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Recipe Plan

Community
shinpr
recipe-plan

Create work plan from design document and obtain plan approval

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerecipe-plan
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 Plan 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-fullstack/skills/recipe-plan .claude/skills/recipe-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recipe Plan 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 Plan 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 Plan 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.

Explicit User Instruction: The user explicitly instructs and authorizes every subagent call named in this recipe. Execute each applicable call when its prerequisites are met.

Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before making workflow decisions, invoking agents, or resolving findings.

Context: Dedicated to the planning phase.

Orchestrator Definition

Core Identity: "I am an orchestrator." (see subagents-orchestration-guide skill)

Local authority gate: Make this recipe's workflow decisions and validate each returned result directly; delegate semantic deliverable production to the named specialist.

Review Resolution Gate [MANDATORY]: Resolve every actionable deliverable-review finding through subagents-orchestration-guide Review Resolution before correction or progression. Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.

Execution Protocol:

  1. Invoke named specialists for deliverable production — pass data between them and validate their results
  2. Follow subagents-orchestration-guide skill planning flow exactly:
    • Execute steps defined below
    • Stop and obtain approval for plan content before completion
  3. Scope: See Scope Boundaries below

At each Agent invocation below, build the prompt as a mechanical extraction: copy the named source values into the exact fields, apply only the declared serialization, then invoke immediately.

Acceptance-test-generator is part of this planning flow and may return no selected lanes when the Design Doc has no justified integration/E2E proof boundary.

Scope Boundaries

Included in this skill:

  • Design document selection
  • Test skeleton generation with acceptance-test-generator
  • Work plan creation with work-planner
  • Work plan review with document-reviewer
  • Plan approval obtainment

Responsibility Boundary: This skill completes when the user authorizes implementation of the reviewed plan.

Follow the planning process below:

Execution Process

Step 1: Design Document Selection

  • Use the Design Doc explicitly supplied in $ARGUMENTS when present
  • Otherwise use the only Design Doc under docs/design/ when exactly one exists
  • Report when none exist; when multiple exist, present them for selection

Step 2: Test Skeleton Generation

  • Invoke acceptance-test-generator with design_docs: [selected Design Doc path] and confirmed_requirement_context as the approved PRD path named by its Requirement Convergence section, or that section's unchanged convergence record when no PRD exists
  • Follow subagents-orchestration-guide HC-06 for value_input_required and its unknown-value continuation
  • Pass every non-null generated skeleton path to the next process; treat an evidence-backed empty lane as complete for that lane and continue to the next process

Step 3: Work Plan Creation

Invoke work-planner using Agent tool:

  • subagent_type: "dev-workflows-fullstack:work-planner"
  • description: "Work plan creation"
  • mode: create
  • designDoc: [selected Design Doc path]
  • prd: [approved PRD path] when one exists
  • testSkeletons: [non-null generatedFiles paths]

Step 4: Work Plan Review

Invoke document-reviewer to review the work plan:

  • subagent_type: "dev-workflows-fullstack:document-reviewer"
  • description: "Work plan review"
  • prompt: "doc_type: WorkPlan target: docs/plans/[plan-name].md. Review the Work Plan's own Implementation Scope, tasks, Completion Criteria, dependencies, execution order, exact source-anchor existence, executable verification, and Review Scope. Governing Documents paths are citation sources only; keep issues limited to violations of cited obligations."
  • Run the Review Resolution Gate through correction re-review, its parent requirement or authority exits, and convergence, using work-planner in update mode for rerouted corrections. Present the plan for approval only at its convergence condition.

Step 5: Present for Approval

  • Present the reviewed work plan to the user for batch approval. If the user requests changes, re-invoke work-planner with the user's requested changes verbatim and re-run Step 4.
  • Record unresolved technical evidence or external dependencies in the plan with their affected task and verification boundary. Return to the requirements gate only when confirmed outcome, desired-future requirements, and non-goals cannot all remain true without a user choice.

Response at Completion

Recommended: After plan approval, output the standard block below.

Planning phase completed.
- Work plan: docs/plans/[plan-name].md
- Implementation authorization: granted for this task set

Please provide separate instructions for implementation.

When findings were declined during Work Plan review, append their IDs, governing reasons, and evidence to this completion response.

Frequently asked questions

What does the Recipe Plan AI skill do?

Create work plan from design document and obtain plan approval

Why use Recipe Plan on TypingMind?

Because you install it once and use it with any model. Recipe Plan 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 Plan in TypingMind?

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

Which AI models can use Recipe Plan?

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 Plan?

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

Is the Recipe Plan 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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