sovereign-agent-logbook

--- name: sovereign-agent-logbook description: Use whenever an agent is about to take an action with side effects (write, send, pay, delete, change configuration) and a Sovereign Agent Logbook MCP server is connected. Teaches when to log, what to put in each field, when to ask a human, and what must never go in the log. version: 0.1.1 metadata: openclaw: homepage: https://github.com/Delfen-com/sovereign-agent-logbook requires: bins: - sovereign-agent-logbook install: - kind: uv package: sovereign-agent-logbook bins: [sovereign-agent-logbook, logbook] envVars: - name: LOGBOOK_PATH required: false description: Path of the logbook file. Default ./logbook.jsonl. --- # Sovereign Agent Logbook — how to use it well You have a tamper-evident logbook available as MCP tools: `log_event`, `verify_logbook`, `recent_events`, `export_logbook`. This skill tells you how to use them so that a human can later reconstruct what you did and why. ## Setup The tools come from a local MCP server, installed from PyPI as `sovereign-agent-logbook` (Python 3.11+). Connect it to your agent as a stdio server with the command `sovereign-agent-logbook`. In OpenClaw: ```bash openclaw mcp add logbook --command sovereign-agent-logbook openclaw mcp doctor logbook --probe ``` The server makes no network calls of its own. The log is a file on your own machine (`LOGBOOK_PATH`). ## The one rule **Log before and after every action that changes something outside this conversation.** Writing a file, sending a message, making a payment, changing a record, deleting anything, calling an external system that does something. Reading is optional to log; acting is not. Never skip a log because the action feels small. The small ones are the ones people ask about later. ## Before the action Call `log_event` with: - `agent_id`: your stable name as the operator configured it (e.g. `invoice-bot`). - `model`: the model you are running on, as precisely as you know it. - `action`: the tool or operation, e.g. `send_email`, `update_crm_record`. - `arguments`: the actual arguments. Secrets are masked automatically, but do not put passwords, tokens or personal identifiers anywhere else. - `reason`: one plain sentence a colleague would accept. "Invoice 2026-118 matches purchase order 4471 and is within the auto-approval limit." - `approval`: - `"human"` + `approved_by` if a person said yes in this conversation. - `"auto"` + `approval_rule` if a configured rule allows it (name the rule). - `"none"` if neither — and then **stop and ask a human** unless the operator has explicitly allowed unlogged autonomous actions. - `outcome`: `"pending"`. - `run_id`: the same id for every event in this task, so they group. ## After the action Call `log_event` again with the same `run_id` and `action`, now with `outcome` set to `"success"`, `"error"` or `"escalated"`, and `result_summary` in one line. If it errored, say what the error was. ## When to escalate Set `outcome: "escalated"` and hand over to a human when: - an action would move money, delete data, or contact someone outside the organisation, and no human has approved it in this conversation; - an argument looks wrong (an amount an order of magnitude off, an address you have never seen); - the previous event for this `run_id` errored and you would be retrying something with side effects. ## What must never be in the log - Passwords, API keys, tokens (masked if under a recognised key; do not rely on it — leave them out). - Full personal identifiers (BSN, IBAN, passport numbers) in `reason` or `result_summary`. Refer to "the customer's IBAN on file" instead. - Free-text copies of documents or emails. Log that you read them and their reference, not their content. ## Checking yourself At the end of a task, call `verify_logbook`. If it reports `ok: false`, tell the human immediately — it means the file was altered outside your control, which is itself an incident. ## Works with any model This skill contains no model-specific instructions. It has been written to read the same for Mistral, GPT-NL, Llama, Claude, GPT or any other model that can follow instructions and call tools.

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