Recipe Update Doc logo

Recipe Update Doc

Community
shinpr
recipe-update-doc

Update existing design documents (Design Doc / PRD / ADR) with review

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerecipe-update-doc
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 Update Doc 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-update-doc .claude/skills/recipe-update-doc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recipe Update Doc 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 Update Doc 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 Update Doc 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 updating existing design documents.

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 Gate: Complete Steps 1-6 in order, following only the branches activated by document type and review result. Advance only through each step's stated evidence, review convergence, or approval condition. Complete after the final approval gate and every applicable Completion Criterion is satisfied.

Execution Protocol:

  1. Invoke named specialists for deliverable production — pass deliverable paths between them and validate their results (see subagents-orchestration-guide "Orchestrator Execution Boundary")
  2. Execute update flow:
    • Identify target → Clarify changes → Update document → Review → Consistency check
    • Stop at the [Stop: Final approval] marker → Wait for user approval before completing
  3. Scope: Complete when the user approves completing the update

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.

CRITICAL: Execute document-reviewer — it is the quality gate for document accuracy.

Workflow Overview

Target document → change clarification
              technical-designer / technical-designer-frontend / prd-creator (update mode)
                        ↓ (Design Doc only)
              code-verifier → document-reviewer
                        ↓ (Design Doc only)
              design-sync → [Stop: Final approval]

Scope Boundaries

Included in this skill:

  • Existing document identification and selection
  • Change content clarification
  • Document update with appropriate agent (update mode)
  • Document review with document-reviewer
  • Consistency verification with design-sync (Design Doc only)

Out of scope (redirect to appropriate skills):

  • New requirement analysis
  • Work planning or implementation

Responsibility Boundary: This skill completes when the user approves completing the update.

Target document: $ARGUMENTS

Execution Flow

Step 1: Target Document Identification

Discover the existing documents under docs/design/, docs/prd/, and docs/adr/.

Decision flow:

SituationAction
$ARGUMENTS specifies a pathUse specified document
$ARGUMENTS describes a topicSearch documents matching the topic
Multiple candidates foundPresent options with AskUserQuestion
No documents foundReport and end (document creation is out of scope)

Step 2: Document Type and Layer Determination

Determine type from document path, then determine the layer to select the correct update agent:

Path PatternTypeUpdate AgentNotes
docs/design/*.mdDesign Doctechnical-designer or technical-designer-frontendSee layer detection below
docs/prd/*.mdPRDprd-creator-
docs/adr/*.mdADRtechnical-designer or technical-designer-frontendSee layer detection below

Layer detection (for Design Doc and ADR): Read the document and determine its layer from content signals:

  • Frontend (→ technical-designer-frontend): Document title/scope mentions React, components, UI, frontend; or file contains component hierarchy, state management, UI interactions
  • Backend (→ technical-designer): All other cases (API, data layer, business logic, infrastructure)

ADR Update Guidance:

  • Minor changes (clarification, typo fix, small scope adjustment): Update the existing ADR file
  • Major changes (decision reversal, significant scope change): Create a new ADR that supersedes the original

Step 3: Change Content Clarification

Determine which sections need updating, the reason for the change, and the expected outcome after the update. Derive them from the request and the target document. Use AskUserQuestion only for an item the request and document leave undetermined, and only when a different answer would change which sections are updated or what the update must achieve.

Pass the resulting items, covered sections, and any stated total size budget to update or revision agents. Before the next approval gate, map every diff hunk to an approved item or required consistency update. Remove accidental unmapped changes; when a necessary change would alter the requested document outcome or explicit size constraint, return to the request clarification gate.

Step 4: Document Update

Invoke the update agent determined in Step 2:

subagent_type: [Update Agent from Step 2]
description: "Update [Type from Step 2]"
prompt: |
  Operation Mode: update
  Existing Document: [path from Step 1]

  ## Changes Required
  [Step 3 statements for the sections to update, reason, and expected outcome, copied verbatim]

  Update the document to reflect the specified changes.
  Add change history entry.

Step 5: Document Review

For Design Doc updates only: Before document-reviewer, invoke code-verifier:

subagent_type: code-verifier
description: "Verify updated Design Doc"
prompt: |
  doc_type: design-doc
  document_path: [path from Step 1]
  Verify the updated Design Doc against current codebase.

  Verification focus: Pay special attention to literal identifier referential
  integrity in the updated sections (paths, endpoints, type names, config keys).

Store output as: $CODE_VERIFICATION_OUTPUT

Invoke document-reviewer with the applicable exact shape:

  • Design Doc: doc_type: DesignDoc, target: [path from Step 1], review_context: update, and verification_evidence: $CODE_VERIFICATION_OUTPUT.
  • PRD: doc_type: PRD and target: [path from Step 1].
  • ADR: doc_type: ADRBatch, targets: [path from Step 1], and review_context: update.

For each type, review consistency of the changed sections and their dependent statements, governing requirements, and change history.

Store output as: $STEP_5_OUTPUT

On review result:

  • Pass → Proceed to Step 6
  • Needs revision → Apply the Review Resolution Gate. Return to Step 4 when apply findings exist, using the following prompt:
    subagent_type: [Update Agent from Step 2]
    description: "Revise [Type from Step 2]"
    prompt: |
      Operation Mode: update
      Existing Document: [path from Step 1]
    
      ## Adjudicated Review Findings
      [complete reviewer finding objects verbatim, with only their orchestrator dispositions added]
    
      Treat these findings as the complete revision scope and preserve adjacent content.
  • On re-review pass prior_feedback as [{id, disposition, reason?, evidence}]
  • All actionable findings are decline → Proceed to Step 6

Step 6: Consistency Verification and Final Approval [Stop: Final approval]

For Design Doc only, invoke design-sync first:

subagent_type: design-sync
description: "Verify consistency"
prompt: "source_design: [path from Step 1]"

When conflicts are detected, apply the Review Resolution Gate and follow its bounded verifier handoff and convergence rules, returning selected corrections to Step 4 for the owning document.

For every document type, present the updated document, the review outcome, any resolved declines, and the sync result when one ran. This is the only approval gate in the flow: wait for the user's decision before completing.

Error Handling

ErrorAction
Target document not foundReport and end (document creation is out of scope)
Sub-agent update failsLog failure, present error to user, retry once

Completion Criteria

  • Identified target document
  • Change content determined from the request, the document, or a question whose answer changed the update
  • Updated document with appropriate agent (update mode)
  • Executed code-verifier before document-reviewer (Design Doc only)
  • Executed document-reviewer and addressed feedback
  • Executed design-sync for consistency verification (Design Doc only)
  • Obtained user approval to complete the update

Output Example

Document update completed.

  • Updated document: docs/design/[document-name].md
  • Approval: user approved completing the update

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

Frequently asked questions

What does the Recipe Update Doc AI skill do?

Update existing design documents (Design Doc / PRD / ADR) with review

Why use Recipe Update Doc on TypingMind?

Because you install it once and use it with any model. Recipe Update Doc 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 Update Doc 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-update-doc. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recipe Update Doc?

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 Update Doc?

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

Is the Recipe Update Doc 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇