AD clawhub-skill-maintainer
Maintain a large ClawHub skill portfolio with a quality-first and AI-assisted upgrade lens. Use when the user wants to audit published skills, find high-quality skills worth maintaining, analyze downloads/installs/stars/comments, detect stale or partial data, generate safe upgrade/maintenance queues, prepare AI maintainer prompts, handle bulk-publishing account risk, or build/update a ClawHub skill dashboard.
Maintain a large ClawHub skill portfolio with a quality-first and AI-assisted upgrade lens.
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency
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: 15. 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 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 22 mutating operations with no state check
- 40Consistency. Frontmatter name (clawhub-skill-maintainer) differs from the folder (skill-maintainer)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 69 steps, 1 vague phrases
- 100Execution cost. Instruction body is 2390 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (16 tags): a typed call is more reliable
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
- -36 of 9 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 412: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (9 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.