AC improvement-evaluator
当需要验证 Skill 改进是否真正提升了 AI 执行效果时使用。通过预定义任务集（YAML）运行 AI 任务，判定 pass/fail，输出 execution_pass_rate。不用于文档结构评分（用 improvement-learner）或候选打分（用 improvement-discriminator）。
As a process C 55/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches
The same skill appears in 1 more place: ClawHub
How to improve
For the model run — optional
- 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: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "triggers"
Process rating: all ten parameters 55/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 100Steps. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2352 tokens
- 100Running it twice. No mutating operations
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (6 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)
- -31 of 2 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 159: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 23 items
- +3Output format is stated explicitly
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.
External checks
ClawHub: clean
This skill is a disclosed execution evaluator that runs task suites through Claude and optional pytest checks; its behavior matches its purpose but should be used with trusted inputs.
LLM: benign (high) · VirusTotal: · 29 May 2026