SKILLEMALL.ai
GitHub ↗ RU Sign in

AC hotdog

Hot dog or not? Classify food photos and battle Nemotron. Use when a user sends a food photo, asks if something is a hot dog, or says 'hotdog', '/hotdog', or 'hot dog battle'.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 297 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Hot dog or not? Classify food photos and battle Nemotron. Use when a user sends a food photo, asks if something is a hot dog, or says 'hotdog', '/hotdog', or…

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

Proceduretype 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
92/100
safety, quality, tests
Safety 60%
98
Quality 40%
83
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    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
    • low Secrets in code secret-high-entropy-token battle-token.txt:1
      High-entropy token-like string (may be an id, hash or a credential)
      ih1r…i9X
    • low Secrets in code secret-high-entropy-token SKILL.md:31
      High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
      exec: curl -s -X POST "https://api.hotdogornot.xyz/api/battle/round" -H "Authorization: Bearer ih1r…i9X" -F "image=@{{MediaPath}}" -F "claw_model={{Model}}" -F "claw_answer=ANS
      placeholder

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 40Consistency. Frontmatter name (hotdog) differs from the folder (temp)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 5 steps
    • 100Execution cost. Instruction body is 297 tokens
    • 100Running it twice. No mutating operations

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 175: enough signal without eating the budget
    • +4Structure: 3 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.