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


Telefonat mit Oma eintragen bitte und mir Nihan Teyze jeweils 1h heute

Bitte schreib in die Notizen jeweils „Besuch bei Oma“


Explanation:
Der Chef möchte zwei Termine für heute in seinem Kalender eintragen:

1. "Telefonat mit Oma" - 1 Stunde
2. "Besuch bei Oma" mit Nihan Teyze - 1 Stunde

Ich muss zuerst die aktuelle Uhrzeit und den Kalender checken. Heute ist laut System-Kontext Mittwoch, 3. Juni 2026, 14:12 Uhr.

Ich sollte den Google-Workspace-Skill laden, um Termine im Kalender einzutragen. Aber zuerst sollte ich prüfen, ob der Google OAuth-Token noch gültig ist (laut Memory: "Google OAuth token expires every ~7 days. Proactively check auth").

Lass mich den Google-Workspace-Skill laden und dann die Termine eintragen.


google-workspace

---
name: google-workspace
description: "Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python."
version: 1.0.1
author: Nous Research
license: MIT
required_credential_files:
  - path: /DATA/AppData/hermes/google_client_secret.json
    description: Google OAuth2 client credentials - project amadeus-494810
  - path: /DATA/.hermes/google_token.json
    description: Google OAuth2 token - ahmed.aytac@gmail.com, all scopes granted
metadata:
  hermes:
    tags: [Google, Gmail, Calendar, Drive, Sheets, Docs, Contacts, Email, OAuth]
    homepage: https://github.com/NousResearch/hermes-agent
    related_skills: [himalaya]
---

# Google Workspace

Gmail, Calendar, Drive, Contacts, Sheets, and Docs — through Hermes-managed OAuth and a thin CLI wrapper. When `gws` is installed, the skill uses it as the execution backend for broader Google Workspace coverage; otherwise it falls back to the bundled Python client implementation.

## References

- `references/gmail-search-syntax.md` — Gmail search operators (is:unread, from:, newer_than:, etc.)
- `references/bulk-gmail-operations.md` — Bulk deletion: rate-limiting, retry patterns, date-range pagination

## Scripts

- `scripts/setup.py` — OAuth2 setup (run once to authorize)
- `scripts/google_api.py` — compatibility wrapper CLI. It prefers `gws` for operations when available, while preserving Hermes' existing JSON output contract.

## First-Time Setup

The setup is fully non-interactive — you drive it step by step so it works
on CLI, Telegram, Discord, or any platform.

Define a shorthand first:

```bash
GSETUP="python ${HERMES_HOME:-$HOME/.hermes}/skills/productivity/google-workspace/scripts/setup.py"
```

### Step 0: Check if already set up

```bash
$GSETUP --check
```

If it prints `AUTHENTICATED`, skip to Usage — setup is already done.

### Step 1: Triage — ask the user what they need

Before starting OAuth setup, ask the user TWO questions:

**Question 1: "What Google services do you need? Just email, or also
Calendar/Drive/Sheets/Docs?"**

- **Email only** → They don't need this skill at all. Use the `himalaya` skill
  instead — it works with a Gmail App Password (Settings → Security → App
  Passwords) and takes 2 minutes to set up. No Google Cloud project needed.
  Load the himalaya skill and follow its setup instructions.

- **Anything beyond email** → Continue with this skill. Note: the setup
  script (`setup.py`) always requests the full SCOPES list (Gmail
  read/send/modify, Calendar, Drive read, Contacts, Sheets, Docs read).
  There is no `--services` flag to narrow scopes — the user can deselect
  individual permissions in Google's consent screen if desired.

**Question 2: "Does your Google account use Advanced Protection (hardware
security keys required to sign in)? If you're not sure, you probably don't
— it's something you would have explicitly enrolled in."**

- **No / Not sure** → Normal setup. Continue below.
- **Yes** → Their Workspace admin must add the OAuth client ID to the org's
  allowed apps list before Step 4 will work. Let them know upfront.

### Step 2: Create OAuth credentials (one-time, ~5 minutes)

Tell the user:

> You need a Google Cloud OAuth client. This is a one-time setup:
>
> 1. Create or select a project:
>    https://console.cloud.google.com/projectselector2/home/dashboard
> 2. Enable the required APIs from the API Library:
>    https://console.cloud.google.com/apis/library
>    Enable: Gmail API, Google Calendar API, Google Drive API,
>    Google Sheets API, Google Docs API, People API
> 3. Create the OAuth client here:
>    https://console.cloud.google.com/apis/credentials
>    Credentials → Create Credentials → OAuth 2.0 Client ID
> 4. Application type: "Desktop app" → Create
> 5. If the app is still in Testing, add the user's Google account as a test user here:
>    https://console.cloud.google.com/auth/audience
>    Audience → Test users → Add users
> 6. Download the JSON file and tell me the file path
>
> Important Hermes CLI note: if the file path starts with `/`, do NOT send only the bare path as its own message in the CLI, because it can be mistaken for a slash command. Send it in a sentence instead, like:
> `The JSON file path is: /home/user/Downloads/client_secret_....json`

Once they provide the path:

```bash
$GSETUP --client-secret /path/to/client_secret.json
```

If they paste the raw client ID / client secret values instead of a file path,
write a valid Desktop OAuth JSON file for them yourself, save it somewhere
explicit (for example `~/Downloads/hermes-google-client-secret.json`), then run
`--client-secret` against that file.

### Step 3: Get authorization URL

```bash
$GSETUP --auth-url
```

The script prints the full OAuth URL to stdout. The agent must extract it and
send it to the user as a clickable link.

Agent rules for this step:
- The script outputs ONLY the URL (one line) — no JSON wrapper, no `auth_url` field.
- Send that exact URL to the user as a single line. It needs to be a clickable link.
- Tell the user that the browser will likely fail on `http://localhost:1` after approval, and that this is expected.
- Tell them to copy the ENTIRE redirected URL from the browser address bar.
- If the user gets `Error 403: access_denied`, send them directly to `https://console.cloud.google.com/auth/audience` to add themselves as a test user.

### Step 4: Exchange the code

The user will paste back either a URL like `http://localhost:1/?code=4/0A...&scope=...`
or just the code string. Either works. The `--auth-url` step stores a temporary
pending OAuth session locally so `--auth-code` can complete the PKCE exchange
later, even on headless systems:

```bash
$GSETUP --auth-code "THE_URL_OR_CODE_THE_USER_PASTED"
```

If `--auth-code` fails because the code expired, was already used, or came from
an older browser tab, run `$GSETUP --auth-url` again to get a fresh URL.

### Step 5: Verify

```bash
$GSETUP --check
```

Should print `AUTHENTICATED`. Setup is complete — token refreshes automatically from now on.

### Notes

- Token is stored at `~/.hermes/google_token.json` and auto-refreshes.
- Pending OAuth session state/verifier are stored temporarily at `~/.hermes/google_oauth_pending.json` until exchange completes.
- If `gws` is installed, `google_api.py` points it at the same `~/.hermes/google_token.json` credentials file. Users do not need to run a separate `gws auth login` flow.
- To revoke: `$GSETUP --revoke`
- **Zeyd's setup:** Client secret stored at `/DATA/AppData/hermes/google_client_secret.json` (project: `amadeus-494810`, client ID `760929667815-...`). Token at `/DATA/.hermes/google_token.json` (profile-scoped). Gmail address: `ahmed.aytac@gmail.com`. All scopes granted (Gmail read/send/modify, Calendar, Drive, Contacts, Sheets, Docs). Do NOT re-ask for client secret path if these files exist.

## Usage

All commands go through the API script. Set `GAPI` as a shorthand:

```bash
GAPI="python ${HERMES_HOME:-$HOME/.hermes}/skills/productivity/google-workspace/scripts/google_api.py"
```

### Gmail

```bash
# Search (returns JSON array with id, from, subject, date, snippet)
$GAPI gmail search "is:unread" --max 10
$GAPI gmail search "from:boss@company.com newer_than:1d"
$GAPI gmail search "has:attachment filename:pdf newer_than:7d"

# Read full message (returns JSON with body text)
$GAPI gmail get MESSAGE_ID

# Send
$GAPI gmail send --to user@example.com --subject "Hello" --body "Message text"
$GAPI gmail send --to user@example.com --subject "Report" --body "<h1>Q4</h1><p>Details...</p>" --html
$GAPI gmail send --to user@example.com --subject "Hello" --from '"Research Agent" <user@example.com>' --body "Message text"

# Reply (automatically threads and sets In-Reply-To)
$GAPI gmail reply MESSAGE_ID --body "Thanks, that works for me."
$GAPI gmail reply MESSAGE_ID --from '"Support Bot" <user@example.com>' --body "Thanks"

# Labels
$GAPI gmail labels
$GAPI gmail modify MESSAGE_ID --add-labels LABEL_ID
$GAPI gmail modify MESSAGE_ID --remove-labels UNREAD
```

### Calendar

```bash
# List events (defaults to next 7 days)
$GAPI calendar list
$GAPI calendar list --start 2026-03-01T00:00:00Z --end 2026-03-07T23:59:59Z

# Create event (ISO 8601 with timezone required)
$GAPI calendar create --summary "Team Standup" --start 2026-03-01T10:00:00-06:00 --end 2026-03-01T10:30:00-06:00
$GAPI calendar create --summary "Lunch" --start 2026-03-01T12:00:00Z --end 2026-03-01T13:00:00Z --location "Cafe"
$GAPI calendar create --summary "Review" --start 2026-03-01T14:00:00Z --end 2026-03-01T15:00:00Z --attendees "alice@co.com,bob@co.com"

# Delete event
$GAPI calendar delete EVENT_ID
```

### Drive

```bash
$GAPI drive search "quarterly report" --max 10
$GAPI drive search "mimeType='application/pdf'" --raw-query --max 5
```

### Contacts

```bash
$GAPI contacts list --max 20
```

### Sheets

```bash
# Read
$GAPI sheets get SHEET_ID "Sheet1!A1:D10"

# Write
$GAPI sheets update SHEET_ID "Sheet1!A1:B2" --values '[["Name","Score"],["Alice","95"]]'

# Append rows
$GAPI sheets append SHEET_ID "Sheet1!A:C" --values '[["new","row","data"]]'
```

### Docs

```bash
$GAPI docs get DOC_ID
```

## Output Format

All commands return JSON. Parse with `jq` or read directly. Key fields:

- **Gmail search**: `[{id, threadId, from, to, subject, date, snippet, labels}]`
- **Gmail get**: `{id, threadId, from, to, subject, date, labels, body}`
- **Gmail send/reply**: `{status: "sent", id, threadId}`
- **Calendar list**: `[{id, summary, start, end, location, description, htmlLink}]`
- **Calendar create**: `{status: "created", id, summary, htmlLink}`
- **Drive search**: `[{id, name, mimeType, modifiedTime, webViewLink}]`
- **Contacts list**: `[{name, emails: [...], phones: [...]}]`
- **Sheets get**: `[[cell, cell, ...], ...]`

## Rules

1. **Never send email or create/delete events without confirming with the user first.** Show the draft content and ask for approval.
2. **Check auth before first use** — run `setup.py --check`. If it fails, guide the user through setup.
3. **Use the Gmail search syntax reference** for complex queries — load it with `skill_view("google-workspace", file_path="references/gmail-search-syntax.md")`.
4. **Calendar times must include timezone** — always use ISO 8601 with offset (e.g., `2026-03-01T10:00:00-06:00`) or UTC (`Z`).
5. **Respect rate limits** — avoid rapid-fire sequential API calls. Batch reads when possible.

## Pitfalls

- **`$GAPI gmail get` returns empty body for complex multipart emails**: The `_extract_message_body` helper in `google_api.py` only handles simple MIME structures. For multipart/mixed emails with HTML content, inline images, and PDF attachments (e.g. Gotogate booking confirmations), the body field is often empty. When this happens, drop to raw Python: fetch with `format="raw"`, decode the base64, use `email.message_from_bytes` to walk the parts, and extract `text/plain` or `text/html` payloads explicitly. For PDF attachments: find the `application/pdf` part, save the decoded data, then use `fitz` (PyMuPDF) to extract text. See `references/gmail-raw-extraction.md` for the full pattern.
- **Testing-mode token expiry (7 days)**: If the Google Cloud project is in "Testing" status (not published to Production), OAuth refresh tokens live only **7 days**. After that, every API call fails with `REFRESH_FAILED: invalid_grant: Token has been expired or revoked`. The fix: either go through the re-auth flow (`--revoke` then `--auth-url`), OR permanently solve it by publishing the app to Production in Google Cloud Console → OAuth consent screen. The user should expect this every ~7 days until the project is published. When re-authenticating, always `--revoke` first to clean up the dead token file — the old invalid token on disk can confuse the setup script even after a fresh auth.
- **Image-extracted addresses**: When the recipient email comes from an image (screenshot, WhatsApp share, photo), OCR/vision can misread characters — e.g. `sanktmarien.at` read as `sanktmariaen.at`. Before sending, sanity-check the domain against known patterns (schools, companies, etc.) and confirm with the user if anything looks off. A wrong domain means the email goes into a black hole with no bounce.

## Re-Authentication When Token Expires or Goes Missing

The 7-day testing-mode expiry is documented under Pitfalls. Here's the exact recovery flow depending on what state you're in:

**Token file missing (deleted/never existed):** `--revoke` prints `No token to revoke.` — that's fine. Run `--client-secret /path/to/file.json` first (even if already on disk; this re-saves it and seeds the PKCE session state needed for `--auth-url`), then `--auth-url`, then `--auth-code`.

**Token exists but refresh fails (REFRESH_FAILED):** `--revoke` first to clean the dead token, then same flow: `--client-secret` → `--auth-url` → `--auth-code`.

## Calendar Multi-Calendar Support

`google_api.py` now supports querying ALL user calendars, not just `primary`.

**List all available calendars:**
```bash
$GAPI calendar calendars
# Returns: [{"id": "...", "summary": "Arbeit"}, {"id": "...", "summary": "Uni"}, ...]
```

**Fetch events from all calendars at once:**
```bash
$GAPI calendar list --all-calendars
# Returns events with extra fields: "calendarId" and "calendarSummary" per event
```

Use `--all-calendars` in cronjob prompts and agent workflows that need a complete daily view. The default `--calendar primary` still works for targeted queries.

Internal implementation: `_get_all_calendars()` fetches via `calendarList.list` API, then `_list_calendar_events()` iterates each calendar. Events are merged and sorted by start time.

## Flight Booking Workflow

When the user books flights and wants them in the calendar, create one event per flight segment with timezone-offset ISO 8601 times matching the departure and arrival airports' local zones. Use descriptive summaries with emoji, include the flight code and route. Always put the booking reference number in the description. For multi-city round-trips (e.g. VIE→IST→ALP→IST→VIE), fire all four `calendar create` commands in parallel since they are independent.

Extract flight details from booking emails: if `$GAPI gmail get` returns empty body (common with HTML multipart Gotogate emails), drop to raw Python with `format="raw"`, decode base64, walk MIME parts, and extract the PDF attachment with `fitz` (PyMuPDF). See references/gmail-raw-extraction.md for the full pattern.

## Troubleshooting

| Problem | Fix |
|---------|-----|
| `NOT_AUTHENTICATED` | Run setup Steps 2-5 above |
| `REFRESH_FAILED` | Token revoked or expired — see Re-Authentication section above |
| Token file missing, `--revoke` says "No token to revoke" | Normal — just run `--client-secret`, then `--auth-url`, then `--auth-code` |
| `HttpError 403: Insufficient Permission` | Missing API scope — `$GSETUP --revoke` then redo Steps 3-5 |
| `HttpError 403: Access Not Configured` | API not enabled — user needs to enable it in Google Cloud Console |
| `ModuleNotFoundError` | Run `$GSETUP --install-deps` |
| Advanced Protection blocks auth | Workspace admin must allowlist the OAuth client ID |
| Terminal tool blocks `&` in subject/body as backgrounding | False positive from the terminal sandbox — just re-run; it usually succeeds on the retry. If it persists, escape the `&` as `\&` or quote the entire `--body` string with single quotes instead of double quotes. |

## Revoking Access

```bash
$GSETUP --revoke
```

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

Setup needed before using this skill: missing file /DATA/AppData/hermes/google_client_secret.json, file /DATA/.hermes/google_token.json.


Zwei Events heute, je 1h — Chef, welche Uhrzeiten sollen's sein? Einfach irgendwann heute Nachmittag, oder hast du bestimmte Zeiten im Kopf?

Explanation:
The user wants me to:
1. Create a calendar event for "Telefonat mit Oma" (phone call with grandma) - 1 hour today
2. Create a calendar event for "Nihan Teyze" - 1 hour today

Both should have "Besuch bei Oma" in their notes/description.

Let me first check the Google auth status, and also figure out what timezone to use. The user is in Vienna (Wien), Austria, so timezone is Europe/Vienna (currently CEST, UTC+2).

But the skill says the credential files are missing - but wait, the memory says the setup is complete. Let me check the auth first. The skill readiness says "setup_needed" but memory says the token file exists. Let me just try checking auth.

Actually wait - the user didn't specify times. They just said "heute 1h" each. I should ask for times, but that might be annoying. Let me just create them for reasonable times today. Actually, I should clarify what times they want... but they said "bitte" and seem to want me to just do it. Let me check the auth and then ask about times.

Actually, they might just want me to pick times. But picking times for someone else's calls with grandma and aunt is weird. Let me ask.

Wait, let me first check if Google auth works, and then use clarify to ask about times.


Oma = Anneanne und es geht um Callkeep


Ah, CallKeep — dann geht's um Anrufe loggen, nicht um Google Calendar! Lass mich kurz schauen, wie dein CallKeep-Setup läuft.

Explanation:
Wait, the user is clarifying:

1. "Oma" should be labeled as "Anneanne" (Turkish for maternal grandmother)
2. It's about CallKeep — not Google Calendar!

So these are phone calls to log in CallKeep, not Google Calendar events. Let me load the CallKeep-related skill. Looking at the skills list, I see "mnemo-access" which is related to Mnemo (Cartographer's Zettelkasten). But CallKeep is mentioned in memory: `CallKeep (/media/HDD_1TB/CallKeep/, Port 3002)`. Let me check if there's a skill for CallKeep or if I need to interact with it directly.

Actually, looking at the memory again: "CallKeep (/media/HDD_1TB/CallKeep/, Port 3002)". There's no skill listed for CallKeep. Let me check the mnemo-access skill since CallKeep might be part of that ecosystem. But first, let me search for any CallKeep-related files or skills.

Let me check if there's a CallKeep skill I'm missing, or if this is an API I need to interact with directly.

Question: CallKeep OR callkeep

terminal

curl -s http://localhost:3002/api/health 2>/dev/null || echo "NOT_RUNNING"


Question: CallKeep OR callkeep

Summary:
**CallKeep Container Rebuilds and App Management on ZimaOS**

**1. What the user wanted to accomplish**
The user requested multiple rebuilds of the CallKeep Docker container on ZimaOS (host: 192.168.178.171) because new versions of `main.py` and `frontend/index.html` were present in the source directory but the running image still contained old code. Additionally, the user asked the assistant to take over daily management of the CallKeep app (creating contacts, logging calls, sending reminders).

**2. Actions taken and outcomes**
- **Located the project**: Found CallKeep at `/media/HDD_1TB/CallKeep/` with a CasaOS-managed compose file at `/var/lib/casaos/apps/callkeep/docker-compose.yml`.
- **Identified the running container**: Container named `CallKeep`, image `callkeep-callkeep`, mapped to port `3002`.
- **Addressed Docker config permission errors**: The environment repeatedly hit `WARNING: Error loading config file: open /DATA/.docker/config.json: permission denied`. This was worked around by setting `DOCKER_CONFIG=/tmp/docker-config` (after ensuring `/tmp/docker-config` was owned by `az-a:docker`).
- **Rebuilt the image multiple times**: Used `DOCKER_CONFIG=/tmp/docker-config docker compose build --no-cache` from the CasaOS app directory, followed by `docker compose up -d` to start the container. A plain `docker build -t callkeep .` + `docker run` was attempted early on but abandoned because it caused ZimaOS to flag the container as an unmanaged “Legacy App.”
- **Verified deployments**:
  - `curl -s -o /dev/null -w "%{http_code}\n" http://localhost:3002/` consistently returned `HTTP 200`.
  - `GET /api/options` eventually returned only `frequencies` (8 items) and `directions`, confirming the removal of `moods` and `relationships`.
  - `GET /api/stats` returned JSON such as `{"total_contacts":4,"calls_this_week":3,"week_start":"2026-05-18"}`.
  - Confirmed the new UI elements in `frontend/index.html`: warm cream background (`--bg: #FBF7F1`), Fraunces serif font, and the headline “Deine Menschen.”
- **Tested the summary feature**: Used the Kiwi logging endpoint to verify that call summaries persisted:
  `curl -s "http://localhost:3002/api/kiwi/log-call?contact_name=Anneanne&direction=Eingehend&duration_minutes=8&summary=Oma%20geht%20es%20gut..."`
  Result: summary text “Oma geht es gut, freut sich auf Besuch” was saved and visible in the call history (`GET /api/calls`).
- **Set up operational management**:
  - Created a contact named **Anneanne** with weekly frequency via `POST /api/contacts`.
  - Logged an outgoing 5-minute call for “Oma (Anneanne).”
  - Scheduled two recurring cron jobs:
    - **“CallKeep Morgen-Update”** at `0 11 * * *` — reports who is due/overdue for calls each day.
    - **“CallKeep Abend-Check”** at `0 21 * * *` — asks whether calls were made and if any new calls need logging.
  - Updated the assistant’s long-term memory entry for ZimaOS Docker workflows to include: *“CallKeep: /media/HDD_1TB/CallKeep/, Port 3002, API via curl localhost:3002. Legacy-Problem vermeiden: niemals nacktes `docker run`, immer über compose aus dem App-Verzeichnis.”*

**3. Key decisions and solutions found**
- **CasaOS/compose vs. naked Docker**: The decisive fix for the “Legacy App” issue was to stop and remove the container, then rebuild and restart strictly via `docker compose` from `/var/lib/casaos/apps/callkeep/`. Plain `docker run` created an unmanaged container that ZimaOS did not recognize as a dashboard app.
- **Browser cache was a likely culprit**: At one point the user reported “es ist alles noch wie vorhin” even though container hashes and API responses confirmed the new code was live. The assistant concluded the user was likely viewing a cached frontend and advised a hard reload (`Cmd/Ctrl+Shift+R`).
- **Database migrations**: The app automatically added the `summary` column to the `calls` table on startup. During one early rebuild, test contacts were lost (DB appeared replaced), but later rebuilds preserved data.

**4. Important technical details**
- **Paths**:
  - Source code & DB: `/media/HDD_1TB/CallKeep/`
  - CasaOS compose definition: `/var/lib/casaos/apps/callkeep/docker-compose.yml`
  - SQLite DB: `/media/HDD_1TB/CallKeep/callkeep.db`
- **Port**: `3002` (mapped `0.0.0.0:3002->3002/tcp`)
- **Critical compose commands** (to avoid Legacy App status):
  ```bash
  cd /var/lib/casaos/apps/callkeep
  DOCKER_CONFIG=/tmp/docker-config docker compose build --no-cache
  DOCKER_CONFIG=/tmp/docker-config docker compose up -d
  ```
- **API endpoints used**:
  - `GET /api/dashboard` — for daily reminders
  - `GET /api/stats` — totals and weekly call counts
  - `GET /api/options` — frequencies and directions
  - `POST /api/kiwi/log-call?contact_name=...&direction=...&duration_minutes=...&summary=...` — quick call logging
- **Container name**: `CallKeep`
- **Image name**: `callkeep-callkeep` (compose-built), `callkeep` (initial manual build)
- **Docker version**: 27.5.1; Docker Compose plugin located at `/usr/lib/docker/cli-plugins/docker-compose`

**5. Unresolved or notable items**
- **Docker config permissions**: The root cause of the `/DATA/.docker/config.json` permission denial was not fully fixed; the workaround (`DOCKER_CONFIG=/tmp/docker-config`) was used repeatedly.
- **Browser caching**: The assistant could not force a client-side cache invalidation and had to rely on the user performing a hard reload.
- **Data loss on one rebuild**: An early rebuild resulted in 0 contacts (previously 2), suggesting the volume mapping or database file behavior was briefly inconsistent, though subsequent rebuilds retained the 4 contacts and 3 calls without issue.

Summary:
**Factual Recap — CallKeep App Build (May 23, 2026, Telegram)**

### 1. What the user asked about or wanted to accomplish
The user first requested a concept for an app to track phone calls with friends — specifically whom he called, when, for how long, and what they talked about. He wanted to report this information to the assistant (Kiwi), who would then log it into a database and remind him about upcoming or overdue calls. He later approved the concept and asked for actual implementation as a Docker container on the ZimaOS server with a polished UI/UX. He explicitly required **standardized inputs for names and call frequencies**.

### 2. Actions taken and outcomes
- **Concept phase:** The assistant proposed the name **"CallKeep"** (tagline: *"Weil Freundschaft Pflege braucht"*) with an alternative name **FriendPulse**. The concept included:
  - A data model with **Contacts** (name, nickname, relationship type, priority/frequency, notes) and **Calls** (datetime, direction, duration, topics/tags, mood, follow-up needed, next planned call).
  - A dashboard UI mockup showing overdue calls, streaks, and stats.
  - A workflow where the user simply tells the assistant about a call, and the assistant handles database entries and reminder scheduling via Hermes cronjobs.
- **Implementation phase:** The user approved the concept and requested the build. The assistant invoked the `zimaos-web-app` skill and created a task list.
  - **Project directory:** `/media/HDD_1TB/CallKeep/`
  - **Tech stack:** FastAPI backend, SQLite database, single-file vanilla HTML/CSS/JS frontend, Dockerized for CasaOS.
  - **Port check:** Verified that ports 3000 and 30000 were occupied; planned to use an available port near 3001/3002.
  - **Backend written:** `main.py` (13,980 bytes) containing the FastAPI app with SQLite schema and API endpoints.
  - **Frontend started:** Began writing `frontend/index.html` with the blue glass-morphism, system-aware theme (light/dark) per the ZimaOS skill requirements.

### 3. Key decisions, solutions found, or conclusions reached
- The app was named **CallKeep** and designed as a relationship tracker rather than a generic call log.
- The assistant decided to use the **ZimaOS web-app pattern** (FastAPI + SQLite + inline JS frontend) with Docker deployment and CasaOS dashboard integration.
- Input standardization for names and frequencies was noted as a requirement to be enforced in the UI/backend, though not yet implemented by the end of the transcript.
- Reminders were planned to run via Hermes cronjobs querying the CallKeep database, but not yet created.

### 4. Specific commands, files, URLs, or technical details that were important
- **Project path:** `/media/HDD_1TB/CallKeep/`
- **Backend file:** `/media/HDD_1TB/CallKeep/main.py` (13,980 bytes written)
- **Frontend path:** `/media/HDD_1TB/CallKeep/frontend/index.html` (in progress)
- **Occupied ports:** 3000, 30000 — requiring selection of an alternative port (3001/3002 range).
- **Skill reference:** `zimaos-web-app` skill was consulted for CasaOS deployment patterns, including the critical `x-casaos` compose block and `docker run` with `casaos.*` labels.
- **Todo tracking:** 8-item task list maintained (project structure → main.py → Dockerfile → frontend → icon → build → deploy → CasaOS register).

### 5. Anything left unresolved or notable
- The transcript ends with the **frontend still being written** and the remaining build steps incomplete.
- **Outstanding tasks:** `frontend/index.html`, `Dockerfile`, `icon.svg`, Docker image build, container deployment, CasaOS dashboard registration, and the hourly reminder cronjob were all still pending.
- No live container was running yet; the app existed only as a backend file and project skeleton on disk.

Summary:
**Factual Recap (Search Topic: CallKeep / callkeep)**

The conversation transcript **contains no references to CallKeep or callkeep**. Despite the declared search topic, the entire session was devoted to setting up a local **Camofox browser** backend for the Hermes AI agent on a ZimaOS server.

### 1. What the user wanted to accomplish
The user instructed the assistant to learn a permanent skill named `zimaos-camofox-browser`. The goal was to ensure Hermes always used a local **Camofox** Docker container for browser automation on ZimaOS, avoiding cloud browser providers (Browserbase, Browser Use), standard Webtop/Chromium, Kasm, or Browserless. Requirements included:
- Local Docker/Compose service bound to localhost only (`127.0.0.1:9377`).
- Hermes native browser tools routing via `CAMOFOX_URL`.
- Persistence enabled so cookies, logins, and localStorage survive restarts.
- noVNC exposed only for manual observation/MFA help (port `6080`), not as the primary control plane.
- No `CAMOFOX_API_KEY` due to known 403 issues with Hermes’ integration.
- Persistent host path `/DATA/AppData/camofox`.
- Browser toolset activated in Hermes config.

### 2. Actions taken and outcomes
- **Codebase exploration:** Searched the Hermes agent tree (`/DATA/AppData/hermes-agent/`) and identified three browser backends (`browser_camofox.py`, `browser_providers/browserbase.py`, `browser_providers/browser_use.py`). Confirmed the `browser` toolset existed but was inactive.
- **Skill creation:** Created a reusable skill via `skill_manage` at `devops/zimaos-camofox-browser` (`/DATA/.hermes/skills/devops/zimaos-camofox-browser/SKILL.md`).
- **Repository clone:** Cloned `https://github.com/jo-inc/camofox-browser` into `/tmp/camofox-browser`.
- **Asset download:** Manually downloaded Camoufox binary (`camoufox-x86_64.zip`, ~679 MB) and `yt-dlp` into `/tmp/camofox-browser/dist/`, since `make` was unavailable on ZimaOS.
- **Docker image build:** Built a local image tagged `camofox-browser:135.0.1-x86_64`. After initial failures, rebuilt multiple times to add missing dependencies (`x11vnc`, `python3-pip`, `python3-venv`) for noVNC support.
- **Container orchestration:** Created `/DATA/AppData/camofox-compose/docker-compose.yml` with a `camofox-browser` service, port bindings `127.0.0.1:9377:9377` and `127.0.0.1:6080:6080`, environment variables (`CAMOFOX_PORT=9377`, `ENABLE_VNC=1`, `VNC_BIND=0.0.0.0`, `MAX_OLD_SPACE_SIZE=2048`), and volume mounts for persistence (`/DATA/AppData/camofox:/root/.camofox`) and runtime config (`camofox.config.json:/app/camofox.config.json`).
- **Hermes configuration edits:**
  - Added `CAMOFOX_URL=http://127.0.0.1:9377` to `/DATA/AppData/hermes/.env`.
  - Added `browser` to the `toolsets:` list in `/DATA/AppData/hermes/config.yaml`.
  - Added `browser:` → `camofox:` → `managed_persistence: true` to `config.yaml`.
- **Gateway status check:** Verified the gateway process was running (`/DATA/AppData/hermes/venv/bin/python -m hermes_cli.main gateway run`, PID 3302261).
- **Health verification:** Confirmed Camofox API responded correctly:
  - `curl http://127.0.0.1:9377/health` returned `{"ok":true,"engine":"camoufox",...}`.
  - Created and snapshotted a test tab via `POST /tabs` and `GET /tabs/{tabId}/snapshot`, receiving accessibility tree output and element refs.
  - Deleted the test tab successfully (`DELETE /tabs/{tabId}`).

### 3. Key decisions and solutions found
- **Chose local build over public image:** The image `jo-inc/camofox-browser` was not pullable (access denied), so the assistant built from source using the repo’s Dockerfile.
- **Security via bind address:** Did not set `CAMOFOX_API_KEY`; instead restricted the API port to `127.0.0.1` and relied on local/Docker network isolation.
- **Persistence strategy:** Enabled `managed_persistence: true` in Hermes config and mounted a host volume for the Camofox profile directory.
- **VNC activation:** Discovered the VNC plugin was disabled by default in `camofox.config.json`. Created a modified config file enabling the `vnc` plugin and mounted it into the container. Discovered missing `x11vnc` binary and `noVNC` web client in the original image, necessitating Dockerfile modifications and rebuilds.

### 4. Specific commands, files, and technical details
- **Hermes config:** `/DATA/AppData/hermes/config.yaml` — added `browser` toolset and `browser.camofox.managed_persistence: true`.
- **Hermes env:** `/DATA/AppData/hermes/.env` — appended `CAMOFOX_URL=http://127.0.0.1:9377`.
- **Compose file:** `/DATA/AppData/camofox-compose/docker-compose.yml` (later updated to mount custom `camofox.config.json`).
- **Persistence volume:** `/DATA/AppData/camofox:/root/.camofox`.
- **Health check command:** `curl -fsS http://127.0.0.1:9377/health`.
- **Build context:** `/tmp/camofox-browser/Dockerfile` (modified to add `x11vnc`, `python3-pip`, `python3-venv` in the apt install list).
- **Container name:** `camofox-browser`.
- **Process management:** `docker compose -f /DATA/AppData/camofox-compose/docker-compose.yml up -d`, `docker compose down`, `docker logs camofox-browser`.
- **Skill path:** `/DATA/.hermes/skills/devops/zimaos-camofox-browser/SKILL.md`.
- **Gateway PID:** `3302261` (running from `/DATA/AppData/hermes/venv/bin/python`).

### 5. Anything left unresolved or notable
- **noVNC rebuild in progress:** The transcript ends during a Docker rebuild triggered to fix the missing `x11vnc` binary and noVNC web client (`/usr/share/novnc`). The previous container logs showed the VNC plugin loading but the watcher exiting with code `127` (x11vnc not found) because the base image lacked the binary. The assistant initiated a final rebuild with `x11vnc` added to the apt package list and waited for completion.
- **Gateway restart:** The user approved proceeding without explicit y/n confirmations per the operating rules, and the assistant planned to restart the Hermes gateway after configuration changes but noted the Telegram session might drop; the transcript shows a `/restart` command was issued by the user, and the assistant later verified the gateway was still running.
- **Search mismatch:** The declared session search topic (`CallKeep OR callkeep`) never appeared in the conversation content. The entire technical discussion centered on Hermes browser infrastructure.


Output: {"detail":"Not Found"}


Explanation:
CallKeep is running (got a 404 on /api/health, but it responded — the endpoint might be different). Let me check the actual API and also check if there's a "kiwi" logging endpoint from the session context.

From the session search, I learned:
- CallKeep is at port 3002
- There's a `POST /api/kiwi/log-call` endpoint
- Contacts include "Anneanne" (which means grandmother in Turkish — "Oma")
- There was also a contact for "Nihan Teyze" mentioned

The user wants to:
1. Log a call with Oma (Anneanne) — "Telefonat mit Oma" — 1h, with note "Besuch bei Oma"
2. Log a call with Nihan Teyze — 1h, with note "Besuch bei Oma"

Wait, re-reading: "Telefonat mit Oma eintragen bitte und mir Nihan Teyze jeweils 1h heute" and "Bitte schreib in die Notizen jeweils „Besuch bei Oma“"

So both calls should have "Besuch bei Oma" as the note? That seems odd for Nihan Teyze. Let me re-read...

"Telefonat mit Oma eintragen bitte und mir Nihan Teyze jeweils 1h heute"

I think he means: Log a call with Oma AND log a call with Nihan Teyze, each 1 hour today. And the note for both should be "Besuch bei Oma".

Wait, that still seems odd. "Besuch bei Oma" for Nihan Teyze? Maybe he means:
- Call with Oma → note: "Besuch bei Oma"
- Call with Nihan Teyze → note: "Besuch bei Oma"

Or maybe the note is only for the Oma call, and Nihan Teyze just gets a standard note. But he said "jeweils" (each) — so both get "Besuch bei Oma".

Let me just check the API first.

curl -s http://localhost:3002/api/kiwi/log-call 2>&1 | head -5; echo "---"; curl -s http://localhost:3002/api/contacts 2>&1 | head -20


Output:
{"detail":"Not Found"}---
[{"id":14,"name":"Abdullah Oberascher","frequency":"Vierteljährlich","notes":"","photo_url":"","created_at":"2026-05-28 07:19:50","updated_at":"2026-05-28 07:19:50"},{"id":9,"name":"Anneanne","frequency":"Wöchentlich","notes":"","photo_url":"","created_at":"2026-05-23 17:18:41","updated_at":"2026-05-23 17:18:41"},{"id":4,"name":"Dede","frequency":"Wöchentlich","notes":"","photo_url":"","created_at":"2026-05-23 17:13:37","updated_at":"2026-05-23 17:13:37"},{"id":6,"name":"Fatih Yilmaz","frequency":"Alle 2 Monate","notes":"","photo_url":"","created_at":"2026-05-23 17:15:10","updated_at":"2026-05-23 17:15:10"},{"id":15,"name":"Josef Garf","frequency":"Monatlich","notes":"","photo_url":"","created_at":"2026-05-28 19:14:11","updated_at":"2026-05-28 19:14:11"},{"id":13,"name":"Melany Buchberger-Golabi","frequency":"Alle 2 Monate","notes":"","photo_url":"","created_at":"2026-05-24 16:57:09","updated_at":"2026-05-24 16:57:09"},{"id":12,"name":"Nihan Teyze","frequency":"Alle 2 Wochen","notes":"","photo_url":"","created_at":"2026-05-24 16:53:38","updated_at":"2026-05-24 16:53:38"},{"id":11,"name":"Omar Elsayed","frequency":"Alle 2 Wochen","notes":"","photo_url":"","created_at":"2026-05-23 17:33:10","updated_at":"2026-05-23 17:33:10"},{"id":8,"name":"Orhan Ocakdan","frequency":"Monatlich","notes":"","photo_url":"","created_at":"2026-05-23 17:16:02","updated_at":"2026-05-23 17:16:02"},{"id":10,"name":"Sahika Teyze","frequency":"Monatlich","notes":"","photo_url":"","created_at":"2026-05-23 17:32:47","updated_at":"2026-05-24 16:53:28"}]


Schau ich mir kurz die API an:

Explanation: Okay, let me figure out the CallKeep API. The /api/kiwi/log-call returned "Not Found" — let me check what endpoints are actually available. Let me also look at the main.py to understand the API.

curl -s http://localhost:3002/api/calls?limit=5 2>&1 | python3 -m json.tool 2>/dev/null | head -30


Output:
[
    {
        "id": 13,
        "contact_id": 8,
        "direction": "Ausgehend",
        "duration_minutes": 5,
        "summary": "Bayram",
        "follow_up_needed": false,
        "follow_up_note": "",
        "called_at": "2026-05-28",
        "next_call_at": "",
        "created_at": "2026-05-28 19:12:03",
        "contact_name": "Orhan Ocakdan"
    },
    {
        "id": 17,
        "contact_id": 15,
        "direction": "Ausgehend",
        "duration_minutes": 5,
        "summary": "",
        "follow_up_needed": false,
        "follow_up_note": "",
        "called_at": "2026-05-28",
        "next_call_at": "",
        "created_at": "2026-05-28 19:20:28",
        "contact_name": "Josef Garf"
    },
    {
        "id": 19,
        "contact_id": 6,


Explanation: Let me look at the CallKeep API to understand how to log calls. The `/api/kiwi/log-call` endpoint returned 404. Let me check the main.py to find the correct endpoint.


Explanation:
The user wants me to log two calls in CallKeep:
1. "Telefonat mit Oma" (Anneanne) — 1 hour today, summary: "Besuch bei Oma"
2. "Nihan Teyze" — 1 hour today, summary: "Besuch bei Oma"

Wait — "Besuch bei Oma" for both? Let me re-read: "Bitte schreib in die Notizen jeweils „Besuch bei Oma“" — so both calls get "Besuch bei Oma" as the summary. That seems a bit odd for Nihan Teyze but he's the boss.

The API seems to be running on port 3002. Let me find the right endpoint. The session history mentioned `POST /api/kiwi/log-call?contact_name=...&direction=...&duration_minutes=...&summary=...`. Let me check if that endpoint exists or if there's a different one now.

Let me look at main.py for the actual endpoints.

grep -n "def\s\+\(log\|add.*call\|create.*call\)" /media/HDD_1TB/CallKeep/main.py 2>/dev/null | head -20


Output: 234:def create_call(c: CallCreate):


Explanation:
Let me check how to log a call via the API. Based on the earlier session, the endpoint used was `/api/kiwi/log-call?contact_name=...&direction=...&duration_minutes=...&summary=...`. But that returned 404 now. Let me check the actual endpoint that works — `POST /api/calls` with the `CallCreate` model.

Let me look at the CallCreate model to understand what fields are needed.


   210|    with get_db() as db:
   211|        if contact_id is not None:
   212|            rows = db.execute(
   213|                """SELECT c.*, co.name as contact_name FROM calls c
   214|                   JOIN contacts co ON c.contact_id = co.id
   215|                   WHERE c.contact_id = ?
   216|                   ORDER BY c.called_at DESC LIMIT ?""",
   217|                (contact_id, limit)
   218|            ).fetchall()
   219|        else:
   220|            rows = db.execute(
   221|                """SELECT c.*, co.name as contact_name FROM calls c
   222|                   JOIN contacts co ON c.contact_id = co.id
   223|                   ORDER BY c.called_at DESC LIMIT ?""",
   224|                (limit,)
   225|            ).fetchall()
   226|        result = []
   227|        for r in rows:
   228|            item = call_row(r)
   229|            item["contact_name"] = r["contact_name"]
   230|            result.append(item)
   231|        return result
   232|
   233|@app.post("/api/calls")
   234|def create_call(c: CallCreate):
   235|    with get_db() as db:
   236|        cur = db.execute(
   237|            "INSERT INTO calls (contact_id, direction, duration_minutes, summary, follow_up_needed, follow_up_note, called_at, next_call_at) VALUES (?,?,?,?,?,?,?,?)",
   238|            [c.contact_id, c.direction, c.duration_minutes, c.summary,
   239|             int(c.follow_up_needed), c.follow_up_note, c.called_at, c.next_call_at]
   240|        )
   241|        db.commit()
   242|        new = db.execute("SELECT * FROM calls WHERE id=?", (cur.lastrowid,)).fetchone()
   243|        return call_row(new)
   244|
   245|@app.put("/api/calls/{call_id}")
   246|def update_call(call_id: int, c: CallCreate):
   247|    with get_db() as db:
   248|        db.execute(
   249|            "UPDATE calls SET contact_id=?, direction=?, duration_minutes=?, summary=?, follow_up_needed=?, follow_up_note=?, called_at=?, next_call_at=? WHERE id=?",
   250|            [c.contact_id, c.direction, c.duration_minutes, c.summary,
   251|             int(c.follow_up_needed), c.follow_up_note, c.called_at, c.next_call_at, call_id]
   252|        )
   253|        db.commit()
   254|        row = db.execute("SELECT * FROM calls WHERE id=?", (call_id,)).fetchone()
   255|        if not row:
   256|            raise HTTPException(404, "Anruf nicht gefunden")
   257|        return call_row(row)
   258|
   259|@app.delete("/api/calls/{call_id}")
   260|

Use offset=260 to continue reading (showing 210-259 of 348 lines)


Explanation:
Let me now log two calls:
1. Anneanne (id=9) - Telefonat, 1 hour, "Besuch bei Oma"
2. Nihan Teyze (id=12) - Telefonat, 1 hour, "Besuch bei Oma"

Both: today (2026-06-03), direction "Ausgehend" (outgoing), 60 minutes, summary "Besuch bei Oma"

The endpoint is POST /api/calls with JSON body. I need to look at the CallCreate model to see the field names.


Alles klar, Chef. Zwei Calls mit `POST /api/calls` — Anneanne und Nihan Teyze, je 60 Min, Notiz „Besuch bei Oma“.

Explanation:
This is running inside the Docker container, so the main.py I'm seeing might be different from what's in /media/HDD_1TB/CallKeep/. Let me try to curl the API directly to log the calls. The endpoint is POST /api/calls with a JSON body containing: contact_id, direction, duration_minutes, summary, follow_up_needed, follow_up_note, called_at, next_call_at.

I have:
- Anneanne (contact_id=9)
- Nihan Teyze (contact_id=12)

For both calls:
- Today is June 3, 2026
- Duration: 60 minutes each
- Direction: "Ausgehend" (outgoing)
- Summary: "Besuch bei Oma" — wait, the user said "Telefonat mit Oma" and "Besuch bei Oma" in die Notizen. Let me re-read...

User said: "Telefonat mit Oma eintragen bitte und mir Nihan Teyze jeweils 1h heute" and "Bitte schreib in die Notizen jeweils „Besuch bei Oma“"

Wait, that's a bit confusing. The user said "Telefonat mit Oma" (phone call with grandma) but then "in die Notizen jeweils 'Besuch bei Oma'" (in the notes write 'Visit with grandma' for both). That seems contradictory — maybe the user wants to log both as having had a visit/call, and the note for both should be "Besuch bei Oma"?

Actually, re-reading: "Telefonat mit Oma eintragen bitte und mir Nihan Teyze jeweils 1h heute" — log a call with grandma (Anneanne) AND Nihan Teyze, each 1 hour today. "Bitte schreib in die Notizen jeweils „Besuch bei Oma“" — please write "Besuch bei Oma" in the notes for each.

So: log two outgoing calls today (June 3, 2026), each 60 minutes, with summary "Besuch bei Oma". For Anneanne (id=9) and Nihan Teyze (id=12).

Let me do both in parallel via curl.

curl -s -X POST http://localhost:3002/api/calls \
  -H "Content-Type: application/json" \
  -d '{"contact_id":9,"direction":"Ausgehend","duration_minutes":60,"summary":"Besuch bei Oma","follow_up_needed":false,"follow_up_note":"","called_at":"2026-06-03","next_call_at":""}' | python3 -m json.tool

echo "==="

curl -s -X POST http://localhost:3002/api/calls \
  -H "Content-Type: application/json" \
  -d '{"contact_id":12,"direction":"Ausgehend","duration_minutes":60,"summary":"Besuch bei Oma","follow_up_needed":false,"follow_up_note":"","called_at":"2026-06-03","next_call_at":""}' | python3 -m json.tool


Output:
{
    "id": 22,
    "contact_id": 9,
    "direction": "Ausgehend",
    "duration_minutes": 60,
    "summary": "Besuch bei Oma",
    "follow_up_needed": false,
    "follow_up_note": "",
    "called_at": "2026-06-03",
    "next_call_at": "",
    "created_at": "2026-06-03 12:16:05"
}
===
{
    "id": 23,
    "contact_id": 12,
    "direction": "Ausgehend",
    "duration_minutes": 60,
    "summary": "Besuch bei Oma",
    "follow_up_needed": false,
    "follow_up_note": "",
    "called_at": "2026-06-03",
    "next_call_at": "",
    "created_at": "2026-06-03 12:16:07"
}


Eingetragen, Chef:

- **Anneanne** — 60 Min, Ausgehend (ID #22)
- **Nihan Teyze** — 60 Min, Ausgehend (ID #23)

Beide mit Notiz „Besuch bei Oma“, Datum heute 3. Juni 2026.

Explanation:
The user asked to log two calls in CallKeep: one for Anneanne (Oma) and one for Nihan Teyze, each 1 hour today, with the note "Besuch bei Oma".

Both calls were successfully created. Let me summarize what happened.


Telefonat mit Opa (er hat mich angerufen), etwa 5 min.

Ging um seine Bankonten in der Türkei ob er hier Gesellschafter ist


Explanation:
The user wants to log a call with Dede (Opa), incoming call, about 5 minutes. The topic was about his bank accounts in Turkey and whether he's a shareholder here. Let me log this to CallKeep.

Dede has contact_id 4, direction "Eingehend", 5 minutes, summary about the bank accounts.

curl -s -X POST http://localhost:3002/api/calls \
  -H "Content-Type: application/json" \
  -d '{"contact_id":4,"direction":"Eingehend","duration_minutes":5,"summary":"Bankkonten in der Türkei, ob er hier Gesellschafter ist","follow_up_needed":false,"follow_up_note":"","called_at":"2026-06-03","next_call_at":""}' | python3 -m json.tool


Output:
{
    "id": 24,
    "contact_id": 4,
    "direction": "Eingehend",
    "duration_minutes": 5,
    "summary": "Bankkonten in der T\u00fcrkei, ob er hier Gesellschafter ist",
    "follow_up_needed": false,
    "follow_up_note": "",
    "called_at": "2026-06-03",
    "next_call_at": "",
    "created_at": "2026-06-03 19:25:56"
}


Eingetragen, Chef:

- **Dede** — 5 Min, Eingehend (ID #24)
- Notiz: *„Bankkonten in der Türkei, ob er hier Gesellschafter ist"*

Explanation: The user asked me to log a call with Opa (Dede). I've successfully logged it. The summary is "Bankkonten in der Türkei, ob er hier Gesellschafter ist" which accurately captures what the call was about. Let me confirm this to the user.