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AA masterchef

Recipe and ingredient query assistant. Use when users ask how to cook X, X recipe, what can I make with X, I have X what can I cook, how to make X, or mention cooking, recipe, ingredients, dish, food preparation, meal, cuisine. Supports searching by ingredients or by dish name. Also triggers on Chinese keywords: 怎么做X, 做法, 食材, 菜谱, 做菜, 烹饪, 料理, 食谱.

ClawHub Agent Skills author: yummy v0.6.2 MIT-0 2 files body ≈ 1 643 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process A 80/100 · Runs to the end — weak spots: inputs and preconditions

GeneratorInfrastructuretype 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%
95
Quality 40%
87
Run on models
none yet
Process rating
A
80/100
Runs to the end
Inputs and preconditions w 11
0
Result and completion w 14
60
When it triggers w 12
70
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.

Broad scope 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 80/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 61 steps
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1643 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 347: enough signal without eating the budget
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 61 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This appears to be a recipe lookup skill that sends dish or ingredient terms to a disclosed external recipe API, with no evidence of hidden persistence, credential access, or destructive behavior.
    LLM: benign (medium) · VirusTotal: · 29 May 2026