AD xiwang-graphene-medical-hub
烯旺集团医疗科研成果智能检索与视频口播文案生成技能。自动检索「烯旺医疗科研成果」文件夹内全部66个文档（含学术论文、公众号推文、热敏灸专著、企业资料等），支持按关键词、主题分类（肿瘤/骨科/五官科/妇科/皮肤/睡眠/心血管/中医/基础理论/企业/产品共11类）、疾病名称检索科研成果，并根据检索结果自动生成高质量的视频口播文案。适用于需要查找烯旺集团石墨烯医疗科研数据、撰写医疗健康科普视频脚本、制作产品宣传文案等场景。触发词：检索医疗科研成果、搜索石墨烯医疗、查烯旺科研、医疗科研检索、生成医疗视频文案、石墨烯口播文案、烯旺医疗搜索、medical research search。
烯旺集团医疗科研成果智能检索与视频口播文案生成技能。自动检索「烯旺医疗科研成果」文件夹内全部66个文档（含学术论文、公众号推文、热敏灸专著、企业资料等），支持按关键词、主题分类（肿瘤/骨科/五官科/妇科/皮肤/睡眠/心血管/中医/基础理论/企业/产品共11类）、疾病名称检索科研成果，并根据检索结果自动生成高质量的视频…
As a process D 41/100 · 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: 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") - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 41/100
- 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
- 40Consistency. Frontmatter name (xiwang-graphene-medical-hub) differs from the folder (xiwang-medical-research)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 40 steps
- 100Execution cost. Instruction body is 1193 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (5 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
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +3Description length 292: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.