BF fto-report-quality
"FTO报告质量审核技能（通用版，一包双模块+可选Harness校验）v9.0。适用于任意公司、法务团队或专利代理所，接收已有FTO报告、专利侵权风险分析报告或展会知识产权风险自评报告，按事实层、法律判断层、决策可用层三层方法论进行质量审核，并用A/B/C/D四维度量化评分。新增：矩阵式场景识别、五轨检索（含时序轨）、Jackknife召回率估算、致命缺陷一票否决、摘要优先输出模式、桑基图可视化、可执行优先级建议排序、Harness跨字段逻辑校验。触发本skill进行FTO报告质量审核时，必须调用generate_report.py生成正式HTML文件；当用户明确要求校验时，再使用内置validate_report.py检查HTML结果。"
"FTO报告质量审核技能（通用版，一包双模块+可选Harness校验）v9.0。适用于任意公司、法务团队或专利代理所，接收已有FTO报告、专利侵权风险分析报告或展会知识产权风险自评报告，按事实层、法律判断层、决策可用层三层方法论进行质量审核，并用A/B/C/D四维度量化评分。新增：矩阵式场景识别、五轨检索（含时序轨）…
As a process F 35/100 · Will not run — References files that are not bundled: assets/fto_report.css, assets/harness_report.css
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 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
missing-refreference to a missing file: assets/fto_report.css - warning
missing-refreference to a missing file: assets/harness_report.css - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 35/100
- 0Tools and files. 2 referenced file(s) missing: assets/fto_report.css, assets/harness_report.css
- 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
- 100Steps. 111 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3341 tokens
- 100Running it twice. No mutating operations
- low 16 top-level sections: this looks like several domains in one skill
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
- -217 emoji in the instructions: noise for the model
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 324: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 111 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (1 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.