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AB plan2meal

Manage recipes and grocery lists in Plan2Meal via chat (add recipe URLs, list/search/show/delete recipes, and create/manage grocery lists). Use when users explicitly want Plan2Meal recipe/grocery actions. Do not use for generic cooking advice, nutrition coaching, or non-Plan2Meal todo/shopping tools. Success = command executed with clear result text (IDs, counts, links/errors) and accurate data-routing disclosure.

modbender/skill-library-mcp Agent Skills author: modbender MIT 11 files body ≈ 559 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manage recipes and grocery lists in Plan2Meal via chat (add recipe URLs, list/search/show/delete recipes, and create/manage grocery lists).

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

AnalyzerPersonal productivitySoftware developmenttype 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
94/100
safety, quality, tests
Safety 60%
98
Quality 40%
87
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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 Exfiltration read-dotenv README.md:18
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv SKILL.md:53
      Reads a .env file
      cp .env.example .env

    Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "requiredEnv"
    • note frontmatter-key unknown frontmatter key "optionalEnv"
    • note frontmatter-key unknown frontmatter key "primaryEnv"

    Process rating: all ten parameters 68/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 100Tools and files. No external tools needed
    • 100Steps. 32 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 559 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 417: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 32 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

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