AD cold-case-investigator
全球未破悬疑案件（cold case / unsolved case）迭代式深度自动化调查与创作辅助技能。 当用户提到"查案件""调查""搜案子""搜悬案""查悬疑案件""查未破案件" "cold case""unsolved case"或明确要求搜索某个具体案件信息时触发。 也适用于内容创作者需要"搜外网素材""找案件原型""查真实犯罪资料"等创作调研场景。 采用"广度搜索→信息分析→双轨深追→穷尽为止"的迭代循环： Track A（证据支撑型）基于已获取信息进行系统性分析，制定有信源支持的深追方向； Track B（直觉推演型）生成纯粹猜想的深追方向，无证据基础但逻辑自洽，专为创作启发设计。 优先使用案件所在国信源，多语种多角度交叉验证。 内置异常处理协议（零结果处理/抓取失败/轮次上限/矛盾分级）， 明确标注能力边界与适用案件范围，不执行模糊降级搜索。
全球未破悬疑案件（cold case / unsolved case）迭代式深度自动化调查与创作辅助技能。 当用户提到"查案件""调查""搜案子""搜悬案""查悬疑案件""查未破案件" "cold case""unsolved case"或明确要求搜索某个具体案件信息时触发。…
As a process D 46/100 · Unfinished process — 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: 8. 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 "agent_created"
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 147 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2782 tokens
- 100Running it twice. No mutating operations
- low 11 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -215 emoji in the instructions: noise for the model
- +2Single-language instructions
- +5Description quotes 4 example trigger phrases
- +3Description length 384: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 147 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.