AC skill-publish
Publish SKILL.md files to ClawHub (clawhub.ai) and diagnose publish failures across the three skill repos (history, ai-custom-skills, ai-thoughts). Use when the user wants to publish skills to ClawHub, manually trigger a publish, check why a publish failed or was skipped, or understand the ClawHub skill sync pipeline.
As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
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 · 1
✓ No critical or high findings
Medium and low: 1
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low Exfiltration
net-credential-useSKILL.md:50Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host; quoted — discussed, not commanded)5. **Verify on ClawHub** (read-only, API): `curl -s -H "Authorization: Bearer $CLH_TOKEN" https://clawhub.ai/api/v1/skills/<slug>` — the `latestVersion` and `owner.handle` fields confirm what is live.
vendor-hostquoted
Files scanned: 2. 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 62/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 30 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1411 tokens
- 100Running it twice. Mutating operations check current state
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
- +1No license
- +2Single-language instructions
- +3Description length 319: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 30 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.