BF prism-auto-config-map
棱镜车智-专利论文数据穿透 Skill。用于车企下一代车型规划、竞品配置对标、 技术趋势预测、供应链机会识别和售前 Demo。围绕特定细分市场、车型清单或 关键配置，基于 QFD/质量屋 + Matrix Analysis 为主轴，辅以 Benchmarking、 Kano、技术 S 曲线、专利壁垒矩阵，融合官方参数、配置库、VOC 与智慧芽专利/论文/文献/企业产业链数据， 输出强视觉 HTML 前瞻决策报告。核心价值是把市场配置现象穿透到专利、 论文/文献、关键申请人、供应链合作关系和未来 1-2 年量产机会。 不适用于 FTO 法律意见、精确 BOM 成本测算、泛财报融资分析或脱离车型 配置决策的公司战略画像。
棱镜车智-专利论文数据穿透 Skill。用于车企下一代车型规划、竞品配置对标、 技术趋势预测、供应链机会识别和售前 Demo。围绕特定细分市场、车型清单或 关键配置，基于 QFD/质量屋 + Matrix Analysis 为主轴，辅以 Benchmarking、 Kano、技术 S…
As a process F 40/100 · Will not run — References files that are not bundled: assets/vehicle_visuals/..., assets/vehicle_visuals/photo_real/
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: 12. 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/vehicle_visuals/... - warning
missing-refreference to a missing file: assets/vehicle_visuals/photo_real/ - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 40/100
- 0Tools and files. 2 referenced file(s) missing: assets/vehicle_visuals/..., assets/vehicle_visuals/photo_real/
- 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
- 100Steps. 143 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2021 tokens
- 100Running it twice. No mutating operations
- low 19 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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 312: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 143 items
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.