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BD garmin-frisbee-analysis

Ultimate Frisbee performance analytics powered by Garmin data. Analyzes and monitors game and training data . Generates interactive HTML dashboards for post-game review, tournament fatigue tracking, and season-long trend analysis. Supports four comparison modes: training vs training, tournament vs tournament, training vs game intensity, and early vs late season. Built for competitive Ultimate Frisbee players who want data-driven insight into performance, recovery, and fitness progression.

ClawHub Agent Skills author: Evelyn v1.0.0 MIT-0 19 files · 1 script body ≈ 2 343 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticsInfrastructuretype 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
80/100
safety, quality, tests
Safety 60%
94
Quality 40%
60
Run on models
none yet
Process rating
D
44/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 2

✓ No critical or high findings

Medium and low: 2
  • medium Dangerous commands cmd-shell-rc install.sh:42
    Writes to a shell startup file
    echo "     (Add these to ~/.zshrc or ~/.bashrc to persist them)"
  • low Exfiltration read-dotenv references/mcp_setup.md:28
    Reads a .env file
    cp .env.example .env

Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Ultimate Frisbee performance analytics powered by Garmin data. Ana… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "env"
  • note frontmatter-key unknown frontmatter key "install"

Process rating: all ten parameters 44/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 40Consistency. Frontmatter name (garmin-frisbee-analysis) differs from the folder (garmin-ultimate-frisbee-analysis)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 47 steps
  • 100Execution cost. Instruction body is 2343 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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
  • -34 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 493: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 47 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: suspicious
This skill matches its Garmin fitness-analysis purpose, but it needs review because it asks users to handle real account credentials and stores/prints sensitive health and GPS data with weak safeguards.
LLM: suspicious (high) · VirusTotal: · 29 May 2026