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CF ct-safety

基于 FDA FAERS（经 openFDA 公开 REST API）做药物-事件 disproportionality 信号检测，计算 PRR / ROR / IC / EBGM 及 95% 置信区间与信号判定；一次性流水线默认产出两份核心交付物——① 可渲染的 HTML 报告（可视化结论）② XLSX 数据簿（含全部原始 FAERS 计数、2×2 表、四种方法及 FDA 标签/CN-PV/评分明细，供逐条查阅与审计）；同时保留 JSON / Markdown 作兼容备份。可选 --with-cn-pv 增加中国官方药物警戒通报（cdr-adr.org.cn）定性检索作信号佐证。所有数据均为公开不良事件报告，不输入任何保密数据或信息，B 档（普通数据输入 + 对外检索），可快速推广技能。 / Signal detection on FDA FAERS (via openFDA public REST API): computes PRR / ROR / IC / EBGM with 95% CIs and signal flags from the drug-event 2x2 table. The one-shot pipeline emits TWO core deliverables by default — ① a renderable HTML report (visual conclusion) and ② an XLSX workbook holding ALL raw FAERS counts, the 2x2 table, the four methods, and FDA-label / CN-PV / score details for line-by-line audit; JSON / Markdown are kept as compatibility backups. Optional --with-cn-pv adds qualitative China official PV bulletin search (cdr-adr.org.cn) as signal corroboration. All data are public adverse-event reports; zero confidential data or information input — B-tier quickly-adoptable.

ClawHub Agent Skills author: Wintone Zhang v0.9.1 MIT-0 55 files body ≈ 3 540 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

基于 FDA FAERS（经 openFDA 公开 REST API）做药物-事件 disproportionality 信号检测，计算 PRR / ROR / IC / EBGM 及 95% 置信区间与信号判定；一次性流水线默认产出两份核心交付物——① 可渲染的 HTML 报告（可视化结论）② XLSX…

As a process F 56/100 · Will not run — References files that are not bundled: references/*.md

IntegrationExcelData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
73/100
safety, quality, tests
Safety 60%
99
Quality 40%
33
Run on models
none yet
Process rating
F
56/100
Will not run
References files that are not bundled: references/*.md
Tools and files w 18
0
Result and completion w 14
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. Shorten the description to 1024 characters.
  3. The text references files that are not there: add them or drop the references.
For the model run — optional
  • 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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Obfuscation obf-base64-blob adapters/bug_report.py:71
    Long base64-looking blob (detector / deny-list definition)
    "Bg1n…XQs
    detector

Files scanned: 55. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1043 chars, limit 1024
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: references/*.md
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "cn_name"
  • note frontmatter-key unknown frontmatter key "invocable"
  • note frontmatter-key unknown frontmatter key "required_commands"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "permissions"

Process rating: all ten parameters 56/100

Will not run. References files that are not bundled: references/*.md
  • 0Tools and files. 1 referenced file(s) missing: references/*.md
  • 0Result and completion. Does not say what the result is
  • 30Running it twice. 7 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 45 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3540 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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

  • +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 1043: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -322 of 27 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 45 items
  • +4Reference files are cited in the instructions (10 of 15)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 33.

External checks

ClawHub: suspicious
The skill mostly matches its public FAERS analysis purpose, but it adds under-scoped bug-report egress and cross-session agent logging that should be reviewed before installation.
LLM: suspicious (high) · 23 Aug 2026