by default.


User is a maker with a Bambu Lab 3D printer. Existing 3D projects: Gridfinity organizers, Werkzeugkisten, Meshtastic enclosures. Project directories: /DATA/AppData/hermes/Projekte/ and /DATA/AppData/hermes/work/. When asked "what should we build?", user prefers 3D design/CAD projects over general software dev.

User is Ahmed Zeyd Aytac. He refers to me as 'Kiwi' and explicitly corrected me when I mistakenly called him 'Kiwi' — that is not his name.

User's location is Wien (Vienna), Austria. When asked about weather, use this location by default.

Entry added.


Top, **Wien** ist gespeichert! 🎯

Leider habe ich gerade kein direktes Wetter-Tool an Bord. Ich kann dir aber schnell helfen, die aktuelle Vorhersage zu holen – sag mir einfach, welche Quelle du bevorzugst:

- [wetter.at](https://www.wetter.at/wetter/oesterreich/wien) (AT-Standard)
- [wetter.com](https://www.wetter.de/oesterreich/wien.html)
- Oder soll ich dir ein kleines Script schreiben, das z.B. Open-Meteo oder eine API abfragt?

Was passt dir am besten?

Explanation:
Standort ist gespeichert! Wien ist notiert – ab sofort weiß ich Bescheid, wenn du nach dem Wetter fragst.

Leider habe ich kein direktes Wetter-Tool zur Hand, aber ich kann dir helfen, das Wetter online nachzuschlagen oder einen kurzen Befehl zu schreiben. Möchtest du, dass ich das für dich mache?

Oder hast du eine bevorzugte Wetterquelle (z.B. wetter.at, accuweather.com), die ich dir verlinke?


Ich glaube du hast bereits so einen skill


Explanation:
Der User sagt, ich habe bereits einen Skill für Wetter. Lass mich die Skills-Liste prüfen. Ich sehe in der available_skills keinen expliziten Wetter-Skill, aber es könnte sein, dass der User einen Skill meint, den ich übersehen habe, oder vielleicht meint er einen Web-Search / Browser Skill.

Lass mich nochmal genau schauen: Die Skills sind:
- 3d-printing
- autonomous-ai-agents
- creative
- data-science
- devops
- dogfood
- email
- gaming
- github
- mcp
- media
- mlops
- note-taking
- productivity
- red-teaming
- research
- smart-home
- social-media
- software-development
- yuanbao

Kein expliziter "weather" Skill. Aber vielleicht meint er den "web" oder "search" Toolset? Oder es gibt einen Skill, den ich nicht sehe? Die skills_list könnte mehr zeigen.

Warte - der User könnte auch einen custom Skill meinen, den er selbst erstellt hat. Ich sollte skills_list aufrufen, um alle verfügbaren Skills zu sehen.


dogfood

Exploratory QA of web apps: find bugs, evidence, reports.

yuanbao

Yuanbao (元宝) groups: @mention users, query info/members.

Vollständiger Workflow zum Erstellen druckbarer 3D-Modelle aus Textbeschreibungen. Nutzt Python (trimesh + manifold3d) als primären CAD-Engine. Generiert validierte STL/3MF-Dateien für Bambu Lab Drucker.

claude-code

Delegate coding to Claude Code CLI (features, PRs).

codex

Delegate coding to OpenAI Codex CLI (features, PRs).

hermes-agent

Configure, extend, or contribute to Hermes Agent.

opencode

Delegate coding to OpenCode CLI (features, PR review).

architecture-diagram

Dark-themed SVG architecture/cloud/infra diagrams as HTML.

ascii-art

ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.

ascii-video

ASCII video: convert video/audio to colored ASCII MP4/GIF.

baoyu-comic

Knowledge comics (知识漫画): educational, biography, tutorial.

baoyu-infographic

Infographics: 21 layouts x 21 styles (信息图, 可视化).

claude-design

Design one-off HTML artifacts (landing, deck, prototype).

comfyui

Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.

design-md

Author/validate/export Google's DESIGN.md token spec files.

excalidraw

Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).

humanizer

Humanize text: strip AI-isms and add real voice.

ideation

Generate project ideas via creative constraints.

manim-video

Manim CE animations: 3Blue1Brown math/algo videos.

p5.js sketches: gen art, shaders, interactive, 3D.

pixel-art

Pixel art w/ era palettes (NES, Game Boy, PICO-8).

popular-web-designs

54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.

pretext

Use when building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.

sketch

Throwaway HTML mockups: 2-3 design variants to compare.

songwriting-and-ai-music

Songwriting craft and Suno AI music prompts.

touchdesigner-mcp

Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.

jupyter-live-kernel

Iterative Python via live Jupyter kernel (hamelnb).

kanban-orchestrator

Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.

kanban-worker

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

webhook-subscriptions

Webhook subscriptions: event-driven agent runs.

himalaya

Himalaya CLI: IMAP/SMTP email from terminal.

minecraft-modpack-server

Host modded Minecraft servers (CurseForge, Modrinth).

pokemon-player

Play Pokemon via headless emulator + RAM reads.

codebase-inspection

Inspect codebases w/ pygount: LOC, languages, ratios.

github-auth

GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.

github-code-review

Review PRs: diffs, inline comments via gh or REST.

github-issues

Create, triage, label, assign GitHub issues via gh or REST.

github-pr-workflow

GitHub PR lifecycle: branch, commit, open, CI, merge.

github-repo-management

Clone/create/fork repos; manage remotes, releases.

native-mcp

MCP client: connect servers, register tools (stdio/HTTP).

gif-search

Search/download GIFs from Tenor via curl + jq.

heartmula

HeartMuLa: Suno-like song generation from lyrics + tags.

songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

spotify

Spotify: play, search, queue, manage playlists and devices.

youtube-content

YouTube transcripts to summaries, threads, blogs.

audiocraft-audio-generation

AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.

axolotl

Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).

dspy

DSPy: declarative LM programs, auto-optimize prompts, RAG.

evaluating-llms-harness

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

fine-tuning-with-trl

TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.

huggingface-hub

HuggingFace hf CLI: search/download/upload models, datasets.

llama-cpp

llama.cpp local GGUF inference + HF Hub model discovery.

obliteratus

OBLITERATUS: abliterate LLM refusals (diff-in-means).

outlines

Outlines: structured JSON/regex/Pydantic LLM generation.

segment-anything-model

SAM: zero-shot image segmentation via points, boxes, masks.

serving-llms-vllm

vLLM: high-throughput LLM serving, OpenAI API, quantization.

unsloth

Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.

weights-and-biases

W&B: log ML experiments, sweeps, model registry, dashboards.

obsidian

Read, search, create, and edit notes in the Obsidian vault.

airtable

Airtable REST API via curl. Records CRUD, filters, upserts.

docmaster-access

Direkter Zugriff auf DocMaster-Dokumente auf ZimaOS — API-Infos, Credentials und Dateisystem-Pfade.

google-workspace

Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.

linear

Linear: manage issues, projects, teams via GraphQL + curl.

maps

Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.

nano-pdf

Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).

notion

Notion API via curl: pages, databases, blocks, search.

ocr-and-documents

Extract text from PDFs/scans (pymupdf, marker-pdf).

powerpoint

Create, read, edit .pptx decks, slides, notes, templates.

godmode

Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

arxiv

Search arXiv papers by keyword, author, category, or ID.

blogwatcher

Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.

llm-wiki

Karpathy's LLM Wiki: build/query interlinked markdown KB.

polymarket

Query Polymarket: markets, prices, orderbooks, history.

research-paper-writing

Write ML papers for NeurIPS/ICML/ICLR: design→submit.

openhue

Control Philips Hue lights, scenes, rooms via OpenHue CLI.

xurl

X/Twitter via xurl CLI: post, search, DM, media, v2 API.

debugging-hermes-tui-commands

Debug Hermes TUI slash commands: Python, gateway, Ink UI.

hermes-agent-skill-authoring

Author in-repo SKILL.md: frontmatter, validator, structure.

node-inspect-debugger

Debug Node.js via --inspect + Chrome DevTools Protocol CLI.

plan

Plan mode: write markdown plan to .hermes/plans/, no exec.

python-debugpy

Debug Python: pdb REPL + debugpy remote (DAP).

requesting-code-review

Pre-commit review: security scan, quality gates, auto-fix.

spike

Throwaway experiments to validate an idea before build.

subagent-driven-development

Execute plans via delegate_task subagents (2-stage review).

systematic-debugging

4-phase root cause debugging: understand bugs before fixing.

test-driven-development

TDD: enforce RED-GREEN-REFACTOR, tests before code.

writing-plans

Write implementation plans: bite-sized tasks, paths, code.

Use skill_view(name) to see full content, tags, and linked files


Du hast recht, ich habe den **maps** Skill! Lass mich mal reinschauen, was der genau kann:

Explanation: Du hast recht, ich habe einen "maps" Skill! Der beschreibt sich als: "Geocode, POIs, routes, timezones via OpenStreetMap/OSRM." – aber da steht nicht explizit Wetter dabei. Lass mich den Skill trotzdem laden, um zu sehen, was genau drin steht. Vielleicht gibt es Wetter-APIs oder er erwähnt Nominatim/OSRM nur als Beispiele. Ich lade den Skill mal.


maps

---
name: maps
description: "Geocode, POIs, routes, timezones via OpenStreetMap/OSRM."
version: 1.2.0
author: Mibayy
license: MIT
metadata:
  hermes:
    tags: [maps, geocoding, places, routing, distance, directions, nearby, location, openstreetmap, nominatim, overpass, osrm]
    category: productivity
    requires_toolsets: [terminal]
    supersedes: [find-nearby]
---

# Maps Skill

Location intelligence using free, open data sources. 8 commands, 44 POI
categories, zero dependencies (Python stdlib only), no API key required.

Data sources: OpenStreetMap/Nominatim, Overpass API, OSRM, TimeAPI.io.

This skill supersedes the old `find-nearby` skill — all of find-nearby's
functionality is covered by the `nearby` command below, with the same
`--near "<place>"` shortcut and multi-category support.

## When to Use

- User sends a Telegram location pin (latitude/longitude in the message) → `nearby`
- User wants coordinates for a place name → `search`
- User has coordinates and wants the address → `reverse`
- User asks for nearby restaurants, hospitals, pharmacies, hotels, etc. → `nearby`
- User wants driving/walking/cycling distance or travel time → `distance`
- User wants turn-by-turn directions between two places → `directions`
- User wants timezone information for a location → `timezone`
- User wants to search for POIs within a geographic area → `area` + `bbox`

## Prerequisites

Python 3.8+ (stdlib only — no pip installs needed).

Script path: `~/.hermes/skills/maps/scripts/maps_client.py`

## Commands

```bash
MAPS=~/.hermes/skills/maps/scripts/maps_client.py
```

### search — Geocode a place name

```bash
python3 $MAPS search "Eiffel Tower"
python3 $MAPS search "1600 Pennsylvania Ave, Washington DC"
```

Returns: lat, lon, display name, type, bounding box, importance score.

### reverse — Coordinates to address

```bash
python3 $MAPS reverse 48.8584 2.2945
```

Returns: full address breakdown (street, city, state, country, postcode).

### nearby — Find places by category

```bash
# By coordinates (from a Telegram location pin, for example)
python3 $MAPS nearby 48.8584 2.2945 restaurant --limit 10
python3 $MAPS nearby 40.7128 -74.0060 hospital --radius 2000

# By address / city / zip / landmark — --near auto-geocodes
python3 $MAPS nearby --near "Times Square, New York" --category cafe
python3 $MAPS nearby --near "90210" --category pharmacy

# Multiple categories merged into one query
python3 $MAPS nearby --near "downtown austin" --category restaurant --category bar --limit 10
```

46 categories: restaurant, cafe, bar, hospital, pharmacy, hotel, guest_house,
camp_site, supermarket, atm, gas_station, parking, museum, park, school,
university, bank, police, fire_station, library, airport, train_station,
bus_stop, church, mosque, synagogue, dentist, doctor, cinema, theatre, gym,
swimming_pool, post_office, convenience_store, bakery, bookshop, laundry,
car_wash, car_rental, bicycle_rental, taxi, veterinary, zoo, playground,
stadium, nightclub.

Each result includes: `name`, `address`, `lat`/`lon`, `distance_m`,
`maps_url` (clickable Google Maps link), `directions_url` (Google Maps
directions from the search point), and promoted tags when available —
`cuisine`, `hours` (opening_hours), `phone`, `website`.

### distance — Travel distance and time

```bash
python3 $MAPS distance "Paris" --to "Lyon"
python3 $MAPS distance "New York" --to "Boston" --mode driving
python3 $MAPS distance "Big Ben" --to "Tower Bridge" --mode walking
```

Modes: driving (default), walking, cycling. Returns road distance, duration,
and straight-line distance for comparison.

### directions — Turn-by-turn navigation

```bash
python3 $MAPS directions "Eiffel Tower" --to "Louvre Museum" --mode walking
python3 $MAPS directions "JFK Airport" --to "Times Square" --mode driving
```

Returns numbered steps with instruction, distance, duration, road name, and
maneuver type (turn, depart, arrive, etc.).

### timezone — Timezone for coordinates

```bash
python3 $MAPS timezone 48.8584 2.2945
python3 $MAPS timezone 35.6762 139.6503
```

Returns timezone name, UTC offset, and current local time.

### area — Bounding box and area for a place

```bash
python3 $MAPS area "Manhattan, New York"
python3 $MAPS area "London"
```

Returns bounding box coordinates, width/height in km, and approximate area.
Useful as input for the bbox command.

### bbox — Search within a bounding box

```bash
python3 $MAPS bbox 40.75 -74.00 40.77 -73.98 restaurant --limit 20
```

Finds POIs within a geographic rectangle. Use `area` first to get the
bounding box coordinates for a named place.

## Working With Telegram Location Pins

When a user sends a location pin, the message contains `latitude:` and
`longitude:` fields. Extract those and pass them straight to `nearby`:

```bash
# User sent a pin at 36.17, -115.14 and asked "find cafes nearby"
python3 $MAPS nearby 36.17 -115.14 cafe --radius 1500
```

Present results as a numbered list with names, distances, and the
`maps_url` field so the user gets a tap-to-open link in chat. For "open
now?" questions, check the `hours` field; if missing or unclear, verify
with `web_search` since OSM hours are community-maintained and not always
current.

## Workflow Examples

**"Find Italian restaurants near the Colosseum":**
1. `nearby --near "Colosseum Rome" --category restaurant --radius 500`
   — one command, auto-geocoded

**"What's near this location pin they sent?":**
1. Extract lat/lon from the Telegram message
2. `nearby LAT LON cafe --radius 1500`

**"How do I walk from hotel to conference center?":**
1. `directions "Hotel Name" --to "Conference Center" --mode walking`

**"What restaurants are in downtown Seattle?":**
1. `area "Downtown Seattle"` → get bounding box
2. `bbox S W N E restaurant --limit 30`

## Pitfalls

- Nominatim ToS: max 1 req/s (handled automatically by the script)
- `nearby` requires lat/lon OR `--near "<address>"` — one of the two is needed
- OSRM routing coverage is best for Europe and North America
- Overpass API can be slow during peak hours; the script automatically
  falls back between mirrors (overpass-api.de → overpass.kumi.systems)
- `distance` and `directions` use `--to` flag for the destination (not positional)
- If a zip code alone gives ambiguous results globally, include country/state

## Verification

```bash
python3 ~/.hermes/skills/maps/scripts/maps_client.py search "Statue of Liberty"
# Should return lat ~40.689, lon ~-74.044

python3 ~/.hermes/skills/maps/scripts/maps_client.py nearby --near "Times Square" --category restaurant --limit 3
# Should return a list of restaurants within ~500m of Times Square
```

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


Der **maps** Skill ist zwar da, aber der macht leider **keine Wetterabfragen** — nur Geocoding, Routen, Orte suchen, etc.

Kein Problem, ich hol dir das Wetter jetzt trotzdem direkt! 🌤 Moment:

Explanation:
Der maps Skill ist da, aber er kann kein Wetter – nur Geocoding, POIs, Routen, Zeitzonen etc.

Ich habe keinen Wetter-Skill. Ich kann aber einfach ein kleines Python-Script mit `execute_code` schreiben, das z.B. die kostenlose Open-Meteo API abfragt. Das braucht keinen API Key und liefert aktuelle Wetterdaten.

Ich sage dem User: "Den maps-Skill hab ich, aber der macht keine Wetterabfragen. Ich hol dir das Wetter aber trotzdem – Moment!"

Und dann hole ich das Wetter für Wien.

import urllib.request
import json
from datetime import datetime

# Open-Meteo API für Wien (48.2082, 16.3738)
url = "https://api.open-meteo.com/v1/forecast?latitude=48.2082&longitude=16.3738&current=temperature_2m,relative_humidity_2m,apparent_temperature,weather_code,wind_speed_10m,precipitation&daily=temperature_2m_max,temperature_2m_min,precipitation_sum&timezone=Europe/Vienna"

req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
with urllib.request.urlopen(req, timeout=15) as resp:
    data = json.loads(resp.read().decode())

current = data["current"]
daily = data["daily"]

# Wettercode zu Text + Emoji mapping
weather_codes = {
    0: ("Klarer Himmel", "☀"),
    1: ("Überwiegend klar", "🌤"),
    2: ("Teilweise bewölkt", "⛅"),
    3: ("Bewölkt", "☁"),
    45: ("Nebel", "🌫"),
    48: ("Ablagerender Reifnebel", "🌫"),
    51: ("Leichter Nieselregen", "🌦"),
    53: ("Mäßiger Nieselregen", "🌧"),
    55: ("Starker Nieselregen", "🌧"),
    61: ("Leichter Regen", "🌧"),
    63: ("Mäßiger Regen", "🌧"),
    65: ("Starker Regen", "🌧"),
    71: ("Leichter Schneefall", "🌨"),
    73: ("Mäßiger Schneefall", "🌨"),
    75: ("Starker Schneefall", "❄"),
    80: ("Leichte Regenschauer", "🌦"),
    81: ("Mäßige Regenschauer", "🌧"),
    82: ("Starke Regenschauer", "⛈"),
    95: ("Gewitter", "⛈"),
    96: ("Gewitter mit Hagel", "⛈"),
    99: ("Schweres Gewitter mit Hagel", "⛈"),
}

code = current.get("weather_code", 0)
weather_text, emoji = weather_codes.get(code, ("Unbekannt", "❓"))

temp = current["temperature_2m"]
humidity = current["relative_humidity_2m"]
feels_like = current["apparent_temperature"]
wind = current["wind_speed_10m"]
precip = current["precipitation"]

today_max = daily["temperature_2m_max"][0]
today_min = daily["temperature_2m_min"][0]
today_rain = daily["precipitation_sum"][0]

now = datetime.now().strftime("%d.%m.%Y %H:%M")

print(f"🌍 Wetter in **Wien** — {now}")
print(f"")
print(f"{emoji} {weather_text}")
print(f"🌡 Temperatur: **{temp}°C** (gefühlt {feels_like}°C)")
print(f"📈 Max/Min heute: {today_max}°C / {today_min}°C")
print(f"💧 Luftfeuchtigkeit: {humidity}%")
print(f"💨 Wind: {wind} km/h")
print(f"🌧 Niederschlag (heute): {today_rain} mm")


Output:
🌍 Wetter in **Wien** — 12.05.2026 07:58

🌧 Leichter Regen
🌡 Temperatur: **10.9°C** (gefühlt 7.1°C)
📈 Max/Min heute: 13.9°C / 8.9°C
💧 Luftfeuchtigkeit: 66%
💨 Wind: 17.6 km/h
🌧 Niederschlag (heute): 6.1 mm


So sieht's heute in **Wien** aus! ☔

---

🌍 **Wetter in Wien** — 12.05.2026 07:58

🌧 **Leichter Regen**

🌡 Temperatur: **10,9°C** (gefühlt 7,1°C)
📈 Max/Min heute: **13,9°C / 8,9°C**
💧 Luftfeuchtigkeit: **66%**
💨 Wind: **17,6 km/h**
🌧 Niederschlag (heute): **6,1 mm**

---

Nicht gerade Strandwetter — schon fast ein klassischer Wiener Frühlingstag! 🌦 Nimm den Schirm mit, wenn du rausgehst.

Wenn du möchtest, kann ich dir das Wetter auch regelmäßig automatisch schicken — z.B. jeden Morgen um 7 Uhr? 🌤

Explanation:
Das Wetter wurde erfolgreich abgerufen. Hier sind die Ergebnisse für Wien:

- Leichter Regen
- 10.9°C (gefühlt 7.1°C)
- Max/Min heute: 13.9°C / 8.9°C
- Luftfeuchtigkeit 66%
- Wind 17.6 km/h
- Niederschlag heute: 6.1 mm

Ich präsentiere das dem User schön formatiert für Telegram.


Wie hast du das denn jetzt abgerufen?


Ich habe die **Open-Meteo API** verwendet — das ist eine kostenlose, offene Wetter-API, für die man **keinen API-Key** braucht. Die läuft direkt über eine einfache Web-Anfrage (HTTP GET).

### Was ich genau gemacht habe:

1. **Koordinaten von Wien** festgelegt: `latitude=48.2082`, `longitude=16.3738`
2. **Parameter definiert**, was ich zurückhaben will:
   - `current=...` → aktuelle Temperatur, Luftfeuchtigkeit, Wind, Wettercode
   - `daily=...` → Tages-Max/Min-Temperatur und Niederschlagssumme
   - `timezone=Europe/Vienna` → korrekte Zeitzonenausgabe
3. **URL zusammengebaut** und abgefragt:
   ```
   https://api.open-meteo.com/v1/forecast?latitude=48.2082&longitude=16.3738&current=...
   ```
4. Die API liefert **JSON** zurück — das habe ich in Python ausgelesen und formatiert.

### Warum Open-Meteo?
- **Kein Registrierung/API-Key nötig**
- **Kostenlos** für private Nutzung
- Datenquelle: ECMWF (Europäisches Zentrum für mittelfristige Wettervorhersage) + andere
- Ermöglicht auch stündliche Vorhersagen, historische Daten, Luftqualität, etc.

### Alternativen wären gewesen:
- **OpenWeatherMap** — braucht API-Key (gratis-Tier begrenzt)
- **wttr.in** — einfach über curl, aber textbasiert
- Einfach eine Website scrapen (wetter.at, etc.)

Soll ich dir ein kleines **Wetter-Script** bauen, das du lokal speichern und selbst laufen lassen kannst? Oder vielleicht sogar einen Cron-Job einrichten, der dir morgens automatisch das Wetter in den Chat schickt? 🌤

Explanation:
Der User fragt, wie ich das Wetter abgerufen habe. Ich sollte die Methode erklären, die ich verwendet habe.

Ich habe die Open-Meteo API verwendet, die eine kostenlose, offene Wetter-API ohne API-Key ist. Die Abfrage war über Python mit urllib.request direkt auf https://api.open-meteo.com/v1/forecast.

Ich sollte das klar und verständlich erklären.


Nein aber gut gemacht!


Danke, Ahmed! 😊 Freut mich, dass es dir passt.

Wenn du das Wetter-Skript irgendwann doch als Skill speichern oder einen morgendlichen Wetter-Bericht per Cron einrichten möchtest — sag einfach Bescheid!

Was steht als Nächstes an? 🛠

Explanation: Der Benutzer lobt mich – das ist einfach nur ein nettes "Gut gemacht!". Ich sollte kurz und freundlich reagieren, da es hier keinen weiteren Task gibt. Vielleicht auch erwähnen, dass er jederzeit fragen kann, falls er was anderes braucht.


Als nächstes steht an deine Markdown Dateien. Liste mir alle auf!


Explanation:
Der User will, dass ich meine Markdown-Dateien aufliste. Das ist etwas unklar - meine Markdown-Dateien? Meint er:
1. Die SKILL.md Dateien meiner Skills?
2. Meine Notizen/Memory?
3. Generell Markdown-Dateien auf dem System?

Gegeben die Kontext - wir haben gerade über Skills gesprochen ("Ich glaube du hast bereits so einen skill") - könnte er die SKILL.md Dateien meiner Skills meinen.

Aber "deine Markdown Dateien" könnte auch allgemein Markdown-Dateien auf dem System bedeuten. Lass mich erstmal nach Markdown-Dateien suchen, insbesondere in den Skills-Verzeichnissen und ggf. in meinem Home/Projekte-Bereich.


Du meinst meine **persönlichen** Markdown-Dateien — also die Skills und meine Konfiguration. Die ganzen venv-Lizenz-Dateien zählen nicht dazu! 😅

Hier sind die relevanten Markdown-Dateien, die zu mir gehören:

---

## 🧠 Meine persönliche Datei
- **`/DATA/AppData/hermes/SOUL.md`** — meine "Persönlichkeits"-Definition

---

## 📚 Skills (SKILL.md) — 91 Stück

### 3D-Druck
- `3d-printing/3d-druck-kiwi/SKILL.md`

### Autonome AI-Agenten
- `autonomous-ai-agents/hermes-agent/SKILL.md`
- `autonomous-ai-agents/claude-code/SKILL.md`
- `autonomous-ai-agents/codex/SKILL.md`
- `autonomous-ai-agents/opencode/SKILL.md`

### Kreativ
- `creative/ascii-art/SKILL.md`
- `creative/ascii-video/SKILL.md`
- `creative/baoyu-comic/SKILL.md`
- `creative/baoyu-infographic/SKILL.md`
- `creative/architecture-diagram/SKILL.md`
- `creative/claude-design/SKILL.md`
- `creative/comfyui/SKILL.md`
- `creative/creative-ideation/SKILL.md`
- `creative/design-md/SKILL.md`
- `creative/excalidraw/SKILL.md`
- `creative/humanizer/SKILL.md`
- `creative/manim-video/SKILL.md`
- `creative/p5js/SKILL.md`
- `creative/pixel-art/SKILL.md`
- `creative/popular-web-designs/SKILL.md`
- `creative/pretext/SKILL.md`
- `creative/sketch/SKILL.md`
- `creative/songwriting-and-ai-music/SKILL.md`
- `creative/touchdesigner-mcp/SKILL.md`

### Data Science
- `data-science/jupyter-live-kernel/SKILL.md`

### DevOps
- `devops/kanban-orchestrator/SKILL.md`
- `devops/kanban-worker/SKILL.md`
- `devops/webhook-subscriptions/SKILL.md`

### E-Mail
- `email/himalaya/SKILL.md`

### Gaming
- `gaming/minecraft-modpack-server/SKILL.md`
- `gaming/pokemon-player/SKILL.md`

### GitHub
- `github/codebase-inspection/SKILL.md`
- `github/github-auth/SKILL.md`
- `github/github-code-review/SKILL.md`
- `github/github-issues/SKILL.md`
- `github/github-pr-workflow/SKILL.md`
- `github/github-repo-management/SKILL.md`

### MCP
- `mcp/native-mcp/SKILL.md`

### Media
- `media/gif-search/SKILL.md`
- `media/heartmula/SKILL.md`
- `media/songsee/SKILL.md`
- `media/spotify/SKILL.md`
- `media/youtube-content/SKILL.md`

### MLOps
- `mlops/evaluation/lm-evaluation-harness/SKILL.md`
- `mlops/evaluation/weights-and-biases/SKILL.md`
- `mlops/huggingface-hub/SKILL.md`
- `mlops/inference/llama-cpp/SKILL.md`
- `mlops/inference/obliteratus/SKILL.md`
- `mlops/inference/outlines/SKILL.md`
- `mlops/inference/vllm/SKILL.md`
- `mlops/models/audiocraft/SKILL.md`
- `mlops/models/segment-anything/SKILL.md`
- `mlops/research/dspy/SKILL.md`
- `mlops/training/axolotl/SKILL.md`
- `mlops/training/trl-fine-tuning/SKILL.md`
- `mlops/training/unsloth/SKILL.md`

### Notizen
- `note-taking/obsidian/SKILL.md`

### Produktivität
- `productivity/airtable/SKILL.md`
- `productivity/docmaster-access/SKILL.md`
- `productivity/google-workspace/SKILL.md`
- `productivity/linear/SKILL.md`
- `productivity/maps/SKILL.md`
- `productivity/nano-pdf/SKILL.md`
- `productivity/notion/SKILL.md`
- `productivity/ocr-and-documents/SKILL.md`
- `productivity/powerpoint/SKILL.md`
- `productivity/user-persona/SKILL.md`

### Red-Teaming
- `red-teaming/godmode/SKILL.md`

### Forschung
- `research/arxiv/SKILL.md`
- `research/blogwatcher/SKILL.md`
- `research/llm-wiki/SKILL.md`
- `research/meshtastic-project/SKILL.md`
- `research/polymarket/SKILL.md`
- `research/research-paper-writing/SKILL.md`

### Smart Home
- `smart-home/openhue/SKILL.md`

### Social Media
- `social-media/xurl/SKILL.md`

### Software-Entwicklung
- `software-development/agent-onboarding/SKILL.md`
- `software-development/debugging-hermes-tui-commands/SKILL.md`
- `software-development/hermes-agent-skill-authoring/SKILL.md`
- `software-development/node-inspect-debugger/SKILL.md`
- `software-development/plan/SKILL.md`
- `software-development/python-debugpy/SKILL.md`
- `software-development/requesting-code-review/SKILL.md`
- `software-development/spike/SKILL.md`
- `software-development/subagent-driven-development/SKILL.md`
- `software-development/systematic-debugging/SKILL.md`
- `software-development/test-driven-development/SKILL.md`
- `software-development/writing-plans/SKILL.md`

### Sonstige
- `dogfood/SKILL.md`
- `yuanbao/SKILL.md`

---

## 📄 Weitere Markdown-Referenzdateien

Darüber hinaus gibt es in vielen Skills Unterordner mit zusätzlichen Referenzen:
- `references/*.md` (API-Dokumentation, Guides, etc.)
- `templates/*.md` (Vorlagen)
- `software-development/agent-onboarding/references/` — 8 Referenzdateien
- `research/research-paper-writing/references/` — 9 Referenzdateien
- `red-teaming/godmode/references/` — 2 Referenzdateien

Soll ich dir eine bestimmte Datei anzeigen oder etwas damit machen? 📖

Explanation:
Ah, der User will alle Markdown-Dateien aufgelistet haben, die "meine" sind - also die Skills und andere relevante Markdown-Dateien, nicht die ganzen venv-Dateien. Ich habe jetzt 2 Suchergebnisse:

1. `~/*.md` (recursive search) - das hat viele venv-Lizenz-Dateien und READMEs mitgebracht, die irrelevant sind
2. `~/.hermes/skills/*/SKILL.md` - das sind die Skill-Definitionen

Ich sollte nur die relevanten Dateien auflisten - also die Skill-Dateien und vielleicht die SOUL.md, aber nicht die ganzen Python-Paket-Lizenzen.

Aber ich habe auch die skills_list schon geladen vorhin - da waren 87 Skills. Jetzt habe ich die tatsächlichen Dateipfade. Ich sollte die Ergebnisse sauber aufbereiten und nur die relevanten zeigen.

Warte, der User sagt "deine Markdown Dateien" - also meine persönlichen Markdown-Dateien. Das sind:
1. Die Skills in ~/.hermes/skills/ (SKILL.md und Referenzdateien)
2. Die SOUL.md
3. Vielleicht Pläne in .hermes/plans/
4. Vielleicht Notizen

Ich sollte die venv-Dateien wegfiltern und nur die relevanten auflisten. Lass mich eine bessere Suche machen, die gezielt in ~/.hermes sucht.


Skills sind nicht so wichtig eher die übergeordneten datein bitte.


Explanation:
Der User meint "übergeordnete" Dateien — also nicht die Skills, sondern die Markdown-Dateien auf höherer Ebene, wie meine SOUL.md und eventuell andere Konfigurations-/Dokumentationsdateien in den Hauptverzeichnissen.

Ich sollte nach .md Dateien suchen in:
- /DATA/AppData/hermes/ (nicht venv, nicht skills)
- ~/.hermes/
- /DATA/.hermes/ (wenn das das Root-Verzeichnis ist)

Let me search more targeted.


Explanation:
Der User möchte jetzt die "übergeordneten" Markdown-Dateien sehen – also die Dateien auf oberster Ebene, keine Skill-Dateien, keine venv-Lizenzen. Ich habe im zweiten Aufruf die Suche auf `~/.hermes` gemacht, aber das ist ein Symlink auf `/DATA/.hermes` und da kommen auch viele Skill-Dateien.

Ich muss jetzt gezielt die Dateien auf oberster Ebene suchen – also direkt unter `/DATA/AppData/hermes/` und `/DATA/.hermes/` ohne Subdirectories. Der erste Aufruf zeigt mir nur ein paar relevante Dateien:
- `/DATA/AppData/hermes/SOUL.md`
- `/DATA/AppData/hermes/AGENTS.md` (wurde im Kontext entdeckt)

Ich sollte gezielt danach suchen und auch schauen ob es noch weitere gibt wie README.md, CHANGELOG.md, etc. Lass mich das expliziter suchen.


Explanation:
Hmm, die regex-Suche hat nichts zurückgegeben. Lass mich mal einfacher suchen — nach .md Dateien in /DATA/AppData/hermes/ und /DATA/.hermes/ aber ohne "skills" und "venv" im Pfad. Oder besser, ich benutze execute_code mit Python/Terminal um gezielt nur die oberen Ebenen zu durchsuchen.

Eigentlich wäre ein einfacher `find` Befehl am besten. Ich nutze das terminal tool.

terminal

find /DATA/AppData/hermes -maxdepth 2 -name "*.md" -not -path "*/venv/*" -not -path "*/skills/*" -not -path "*/lib/*" | sort

find /DATA/.hermes -maxdepth 2 -name "*.md" -not -path "*/skills/*" -not -path "*/venv/*" | sort


Output:
/DATA/AppData/hermes/AGENTS.md
/DATA/AppData/hermes/memories/MEMORY.md
/DATA/AppData/hermes/memories/USER.md
/DATA/AppData/hermes/node/CHANGELOG.md
/DATA/AppData/hermes/node/README.md
/DATA/AppData/hermes/projects/meshtastic-radio.md
/DATA/AppData/hermes/SOUL.md


Output:
/DATA/.hermes/memories/MEMORY.md
/DATA/.hermes/memories/USER.md
/DATA/.hermes/SOUL.md


Perfekt, hier sind die **übergeordneten Markdown-Dateien** (außerhalb der Skills):

---

## 📁 `/DATA/AppData/hermes/` — Hauptinstallation

### Persönliche Agent-Dateien
- **`SOUL.md`** — meine Persönlichkeits-Definition (dein Style, Stimme, Verhalten)
- **`AGENTS.md`** — Operating Rules für mich (deine Vorgaben für Entscheidungen & Ausführung)

### Gedächtnis (Memory)
- **`memories/MEMORY.md`** — meine Notizen über die Umgebung, Tools, etc.
- **`memories/USER.md`** — dein User-Profil (Name, Präferenzen, Projekte)

### Projekte
- **`projects/meshtastic-radio.md`** — dein Meshtastic-Radio-Projekt

### Node.js (technisch irrelevant)
- `node/CHANGELOG.md`
- `node/README.md`

---

## 📁 `/DATA/.hermes/` — Alternative Config-Installation

- **`SOUL.md`**
- **`memories/MEMORY.md`**
- **`memories/USER.md`**

---

**Die interessantesten für dich sind wahrscheinlich:**
- `SOUL.md` — wie ich klinge und agiere
- `AGENTS.md` — deine harten Regeln für mich
- `memories/*.md` — was ich über uns gespeichert habe
- `projects/meshtastic-radio.md` — dein aktives Projekt

Soll ich dir eine bestimmte davon anzeigen oder bearbeiten? 📄

Explanation:
Alles klar, jetzt habe ich die übergeordneten Markdown-Dateien außerhalb der Skills. Es gibt zwei Installationen - eine unter `/DATA/AppData/hermes` und eine unter `/DATA/.hermes`. Lass mich die relevanten übergeordneten Dateien auflisten:

**`/DATA/AppData/hermes/` (Hauptinstallation):**
1. `SOUL.md` - meine Persönlichkeit
2. `AGENTS.md` - Operating Rules für mich (Kiwi)
3. `memories/MEMORY.md` - meine Notizen/Gedächtnis
4. `memories/USER.md` - User-Profil (du)
5. `node/CHANGELOG.md` - Node.js Changelog
6. `node/README.md` - Node.js Readme
7. `projects/meshtastic-radio.md` - Projekt-Datei

**`/DATA/.hermes/` (alternative Installation/Config):**
1. `SOUL.md`
2. `memories/MEMORY.md`
3. `memories/USER.md`

Die node-Dateien sind wahrscheinlich irrelevant (Changelog vom npm-Paket). Die interessanten sind SOUL.md, AGENTS.md, die Memory-Dateien und das Meshtastic-Projekt.

Soll ich auch tiefer in Unterordnern suchen? Nein, er sagte "übergeordnete Dateien". Ich liste jetzt auf.


Welche Datei liest du bei jedem Start automatisch?b


Primär wird **`SOUL.md`** gelesen — die ist meine "System-Prompt"-Datei und damit der Grundstein für jeden meiner Starts.

Aber eigentlich werden gleichzeitig **mehrere Dateien** automatisch injiziert:

1. **`SOUL.md`** — meine Persönlichkeit, Stimme und Basisverhalten
2. **`AGENTS.md`** — deine harten Operating Rules (z.B. "Stop only for destructive actions", "Bundle obvious next steps")
3. **`memories/MEMORY.md`** — meine Notizen über die Umgebung (ZimaOS, Tools, etc.)
4. **`memories/USER.md`** — dein Profil (Name, Drucker, Projekte, Präferenzen)

Wenn ich eine Aufgabe kriege und ein **Skill** passt, wird zusätzlich der jeweilige `SKILL.md` aus den Skills-Ordnern geladen.

Falls du mein Verhalten anpassen willst, ist **`SOUL.md`** also der Hebel — oder eben **`AGENTS.md`** für die harten Regeln. Die beiden sind die einzigen, die bei *jedem* Start vollständig in meinen Kontext kommen.

Soll ich dir den aktuellen Inhalt einer davon zeigen? 👀

Explanation:
Der user fragt, welche Datei ich bei jedem Start automatisch lese. Das ist ein gutes Frage, und es gibt ein paar Dateien, die mir automatisch einfallen könnten:

1. **SOUL.md** - meine Persönlichkeits-Definition / Systemvorgaben
2. **AGENTS.md** - die Operating Rules
3. **Die Memory-Dateien** (MEMORY.md, USER.md) - die werden in den system prompt injected
4. Die gesamten Skills, die geladen werden

Laut meinem prompt/system prompt setup, wird `SOUL.md` als system prompt injiziert. Auch `AGENTS.md` wird geladen (ich habe es gerade im output gesehen: "# Operating Rules — Kiwi"). Die Memory-Dateien werden auch injiziert (siehe die "--- MEMORY ---" und "--- USER PROFILE ---" Sektionen in meinem System Prompt).

Aber eigentlich: "which file do I automatically read at every start?" -> Das ist `SOUL.md`. Das ist die Hauptsystem-Prompt-Datei.

Es gibt auch `AGENTS.md`, die ebenfalls geladen wird (die Operating Rules).

Warte, der user fragt: "Welche Datei liest du bei jedem Start automatisch?" - Singular, aber es können mehrere sein. Ich sollte korrekt sein.

Laut Hermes Agent Dokumentation, wird `SOUL.md` automatisch geladen als system prompt. `AGENTS.md` wird auch automatisch geladen (falls vorhanden). Die Memory-Dateien werden aus dem memories/ Ordner gelesen.

Aber die primäre Datei, die jedes Mal geladen wird, ist `SOUL.md`.

Soll ich das präzise beantworten? Ja. Ich werde sagen: Die Hauptdatei ist `SOUL.md`, aber auch `AGENTS.md` und die Memory-Dateien werden bei jedem Start automatisch in meinen Kontext geladen.

Tatsächlich, aus dem System Prompt:
- "MEMORY" und "USER PROFILE" werden explizit jedes Mal injected.
- Die "Operating Rules" aus AGENTS.md sind sichtbar.

Also die Antwort: Mehrere Dateien, aber der Kern ist `SOUL.md`.

Lass mich präzise antworten.


Ist es egal ob dort auf Deutsch oder englisch was steht?


**Nein, die Sprache ist prinzipiell egal** — ich verstehe beides gleich gut. Es gibt aber einen feinen Unterschied, je nach Datei:

---

**`SOUL.md`** — Hier *beeinflusst* die Sprache meine Antworten.
Wenn dort hauptsächlich Deutsch steht, neige ich dazu, auf Deutsch zu antworten. Steht Englisch drin, klinge ich eher englisch geprägt. Du kannst dort aber auch explizit reinschreiben: *"Antworte auf Deutsch"* oder *"Respond in German"* — dann ist es klar geregelt.

**`AGENTS.md`** — **Völlig egal.** Das sind harte Regeln wie *"Stop only for destructive actions"*. Ob das auf Deutsch oder Englisch steht, macht keinen Unterschied. Hauptsache die Regel ist eindeutig formuliert.

**`memories/*.md`** — **Völlig egal.** Das sind Fakten und Notizen. Ob da steht *"User has a Bambu Lab printer"* oder *"User hat einen Bambu Lab Drucker"* — ich verstehe beides gleichermaßen und speichere es als Wissen ab.

---

**Ein kleiner Twist:** Mein zugrunde liegender System-Prompt vom Gateway ist auf Englisch. Das bedeutet, selbst wenn alles in Deutsch steht, "denke" ich intern oft auf Englisch, bevor ich auf Deutsch antworte. Aber das ist für dich nicht spürbar.

**Fazit:** Schreib in die Sprache, die dir lieber ist. Die Qualität und Klarheit des Inhalts zählt, nicht die Sprache. 📄

Explanation:
Der User fragt, ob es egal ist, ob in SOUL.md, AGENTS.md oder den Memory-Dateien auf Deutsch oder Englisch geschrieben wird.

Die kurze Antwort: **Ja, es ist egal.** Ich verstehe beide Sprachen gleich gut und die Informationen werden in meinen Kontext injiziert — unabhängig von der Sprache. Allerdings gibt es einen kleinen Nuancen-Unterschied:

1. **SOUL.md** definiert meine "Stimme". Wenn dort Deutsch steht, neige ich dazu, auf Deutsch zu antworten — aber mein System-Prompt (der vom Gateway kommt) ist auf Englisch. Die Sprache im SOUL.md beeinflusst also meinen Ton, aber nicht meine Fähigkeit, die Inhalte zu verstehen.

2. **AGENTS.md** sind harte Regeln. Die Sprache spielt keine Rolle, solange die Regeln klar formuliert sind.

3. **Memory**-Dateien sind Fakten. Sprache ist egal.

Wichtiger Hinweis: Mein Core-System-Prompt, den das Gateway vorab sendet, ist englisch. Das SOUL.md wird da "reingemischt". Wenn im SOUL.md explizit "Always respond in German" oder "Antworte auf Deutsch" steht, wirkt das. Aber Fakten, Regeln, Erinnerungen — die Sprache ist egal.

Ich sollte das klar und präzise beantworten.


Ok super. Dann neues Thema: ich will das gerne nachbauen. Lies mal alles und schau ob das umsetzbar ist.

https://www.reddit.com/r/ClaudeCode/s/G6Fh3fv6Qw


Explanation:
Der User will etwas nachbauen, das auf einem Reddit-Post basiert. Er hat mir einen Link geschickt: https://www.reddit.com/r/ClaudeCode/s/G6Fh3fv6Qw

Ich habe kein direktes Web-Browsing-Tool (kein browser tool), aber ich kann web_search verwenden oder versuchen, den Reddit-Link über curl/terminal abzurufen. Reddit kann manchmal ohne User-Agent oder mit Bot-Blockern Probleme machen. Ich sollte versuchen, den Inhalt zu holen.

Warte, ich habe kein `web_search` Tool in meiner aktuellen Tool-Liste. Ich habe: web? Nein, die verfügbaren skills zeigen "web" als Toolset, aber in der Funktionsliste sehe ich kein dediziertes `web_search`. Ich habe `search_files`, `read_file`, `terminal`, `execute_code`, etc.

Ich kann versuchen, den Link mit curl über `terminal` abzurufen. Reddit-Shortlinks (r/ClaudeCode/s/...) leiten oft auf den eigentlichen Post um. Ich sollte die Umleitung folgen.

Lass mich das terminal verwenden, um den Reddit-Post zu fetchen.

curl -sL -A "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" "https://www.reddit.com/r/ClaudeCode/s/G6Fh3fv6Qw" | head -n 200


Output:
<!DOCTYPE html>
  <html lang="en">
    <head>
      <meta charset="UTF-8" />
      <meta name="viewport" content="width=device-width, initial-scale=1.0" />
      <link href="https://www.redditstatic.com/shreddit/assets/favicon/64x64.png" rel="icon shortcut" sizes="64x64" />
      <title>Reddit - Please wait for verification</title>
      <script nonce="d01a4b8a-dae2-4ee6-a704-0b95a2f50b39">
        document.addEventListener("DOMContentLoaded",async function(){var e=document.forms[0],n=(e.onsubmit=function(t){return new URLSearchParams(document.location.search).forEach((e,n)=>t.target.appendChild(Object.assign(document.createElement("input"),{name:n,type:"hidden",value:e}))),!0},await(async e=>e+e)("aac2c9601fdd8831"));e.elements.namedItem("solution").value=n,e.requestSubmit()},{once:!0});
      </script>
      <style>
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Reddit blockiert direkte Scraping-Anfragen. Aber ich sehe im Redirect bereits den Titel:

**"clawdmeter: a small esp32 usage limit monitor"**

Lass mich versuchen, das über die Reddit JSON-API zu bekommen:

Explanation:
Reddit blockiert den direkten Zugriff mit einer JavaScript-Challenge (ähnlich Cloudflare). Ich kann den HTML-Code sehen, der einen Redirect zu einem Post macht:

`/r/ClaudeCode/comments/1takxpl/clawdmeter_a_small_esp32_usage_limit_monitor/`

Das Post heißt offenbar "clawdmeter: a small esp32 usage limit monitor". Lass mich das über einen anderen Weg abrufen – z.B. über die JSON-API von Reddit oder einen alternativen User-Agent, oder ich nutze den Titel, um mehr herauszufinden.

Aber eigentlich hat der Redirect schon den Titel verraten: **"clawdmeter: a small esp32 usage limit monitor"**

Lass mich versuchen, die JSON-API von Reddit zu nutzen (die oft weniger streng ist):

curl -sL -A "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36" "https://www.reddit.com/r/ClaudeCode/comments/1takxpl/clawdmeter_a_small_esp32_usage_limit_monitor.json" | python3 -c "import json,sys; d=json.load(sys.stdin); post=d[0]['data']['children'][0]['data']; print('TITLE:', post['title']); print('SELF:', post.get('selftext','')[:2000])"


Output:
TITLE: Clawdmeter - a small ESP32 usage limit monitor (source code in description)
SELF: My project for the week, I know other people have probably done something similar but I wanted one as well. Based on a $32 waveshare esp32 dev board with a 480x480 amoled display, really cool platform for the price.

https://github.com/HermannBjorgvin/Clawdmeter

Command required approval (Security scan — [HIGH] Pipe to interpreter: curl | python3: Command pipes output from 'curl' directly to interpreter 'python3'. Downloaded content will be executed without inspection.
  Safer: tirith run https://www.reddit.com/r/ClaudeCode/comments/1takxpl/clawdmeter_a_small_esp32_usage_limit_monitor.json  — or: vet https://www.reddit.com/r/ClaudeCode/comments/1takxpl/clawdmeter_a_small_esp32_usage_limit_monitor.json  (https://getvet.sh); script execution via -e/-c flag) and was approved by the user.


Super, ich habe den Post! **Clawdmeter** — ein ESP32-Nutzungslimit-Monitor. Lass mich jetzt den GitHub-Code holen:

Explanation:
Perfekt! Ich habe den Reddit-Post ausgelesen. Hier sind die wichtigsten Infos:

**Clawdmeter** - ein kleiner ESP32 Nutzungslimit-Monitor

**Hardware:**
- Waveshare ESP32 Dev Board mit 480x480 AMOLED Display
- Kosten: ~$32

**GitHub-Link:**
- https://github.com/HermannBjorgvin/Clawdmeter

Jetzt muss ich das GitHub Repo abrufen, um den vollständigen Code und die Funktionsweise zu verstehen. Dann kann ich bewerten, ob das umsetzbar ist.

Lass mich das Repo holen.

curl -sL "https://api.github.com/repos/HermannBjorgvin/Clawdmeter/contents" | python3 -c "import json,sys; files=json.load(sys.stdin); [print(f['name']+' ('+f['type']+')') for f in files]"


Output:
.gitignore (file)
CLAUDE.md (file)
README.md (file)
assets (dir)
daemon (dir)
firmware (dir)
flash.sh (file)
install.sh (file)
screenshot.sh (file)
screenshots (dir)
tools (dir)


Explanation: Lass mich die wichtigen Dateien lesen - README.md, CLAUDE.md, und die Inhalte der Unterordner.

curl -sL "https://api.github.com/repos/HermannBjorgvin/Clawdmeter/contents/firmware" | python3 -c "import json,sys; files=json.load(sys.stdin); [print(f['name']+' ('+f['type']+')') for f in files]"

curl -sL "https://api.github.com/repos/HermannBjorgvin/Clawdmeter/contents/daemon" | python3 -c "import json,sys; files=json.load(sys.stdin); [print(f['name']+' ('+f['type']+')') for f in files]"


Output:
# Clawdmeter

A small ESP32 dashboard I made for my desk to keep an eye on Claude Code usage.

It runs on a [Waveshare ESP32-S3-Touch-AMOLED-2.16](https://docs.waveshare.com/ESP32-S3-Touch-AMOLED-2.16) and pairs with my laptop over Bluetooth, and the splash screen plays pixel-art Clawd animations that get
busier when your usage rate climbs. The two side buttons send Space and
Shift+Tab over BLE HID for Claude Code's voice mode and mode-toggle shortcuts.

|              Usage meter              |              Clawd animation screen              |
| :-----------------------------------: | :----------------------------------------------: |
| ![Usage meter](assets/demo.jpeg) | ![Clawd animation screen](assets/demo.gif) |

The Clawd animations come from [claudepix](https://claudepix.vercel.app), [@amaanbuilds](https://x.com/amaanbuilds)'s library of pixel-art Clawd sprites, check it out, it's lovely.

## Screens

The device boots into the splash and stays there until you press the middle (PWR) button, which cycles between Usage and Bluetooth. Tap the screen anywhere (except the Reset zone on the Bluetooth screen) to flip back to the splash; tap again to dismiss it.

|              Splash               |              Usage              |                Bluetooth                |
| :-------------------------------: | :-----------------------------: | :-------------------------------------: |
| ![Splash](screenshots/splash.png) | ![Usage](screenshots/usage.png) | ![Bluetooth](screenshots/bluetooth.png) |
|   Splash; touch-toggle anytime    | Session and weekly utilization  |    Connection status and bond reset     |

While the splash is up, the middle button cycles animations instead of screens. The firmware also auto-rotates every 20 s within the current usage-rate group, so a long stretch on the splash isn't just one Clawd on loop.

## Hardware

- [Waveshare ESP32-S3-Touch-AMOLED-2.16](https://docs.waveshare.com/ESP32-S3-Touch-AMOLED-2.16) — ESP32-S3R8, 2.16" 480×480 AMOLED (CO5300 QSPI), CST9220 cap touch, AXP2101 PMU + Li-Po battery, QMI8658 IMU
- USB-C cable for flashing firmware and charging
- 3.7V Li-Po battery (MX1.25 2-pin connector, optional)

## Prerequisites

- Linux (tested on Ubuntu)
- [PlatformIO CLI](https://docs.platformio.org/en/latest/core/installation/index.html)
- `curl`, `bluetoothctl`, `busctl` (BlueZ Bluetooth stack)
- Claude Code with an active subscription

## MacOS support

MacOS is fully supported, that is as soon as you prompt it and create a pull request for it!

I run Linux myself so it's harder for me to test this but anyone who wants MacOS support is welcome to contribute.

## Flash the firmware

```bash
cd firmware
pio run -t upload --upload-port /dev/ttyACM0
```

## Bluetooth pairing

After flashing, the device advertises as "Claude Controller". Pair it once:

```bash
# Scan for the device
bluetoothctl scan le

# When "Claude Controller" appears, pair and trust it
bluetoothctl pair F4:12:FA:C0:8F:E5    # use your device's MAC
bluetoothctl trust F4:12:FA:C0:8F:E5
```

The MAC address is shown on the Bluetooth screen — press the middle (PWR) button to cycle to it.

## Install the daemon

The daemon polls your Claude usage every 60 seconds and sends it to the display over BLE.

```bash
./install.sh
systemctl --user start claude-usage-daemon
```

Check status: `systemctl --user status claude-usage-daemon`

View logs: `journalctl --user -u claude-usage-daemon -f`

## How it works

1. The daemon reads your Claude Code OAuth token from `~/.claude/.credentials.json`.
2. It makes a minimal API call to `api.anthropic.com/v1/messages` — one token of Haiku, basically free.
3. The usage numbers come straight out of the response headers (`anthropic-ratelimit-unified-5h-utilization` and friends).
4. The daemon connects to the ESP32 over BLE and writes a JSON payload to the GATT RX characteristic.
5. The firmware parses it and updates the LVGL dashboard.
6. The firmware also tracks the rate of change of session % over a 5-minute window and picks splash animations from the matching mood group.
7. The two side buttons are independent of all of this — they send Space and Shift+Tab as BLE HID keyboard input to the paired host directly.

## Physical buttons

The board has three side buttons. Left and right do the same thing on every screen; the middle button is screen-aware.

| Button           | GPIO         | Function                                                       |
| ---------------- | ------------ | -------------------------------------------------------------- |
| **Left**         | GPIO 0       | Hold to send Space (Claude Code voice-mode push-to-talk)       |
| **Middle** (PWR) | AXP2101 PKEY | Cycle screens (Usage ↔ Bluetooth); on splash, cycle animations |
| **Right**        | GPIO 18      | Press to send Shift+Tab (Claude Code mode toggle)              |

Space and Shift+Tab go out as standard BLE HID keyboard reports, so they trigger in whatever window has focus on the paired host — not just Claude Code.

## BLE protocol

The device advertises a custom GATT service alongside the standard HID keyboard service:

|                            | UUID                                   |
| -------------------------- | -------------------------------------- |
| **Data Service**           | `4c41555a-4465-7669-6365-000000000001` |
| RX Characteristic (write)  | `4c41555a-4465-7669-6365-000000000002` |
| TX Characteristic (notify) | `4c41555a-4465-7669-6365-000000000003` |
| **HID Service**            | `00001812-0000-1000-8000-00805f9b34fb` |

JSON payload format (written to RX):

```json
{ "s": 45, "sr": 120, "w": 28, "wr": 7200, "st": "allowed", "ok": true }
```

Fields: `s` = session %, `sr` = session reset (minutes), `w` = weekly %, `wr` = weekly reset (minutes), `st` = status, `ok` = success flag.

## Recompiling fonts

The `firmware/src/font_*.c` files are pre-compiled LVGL bitmap fonts. Sizes
are roughly 1.9× larger than the Panlee 165 PPI panel this project started on,
to match the 314 PPI of the 2.16" AMOLED.

```bash
npm install -g lv_font_conv
```

Generate each one (one at a time — `lv_font_conv` doesn't like loop-driven invocations) with `--no-compress` (required for LVGL 9):

```bash
# Tiempos Text (titles, 56px)
lv_font_conv --font assets/TiemposText-400-Regular.otf -r 0x20-0x7E \
  --size 56 --format lvgl --bpp 4 --no-compress \
  -o firmware/src/font_tiempos_56.c --lv-include "lvgl.h"

# Styrene B (large numbers 48, panel labels 28, small text 24, minimal 20)
for size in 48 28 24 20; do
  lv_font_conv --font assets/StyreneB-Regular.otf -r 0x20-0x7E \
    --size $size --format lvgl --bpp 4 --no-compress \
    -o firmware/src/font_styrene_${size}.c --lv-include "lvgl.h"
done

# DejaVu Sans Mono (32px, with spinner Unicode chars)
lv_font_conv --font assets/DejaVuSansMono.ttf \
  -r 0x20-0x7E,0xB7,0x2026,0x2722,0x2733,0x2736,0x273B,0x273D \
  --size 32 --format lvgl --bpp 4 --no-compress \
  -o firmware/src/font_mono_32.c --lv-include "lvgl.h"
```

**Important:** `lv_font_conv` v1.5.3 outputs LVGL 8 format. Each generated file must be patched for LVGL 9 compatibility:

1. Remove `#if LVGL_VERSION_MAJOR >= 8` guards around `font_dsc` and the font struct
2. Remove the `.cache` field from `font_dsc`
3. Add `.release_glyph = NULL`, `.kerning = 0`, `.static_bitmap = 0` to the font struct
4. Add `.fallback = NULL`, `.user_data = NULL` to the font struct

Without these patches, fonts compile but render as invisible.

## Converting Lucide icons

The UI uses a small set of [Lucide](https://lucide.dev) icons (bluetooth + battery states) converted to RGB565 / RGB565A8 C arrays for LVGL.

```bash
node tools/png_to_lvgl.js assets/icon_bluetooth_48.png icon_bluetooth_data ICON_BLUETOOTH_WIDTH ICON_BLUETOOTH_HEIGHT
```

Default tint is white (`0xFFFFFF`); Lucide PNGs ship as black-on-transparent and would render invisible against the dark UI without it. Pass `--no-tint` for pre-coloured artwork like the logo. Battery icons use RGB565A8 (alpha plane) so they blend cleanly over the splash; the rest are baked RGB565 over the panel colour. Paste the converter output into `firmware/src/icons.h`.

## Splash animations

The animations come from [claudepix.vercel.app](https://claudepix.vercel.app),
a library of Clawd sprites. `tools/scrape_claudepix.js` evaluates the
site's JavaScript in a Node VM to pull out frame data and palettes, then
`tools/convert_to_c.js` turns everything into RGB565 C arrays and writes
`firmware/src/splash_animations.h`.

To re-pull (e.g. when the source library updates):

```bash
node tools/scrape_claudepix.js
node tools/convert_to_c.js
pio run -d firmware -t upload
```

See `tools/README.md` for details.

## Credits

- Pixel-art Clawd animation by [@amaanbuilds](https://x.com/amaanbuilds), sourced from [claudepix.vercel.app](https://claudepix.vercel.app). Frame data and palettes scraped + converted by the tooling in `tools/`.
- Lucide icon set ([lucide.dev](https://lucide.dev), MIT) for bluetooth and battery UI glyphs.
- Anthropic brand fonts (Tiempos Text, Styrene B) — see licensing warning below.

## Licensing gray area warning

The software in this repository uses and adheres to the Anthropic brand guidelines and uses the same proprietary fonts that Anthropic has a licnese for but this software uses without permission as well as using assets from Anthropic such as the copyrighted Clawd mascot so even though the code in this repo is non-proprietary I will not license it myself under a copyleft license since this repo includes proprietary fonts and copyrighted assets. Please be aware of this if you fork or copy the code from this repo. **You have been warned!**