AC skill-analyst-zh
在安装或发布 OpenClaw skill 之前进行分析评估。对比已安装或 ClawHub 上的同类 skill， 检查功能重叠，进行安全审查，给出明确的安装/发布建议。 需要 `clawhub` CLI 可用。 使用场景：用户想评估某个 skill 是否值得安装、对比 ClawHub 上的同类 skill、 检查本地 skill 是否达到发布标准。 触发词："分析 skill-name"、"评估安装/发布某某技能"、"这个skill值得装吗"、"能发布吗"、"skill对比"。
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 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
- 100Tools and files. No external tools needed
- 100Steps. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 383 tokens
- 100Running it twice. No mutating operations
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 242: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.
External checks
ClawHub: clean
This looks like a skill-review helper with somewhat broad trigger wording, but there is no evidence of hidden access, persistence, credential use, or harmful behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026