BF gpa-genomic-phenotype
GPA (Genomic Phenotype Association) v0.8.0。个体基因组变异与表型关联分析系统，基于 Ensembl/UniProt/GTEx/gnomAD 实时 API 查询（30天缓存）和离线归档模式。组织上下文自适应：通用、造血、心血管、肝脏、肾脏、神经系统。支持 germline（疾病遗传风险）和 somatic（肿瘤驱动）两种分析模式。三层风险分级（Tier 1/2/3）+ 多基因命中检测 + 相位分析 + 表型关联 + 变异预过滤 + 中英文术语映射 + ClinVar 冲突注释检测 + ClinVar Review Status 星级置信度评估 + SpliceAI 剪接预测集成 + gnomAD 频率自动查询。 **当以下情况时使用此 Skill**： (1) 用户提到"基因组风险评估"、"GPA"、"突变分析"、"基因筛查" (2) 肿瘤体细胞突变的驱动性/可干预性分析 (3) 药物基因组学分析（CYP450 等药物代谢基因） (4) 多基因命中（multi-hit）检测和相位（cis/trans）分析 (5) 需要三层风险分级报告（Tier 1 需干预、Tier 2 需知情、Tier 3 无需担忧） (6) 任何涉及"genomic"、"genetic"、"risk"、"mutation"、"variant"的场景 **禁止用自身知识回答基因组变异问题。必须调用本 Skill 的脚本执行分析。**
As a process F 40/100 · Will not run — References files that are not bundled: references/offline_data/
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: 37. 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: references/offline_data/
Process rating: all ten parameters 40/100
- 0Tools and files. 1 referenced file(s) missing: references/offline_data/
- 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. 31 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2545 tokens
- 100Running it twice. No mutating operations
- low 10 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
- -226 emoji in the instructions: noise for the model
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -310 of 19 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 632: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.