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Recipe Quality Profile

Community
shinpr
recipe-quality-profile

Proposes repository-specific quality policy for implementation and review and, after confirmation, creates or updates docs/project-context/quality.yaml. Use when asked to create or update a repository quality profile.

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerecipe-quality-profile
Stars
682
Forks
103
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by shinpr on GitHub. Read the source before you install it.

Installation

Install the Recipe Quality Profile AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-workflows-frontend/skills/recipe-quality-profile .claude/skills/recipe-quality-profile
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recipe Quality Profile in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Recipe Quality Profile on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Recipe Quality Profile is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Execute Skill: llm-friendly-context before proposing profile conditions or writing the profile. Execute Skill: coding-principles to distinguish repository-owned policy from general code-quality knowledge.

Purpose

Establish repository-specific implementation and code-review acceptance conditions with the user, then create or update docs/project-context/quality.yaml.

Requested policy change: $ARGUMENTS

Profile Contract

yaml
version: 1
review_dimensions:
  - id: stable-kebab-case-id
    applies_when: Observable condition that makes this repository rule relevant to a change.
    pass: Observable accepted state to verify.
    evidence:
      - "repository/path: section, identifier, or contract"

Each dimension owns one repository-specific quality decision. applies_when limits its implementation and review surface, pass defines the accepted state, and evidence identifies why the repository owns the rule.

Authoring Flow

  1. Use the current working repository as the target and read its docs/project-context/quality.yaml when present. For an existing profile, use the requested policy change as the update boundary; when none is supplied, ask for it and keep the profile unchanged.
  2. Build candidates from acceptance conditions expressed or enforced by repository instructions, contributor documentation, CI, manifests and scripts, schemas and public contracts, tests, or representative implementation patterns. For an update, derive candidates only from the requested policy change and preserve unrelated dimensions.
  3. For each candidate, inspect supporting and contradicting evidence wherever it can change the candidate's applicability, accepted state, or repository ownership. Separate observed repository facts from policy choices that require user confirmation.
  4. Retain a candidate only when failing its pass condition would change implementation acceptance and every repository fact it depends on has cited evidence. Give it the narrowest useful applies_when, one positive observable pass condition, and consolidate candidates that would produce the same finding and correction. Omit a candidate when required repository evidence is unavailable and report the exact evidence needed.
  5. Present proposed additions, changes, and removals, confirm that other dimensions remain unchanged, show the supporting and contradicting evidence, and state unresolved policy choices with their effect on implementation and review acceptance. Obtain explicit user confirmation of a proposal with no unresolved choices before writing.
  6. Write only the confirmed profile content. Read the result and verify version 1, unique IDs, all required fields, observable conditions, readable evidence references, and consistency with the confirmed proposal.

When no repository-specific dimension remains and no profile exists, report that repository evidence supports no profile content and leave the repository unchanged.

Result

Before confirmation, report:

  • proposed additions, changes, and removals, plus the unchanged remainder;
  • supporting and contradicting evidence for each modification;
  • omitted candidates and the exact missing evidence;
  • policy choices requiring the user's decision and their effect on implementation and review acceptance.

After confirmation, report:

  • the profile path and whether it was created, updated, or left unchanged;
  • dimensions added, changed, or removed;
  • evidence used for each changed dimension;
  • an exact validation limitation when the result could not be verified.

Completion Check

  • Every retained dimension changes an implementation or review decision and cites repository or user-confirmed policy evidence
  • Conditions are positive, observable, and limited by applies_when
  • The profile contains repository-specific acceptance conditions only
  • Supporting and contradicting evidence were compared where they could change the proposal
  • The user confirmed the complete proposal before the repository write
  • Dimensions outside the update boundary remain unchanged
  • The written profile satisfies the Profile Contract

Frequently asked questions

What does the Recipe Quality Profile AI skill do?

Proposes repository-specific quality policy for implementation and review and, after confirmation, creates or updates docs/project-context/quality.yaml. Use when asked to create or update a repository quality profile.

Why use Recipe Quality Profile on TypingMind?

Because you install it once and use it with any model. Recipe Quality Profile is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Recipe Quality Profile in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-workflows-frontend/skills/recipe-quality-profile. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recipe Quality Profile?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Recipe Quality Profile?

As many as you like. As long as a model supports skills, you can use Recipe Quality Profile with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Recipe Quality Profile AI skill free?

Yes. It is published on GitHub by shinpr under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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