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AF garmin-panorama-training-analysis

基于佳明（Garmin Connect）数据的全景训练分析。拉取活动/逐公里明细(lapDTOs)/每日恢复(HRV·RHR·睡眠·Body Battery)/负荷平衡(ACWR·Form·就绪度)，融合 Open-Meteo 天气，输出 Ride Relief 暗黑风格离线 HTML 报告。能力：逐公里技术分解(配速/心率/步频/步幅/垂直振幅/触地时间)、心率5区训练比例、热适应评价(WBGT/露点/湿球/酷热指数)、训练状态与成绩预测(Riegel)、身体成分与能量。触发词：佳明分析、Garmin 训练报告、跑步分析、周报月报、热适应、LSD分析、负荷分析、ACWR、就绪度、训练状态。

ClawHub Agent Skills author: Kevin v0.1.0 MIT-0 13 files body ≈ 4 017 tokens Open the sourceclawhub.ai↗ analyzed 5 h ago

基于佳明（Garmin Connect）数据的全景训练分析。拉取活动/逐公里明细(lapDTOs)/每日恢复(HRV·RHR·睡眠·Body Battery)/负荷平衡(ACWR·Form·就绪度)，融合 Open-Meteo 天气，输出 Ride Relief 暗黑风格离线 HTML…

As a process F 34/100 · Will not run — References files that are not bundled: references/ride_relief_style.css, scripts/fetch_data.py::garmin_weather()

AnalyzerSoftware developmentData and analyticsAI and agentstype 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
F
34/100
Will not run
References files that are not bundled: references/ride_relief_style.css, scripts/fetch_data.py::garmin_weather()
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/ride_relief_style.css
  • warning missing-ref reference to a missing file: scripts/fetch_data.py::garmin_weather()
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 34/100

Will not run. References files that are not bundled: references/ride_relief_style.css, scripts/fetch_data.py::garmin_weather()
  • 0Tools and files. 2 referenced file(s) missing: references/ride_relief_style.css, scripts/fetch_data.py::garmin_weather()
  • 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
  • 70Execution cost. Instruction body is 4017 tokens
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 14 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
  • +2Single-language instructions
  • +3Description length 299: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (22 code blocks)
  • +3All 3 scripts are documented
  • +1License stated

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

External checks

ClawHub: suspicious
The skill mostly does what it claims, but it handles Garmin credentials, health data, and precise locations in ways users should review carefully before installing.
LLM: suspicious (high) · 15 Sept 2026