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agistack

log

by AGIstack · GitHub ↗ · v1.1.1 · MIT-0
cross-platform ✓ Security Clean
464
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Install in OpenClaw
/install log
Description
A privacy-first, local-first provenance protocol for agent workflows. Emits structured audit records for important decisions, tool calls, state changes, and...
README (SKILL.md)

LOG: Local-First Provenance Protocol

I. Purpose

Log standardizes how an agent emits structured provenance records for important workflow events. It does not perform persistence, encryption, approval handling, or immutability enforcement by itself. Those controls belong to the host environment.

Use this skill when a workflow needs:

  • audit-ready activity records
  • debugging traces for failures or retries
  • source-aware decision summaries
  • host-controlled approval gates for high-impact actions

Do not use this skill to:

  • record hidden chain-of-thought
  • store secrets, credentials, or tokens
  • dump raw private documents, attachments, or long transcripts
  • claim storage guarantees the host has not implemented

II. Event Triggers

Emit a log entry only for important workflow events, such as:

  1. tool or API execution
  2. significant decision or state change
  3. task completion, retry, refusal, or failure
  4. high-impact action that may require host approval

Do not emit logs for every minor conversational turn.

III. Security & Redaction Rules

All emitted records must be minimal, factual, and privacy-safe.

Rules:

  • never include passwords, API keys, bearer tokens, cookies, session IDs, or secrets
  • replace sensitive values with [SECRET_REDACTED]
  • never include hidden chain-of-thought or full internal reasoning traces
  • prefer summaries over raw content
  • when sensitive personal data is involved, log only the category of data unless explicitly required and authorized

IV. Approval Signaling

For a high-impact action, emit a log entry with:

  • "approval_required": true

The host environment may use this signal to pause execution until an approval event, user confirmation, or policy check is completed.

Log emits the signal only. The host environment decides whether to block, continue, or reject execution.

V. Source Provenance

When relevant, include source references that explain what the action or decision was based on.

Examples:

  • user instruction
  • local file name
  • tool result identifier
  • API response label
  • workflow state snapshot

Keep source references concise and safe. Do not include sensitive raw content.

VI. Output Contract

When logging is required, output exactly one structured record in a fenced json block prefixed by [LOG_ENTRY].

VII. Required Schema

Use this exact JSON structure:

[LOG_ENTRY]
{
  "timestamp": "YYYY-MM-DDTHH:MM:SSZ",
  "event_type": "observation | decision | execution | state_change | completion | error | refusal",
  "status": "success | failed | pending | intercepted | skipped",
  "actor": "assistant | skill_name | workflow_name",
  "summary": "Concise factual description of what happened",
  "decision_basis": [
    "Key fact, constraint, or condition",
    "Key fact, constraint, or condition"
  ],
  "source_references": [
    "user_prompt",
    "local:file_a.md",
    "tool_result:search_01"
  ],
  "constraints": [
    "local_only",
    "privacy_safe",
    "approval_gate"
  ],
  "impact": "low | medium | high",
  "approval_required": false,
  "payload": {
    "action": "tool name, operation name, or null",
    "parameters_summary": "Redacted summary of relevant inputs",
    "result_summary": "Redacted summary of outputs or outcome"
  },
  "error_summary": null,
  "correlation_id": "optional task or session identifier"
}
Usage Guidance
This skill is a logging/provenance specification and is internally coherent, but the host environment is responsible for enforcing redaction, storage, and approval flow. Before enabling: 1) verify your host will not forward logs to external services and will store them according to your privacy policy; 2) test with non-sensitive inputs to confirm the agent redacts secrets and does not emit chain-of-thought; 3) if you allow 'source_references' that include user prompts or local filenames, ensure those references do not accidentally leak sensitive content; 4) prefer host-side enforcement of approval gates rather than relying on the skill's textual rules alone.
Capability Analysis
Type: OpenClaw Skill Name: log Version: 1.1.1 The 'log' skill is a documentation-only bundle that defines a structured protocol for emitting audit records and provenance data. It contains no executable code (empty entrypoints in skill.json) and includes explicit instructions in SKILL.md to redact secrets, credentials, and sensitive personal data, making it a security-enhancing utility for agent transparency.
Capability Assessment
Purpose & Capability
The skill declares a provenance/logging purpose and only provides instructions for emitting a structured audit record; it requests no binaries, env vars, or installs — all proportional to a logging protocol spec.
Instruction Scope
The SKILL.md stays within logging scope and explicitly forbids secrets and chain-of-thought. One ambiguity: examples show source_references such as "user_prompt" and "local:file_a.md" which could surface sensitive user content or local filenames. The spec requires redaction but enforcement is left to the host; hosts should ensure agents actually redact and avoid including raw sensitive content.
Install Mechanism
Instruction-only (no install spec, no code files to execute). This minimizes filesystem/network risk.
Credentials
No environment variables, credentials, or config paths are requested; the declared requirements are minimal and appropriate for a logging/provenance specification.
Persistence & Privilege
always is false and the skill does not request persistent or elevated privileges. Model invocation is allowed (default) but that is expected for an agent-invocable skill and not, by itself, a concern.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install log
  3. After installation, invoke the skill by name or use /log
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.1
- skill.json and SKILL.md files added to improve structure and metadata. - skill.md removed; documentation now consolidated in SKILL.md. - No changes to skill logic or schema.
v1.1.0
**Refined log protocol for privacy and host control** - Now emphasizes a privacy-first, local-first design; immutability and storage are not handled by the skill itself. - Outlines strict redaction and security rules: no secrets, raw reasoning traces, or sensitive documents in logs. - Introduces clear event triggers and guidelines for what and when to log. - Adds support for signaling when host-controlled approval is required for high-impact actions. - Defines a precise JSON schema for all log entries, with explicit requirements for each field. - Provides guidance on referencing sources in a safe, minimal way.
v1.0.0
Inauguration of the Immutable Memory Layer
Metadata
Slug log
Version 1.1.1
License MIT-0
All-time Installs 3
Active Installs 3
Total Versions 3
Frequently Asked Questions

What is log?

A privacy-first, local-first provenance protocol for agent workflows. Emits structured audit records for important decisions, tool calls, state changes, and... It is an AI Agent Skill for Claude Code / OpenClaw, with 464 downloads so far.

How do I install log?

Run "/install log" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is log free?

Yes, log is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does log support?

log is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created log?

It is built and maintained by AGIstack (@agistack); the current version is v1.1.1.

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