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AC healthfit

Personal comprehensive health management system integrating Western medicine and TCM. Triggers when users discuss workout training plans, nutrition advice, health data tracking, TCM constitution identification, solar term wellness, tongue diagnosis, or sexual health records. Provides four advisors (Coach Alex - Fitness Coach / Dr. Mei - Nutritionist / Analyst Ray - Data Analyst / Dr. Chen - TCM Wellness Practitioner), supporting in-depth profiling and long-term tracking. Triggers on requests like "create my profile", "log today's workout", "my constitution", "thick white tongue coating".

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 41 files body ≈ 4 087 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Personal comprehensive health management system integrating Western medicine and TCM.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

IntegrationData and analyticsAI and agentsWriting and documentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    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: 40. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"
    • note frontmatter-key unknown frontmatter key "keywords"

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4087 tokens
    • 100Steps. 68 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 10 top-level sections: this looks like several domains in one skill
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -216 emoji in the instructions: noise for the model
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +3Description length 597: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 68 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (17 of 17)
    • +3All 4 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.