BD disease-cea-auto
疾病药物经济学自动评价 Skill — 对任意指定疾病，自动设计适合的 Markov / 决策树模型框架， 联网遴选当前最常用治疗药物，搜索模型参数（有效率、AE率、效用值、费用等）， 以中国最新人均 GDP（1倍）为 QALY 支付阈值，计算每种药物的增量成本效果比（ICER）与 货币化净收益（NMB），从大到小排序，最终输出完整 Python 代码 + 科学论文格式报告。 Disease Pharmacoeconomics Auto-Evaluation Skill — For any specified disease, automatically designs an appropriate Markov or decision tree model framework, identifies the most commonly used treatment drugs through web-based search, retrieves model parameters (response rate, adverse event rate, utility values, costs, etc.), uses China's latest per capita GDP (1×) as the WTP threshold per QALY, calculates ICER and NMB for each drug, ranks from highest to lowest, and outputs complete Python code plus a scientific paper–style report. 触发词：药物经济学评价、CEA、成本效果分析、ICER、NMB、多药对比、治疗方案比较、 cost-effectiveness analysis, economic evaluation, multiple drugs, QALY, NMB ranking。
As a process D 49/100 · 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: 4. 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")
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 69 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2107 tokens
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)
- +3Description length 854: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Structure: 14 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.