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AC healthfit

个人全维度健康管理系统，中西医融合。当用户涉及运动训练计划、饮食营养建议、 健康数据记录追踪、中医体质辨识、节气养生、舌诊分析、性健康记录等话题时立即触发。 提供多位专业顾问（运动教练矩阵 / Dr. Mei 营养师 / Analyst Ray 数据分析师 / 中医养生顾问矩阵），运动教练按项目细分（田径、游泳、力量、球类、武术等）， 中医顾问按学科细分（体质辨识、养生功法、内科、妇科等），支持深度建档和长期追踪。 任何"帮我建档"、"记录今天运动"、"我的体质"、"舌苔厚白"、"今天跑步"、 "游泳训练"、"中医调理"类请求均应触发本 skill。

ClawHub Agent Skills author: ChenChen v4.0.0 MIT-0 55 files body ≈ 1 536 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
51/100
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
  • 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: 54. 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 "triggers"
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 51/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (healthfit) differs from the folder (healthfit-cn)
  • 100Tools and files. No external tools needed
  • 100Steps. 13 steps
  • 100Execution cost. Instruction body is 1536 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • medium 25 test cases, all positive: not one "should refuse" or "should ask first"
  • low No test case covers injection arriving through data

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
  • -215 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 278: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 13 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (18 of 19)
  • +3All 4 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
HealthFit is a coherent local health-management skill, but it handles very sensitive health and sexual-health data with broad activation rules and privacy/storage guarantees that are stronger than the artifacts enforce.
LLM: suspicious (medium) · VirusTotal: benign · 28 May 2026