BB skill-review-pro
AI Skill 质量评审系统。通过静态审查对 Skill 进行评分（100分制）， 输出专业的评审报告和改进建议。模块化架构：主控编排 + 类型策略 + 评分模型 + 修复执行。 AI Skill QA System. Evaluates Skills via static analysis, with 100-point scoring, modular architecture with type-aware policies. 触发词：评审 skill, 测评 skill, skill 评分, skill 质量检查, 审查 skill, 改进 skill, 完善技能, 验证修复意见, 稳定性测试, benchmark, review skill, evaluate skill, improve skill, validate fix, skill quality.
As a process B 75/100 · Nearly there — weak spots: result and completion, when it triggers, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 10. 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") - warning
body-longSKILL.md body ≈ 5356 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 75/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5356 tokens
- 100Tools and files. No external tools needed
- 100Steps. 149 steps
- 100Failures and branches. 9 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 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 17 top-level sections: this looks like several domains in one skill
- 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)
- +3Output format is not stated: the model decides each time
- -245 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 391: enough signal without eating the budget
- +4Structure: 43 headings
- +3Step-by-step instructions: 149 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.