LaunchKit · 2026
Back to Skills

agent-self-governance

Self-governance protocol for autonomous agents: WAL (Write-Ahead Log), VBR (Verify Before Reporting), ADL.

0
826 downloads
by @bowen31337

Setup & Installation

openclaw skills install @bowen31337/agent-self-governance

Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:

npx clawhub install agent-self-governance

Version History

v1.1.0Feb 14, 2026 - Version note by skill developer:

Version 1.1.0 introduces infrastructure knowledge logging to the self-governance protocol. - Added IKL (Infrastructure Knowledge Logging) as a fifth protocol, requiring immediate logging of discovered hardware, network, and service facts. - Expanded usage guidelines and examples for capturing infrastructure data, including commands and best practices. - Updated skill description and documentation to reflect IKL protocol and its integration points. - No changes to core logic of WAL, VBR, ADL, or VFM, but improved documentation clarity throughout.

What This Skill Does

Five protocols that address common agent failure modes: WAL persists context before LLM compaction, VBR prevents false task-complete claims, ADL tracks persona drift, VFM monitors token cost against task value, and IKL captures infrastructure facts before they leave context. Each protocol has its own Python script, defined triggers, and log format.

Rather than relying on agent memory or ad-hoc notes, each failure mode gets a dedicated scriptable protocol with explicit triggers, commands, and log formats that survive context resets.

When to use it

  • Recovering key decisions after LLM context compaction mid-session
  • Catching false task-complete claims before they reach the user
  • Routing monitoring tasks to budget models instead of burning Opus tokens on simple summarization
  • Logging GPU server specs discovered during SSH setup before they drop out of context
  • Detecting sycophantic drift in agent responses over long sessions

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User says: 'No, use Podman not Docker'

AGENT
  1. 1Run `python3 scripts/wal.py append <agent_id> correction 'Use Podman not Docker'` before responding
  2. 2Acknowledge the correction to the user
  3. 3Update affected configuration or commands to reference Podman
  4. 4Run `python3 scripts/vbr.py check task123 command 'podman ps'` to verify Podman is available
  5. 5Log the result with `python3 scripts/vbr.py log <agent_id> task123 true 'Podman confirmed available'`
OUTPUT

Correction is persisted to WAL before the agent responds, and tooling availability is verified before proceeding