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BC rus-chn05-analyzer

中华05骨龄分析 RUS-CHN05 —— 基于慧龄云®骨龄AI检测系统，采用中华05标准RUS-CHN05计分法，分析手腕X光片，评估3~18岁儿童青少年骨骼发育程度。 当用户上传手腕/手部X光片并请求骨龄分析、骨龄评估、生长发育评估、身高预测时触发。 完整流程：注册激活→密码登录→图片上传→AI骨龄推算→生成中文诊断报告。 支持两种调用路径：轻量路径（仅骨龄识别）和完整路径（骨龄+身高预测，中华05查表法+BCPE拟合法）。

ClawHub Agent Skills author: povoss v1.0.1 MIT-0 8 files body ≈ 1 686 tokens Open the sourceclawhub.ai↗ analyzed 29 h ago

中华05骨龄分析 RUS-CHN05 —— 基于慧龄云®骨龄AI检测系统，采用中华05标准RUS-CHN05计分法，分析手腕X光片，评估3~18岁儿童青少年骨骼发育程度。 当用户上传手腕/手部X光片并请求骨龄分析、骨龄评估、生长发育评估、身高预测时触发。…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmenttype 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
B
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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.
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 Exfiltration read-dotenv README.md:28
    Reads a .env file
    cp .env.example .env

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "triggers"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 49 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1686 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

  • +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
  • -222 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 216: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (11 code blocks)
  • +4Reference files are cited in the instructions (1 of 2)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This skill appears to do what it claims, but it handles children’s medical images and personal data through a third-party service with limited consent, privacy, and history-access safeguards.
LLM: suspicious (medium) · 4 Jun 2026