AC claw-mechanic
Diagnose, audit, and repair OpenClaw hosts when an agent, gateway, plugin, cron, model route, memory engine, channel, approval policy, or update looks broken, expensive, slow, looping, stale, or misconfigured. Use for focused OpenClaw health checks, post-update triage, plugin update verification, model provider/API wiring audits, cron timeout/root-cause work, exec approval/reviewer problems, and rectification plans that need live proof against openclaw.ai docs instead of guesses.
Diagnose, audit, and repair OpenClaw hosts when an agent, gateway, plugin, cron, model route, memory engine, channel, approval policy, or update looks broken…
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 85Steps. 78 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3026 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +3Description length 484: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 78 items
- +4Reference files are cited in the instructions (3 of 3)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.