BF litigation-risk-monitor
涉诉专利风险监测与同族扩展分析技能。触发场景：用户提供目标申请人名单（1～N 个，中英文均可），希望自动检索这些申请人名下的涉诉专利，做 INPADOC 同族扩展，结合 Patsnap legal 模块与 web.search 公开诉讼信息双向交叉，输出同族基础分析（地域/技术点/法律状态/审查历史）、诉讼时间线（含涉案专利号）、涉诉案件深度分析（原被告/案号/进程/争议焦点/抗辩/结果）、核心发明人近 3 年延伸分析，并生成单一 HTML 报告 + 结构化 JSON/CSV 附件，给出地域风险、应诉预警、趋势预测三维结论。不适用于：单件专利新颖性/创造性分析（路由 novelty-check / non-obviousness-check）、纯 FTO 法律意见、无申请人名单的开放式情报、与"涉诉专利同族 + 诉讼案件 + 发明人趋势"无关的一般性问答。
涉诉专利风险监测与同族扩展分析技能。触发场景：用户提供目标申请人名单（1～N 个，中英文均可），希望自动检索这些申请人名下的涉诉专利，做 INPADOC 同族扩展，结合 Patsnap legal 模块与 web.search…
As a process F 31/100 · Will not run — References files that are not bundled: 含pn+title+url
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.
- 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: 7. 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 ≈ 7927 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: 含pn+title+url - note
frontmatter-keyunknown frontmatter key "copyright"
Process rating: all ten parameters 31/100
- 0Tools and files. 1 referenced file(s) missing: 含pn+title+url
- 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
- 30Running it twice. 3 mutating operations with no state check
- 70Execution cost. Instruction body is 7927 tokens
- 100Steps. 59 steps
- 100Consistency. Name and required fields are in place
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (13 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
- -228 emoji in the instructions: noise for the model
- -31 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 383: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 59 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 50.