BC find-skills
场景驱动+关键词双模式技能发现工具。当用户用自然语言描述场景/需求（如"我想做一个海报""帮我分析股票"），或明确说"安装技能/find skills/找个skill"时，自动从官方内置、本地已安装、SkillHub、虾评、GitHub、ClawHub 六层联合搜索并推荐最合适的技能，支持一键安装。已完全替代官方原 find-skills 插件。
场景驱动+关键词双模式技能发现工具。当用户用自然语言描述场景/需求（如"我想做一个海报""帮我分析股票"），或明确说"安装技能/find skills/找个skill"时，自动从官方内置、本地已安装、SkillHub、虾评、GitHub、ClawHub 六层联合搜索并推荐最合适的技能，支持一键安装。已完全替代官方原…
As a process C 58/100 · 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.
- 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: 4. 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") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "xiaping_trigger" - note
frontmatter-keyunknown frontmatter key "xiaping_category" - note
frontmatter-keyunknown frontmatter key "xiaping_tags" - note
frontmatter-keyunknown frontmatter key "xiaping_eval_strategy"
Process rating: all ten parameters 58/100
- 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
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Tools and files. No external tools needed
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2540 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
- -219 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 174: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (23 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.