BF csr-analyzer
判断用户提供的文档是否属于汽车行业质量管理体系 IATF 16949 中的客户特定要求 (Customer-Specific Requirements, CSR)。直接触发词：判断CSR、客户特定要求、CSR审核、IATF CSR、CSR分析。语义触发：当用户消息中包含'客户要求'+'质量体系'、'OEM特殊要求'、'汽车客户额外要求'、'供应商质量手册'等组合时自动激活。文档分析触发：分析这份文档、帮我看看这份 配合文档附件上传时激活。This skill should be used when the user wants to determine whether a document contains IATF 16949 CSR content, or when uploading automotive quality documents for classification.
判断用户提供的文档是否属于汽车行业质量管理体系 IATF 16949 中的客户特定要求 (Customer-Specific Requirements, CSR)。直接触发词：判断CSR、客户特定要求、CSR审核、IATF…
As a process F 47/100 · Will not run — References files that are not bundled: references/iatf16949_csr.md
How to improve
- The text references files that are not there: add them or drop the references.
- 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
missing-refreference to a missing file: references/iatf16949_csr.md - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 47/100
- 0Tools and files. 1 referenced file(s) missing: references/iatf16949_csr.md
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (csr-analyzer) differs from the folder (skill-csr-analyzer)
- 55Failures and branches. 1 branches
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 77 steps
- 100Execution cost. Instruction body is 2890 tokens
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
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 398: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 77 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.