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Recipe Front Design

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shinpr
recipe-front-design

Execute from repository evidence through applicable UI Spec and optional ADR decisions to complete frontend Design Doc approval

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerecipe-front-design
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 Front Design 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-front-design .claude/skills/recipe-front-design
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recipe Front Design 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 Front Design 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 Front Design 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: documentation-criteria before document routing or creation. Execute Skill: llm-friendly-context before writing Agent prompts, handoffs, or generated artifacts. Execute Skill: subagents-orchestration-guide before invoking agents or resolving findings. Before the first finding disposition, read references/review-resolution.md from the loaded subagents-orchestration-guide skill.

Outcome and Ownership

Coordinate a Medium/Large frontend design from evidence to an applicable UI Spec and approved Design Doc. The user owns product requirements and exclusions; the orchestrator owns convergence readiness, Structural Scale, document routing, ADR qualification, evidence selection, and Review Resolution. Named specialists own semantic investigation and artifacts.

The frontend Design Doc always carries the complete implementation design. An ADR batch narrows qualifying technical choices; an applicable UI Spec owns UI structure and behavior that remain to be designed.

Requirements: $ARGUMENTS

Flow

text
requirement source -> codebase-analyzer -> scope/document routing confirmation [Stop]
                                               |
                             conditional UI analysis -> UI Spec review [Stop]
                                               |
                                   optional ADR batch/review [Stop]
                                               |
              Design Doc -> code-verifier/Resolution -> document-reviewer
                                               |
                               design-sync -> approval [Stop]

Use Review Resolution for every actionable finding. Wait at each [Stop] for explicit user confirmation.

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.

Step 1: Select the Governing Requirement Source

Use the approved PRD path when one exists. Otherwise use the confirmed requirements verbatim.

Set confirmed_requirement_context to the approved PRD path exactly. Only when no approved PRD exists, use the orchestrator-confirmed convergence record unchanged.

Step 2: Collect Repository Decision Material

Invoke dev-workflows-frontend:codebase-analyzer once for the complete confirmed scope with exactly prd_path: [approved PRD path], or requirements: [confirmed requirements verbatim] when no approved PRD exists.

Require one valid JSON result and let the analyzer discover affected paths, responsibility boundaries, and cross-layer contracts. Treat focusAreas as existing-behavior safeguards rather than requirements.

This independent discovery keeps scope and option convergence grounded in repository evidence rather than the orchestrator's unverified implementation hypothesis.

Step 3: Determine UI Spec Applicability and Resolve UI Evidence

Apply the documentation-criteria UI Spec creation condition. When it does not apply, skip UI analysis and Step 5.

When a UI Spec applies, load and apply external-resource-context only when an external resource can change the current UI direction, component contract, or verification boundary. Otherwise use external_resource_refs: [].

Ask for prototype code only when it supplies an unresolved approved UI decision or the target cannot be determined from requirements, repository UI, and recorded resources. A missing optional prototype is not a stop condition.

Invoke dev-workflows-frontend:ui-analyzer with exactly one governing source:

text
prd_path: [approved PRD path]

or, when no approved PRD exists:

text
requirements: [confirmed requirements verbatim]

Add only an existing ui_spec_path, a decision-relevant prototype_path, and selected external_resource_refs or [].

text
ui_spec_path: [existing UI Spec path]
prototype_path: [decision-relevant path]
external_resource_refs: [selected references or []]

Step 4: Confirm Scope and ADR Decisions

Execute Skill: requirement-convergence. Build and judge the convergence record from the governing requirement source, repository analysis, and applicable UI analysis.

Judge all four convergence fields. Assign cost from Step 2 structural evidence and record its unknowns; run the hearing only for fields below ready.

Determine Structural Scale from outcomes and responsibility boundaries; file count is supporting evidence only. Resolve candidate decision points against the governing source, applicable code/UI simplifications, reuse, and invalidations; applicable UI facts may support or contradict the remaining options. Apply documentation-criteria Choice and Durability filters only after this convergence and record passing points as adrDecisionPoints; an empty list is valid.

Present the requirement-convergence Scope Confirmation. Place affected responsibilities, applicable simplifications with their conditions, and qualifying ADR points or none under Decision evidence; place Structural Scale, UI Spec applicability, and the recommended document route under Workflow. Offer proceed, or correct and re-run. Ask a question only when its answer can change a convergence field, the confirmed outcome, or scope. Continue only when every convergence field is ready or weak-but-explicit. [Stop: Scope confirmation].

Step 5: Create and Approve the UI Spec

Run this step only when Step 3 determined that a UI Spec applies.

Invoke dev-workflows-frontend:ui-spec-designer with exact inputs:

text
confirmed_requirement_context: [the value fixed in Step 1]
ui_analysis: [complete Step 3 UI analyzer JSON unchanged]
codebase_analysis: [complete Step 2 codebase-analyzer JSON unchanged]
prototype_path: [decision-relevant path from Step 3 exactly, or absent]
prototype_reference_strength: [binding | reference, present with a prototype_path]
external_resource_refs: [selected Step 3 reference records unchanged, or []]

Invoke dev-workflows-frontend:document-reviewer with exact inputs: doc_type: UISpec and target as the UI Spec path returned by ui-spec-designer, unchanged. pass presents the UI Spec with issues: []; needs_revision applies Review Resolution and re-reviews after correction; rejected resolves the governing-source conflict before another review. [Stop: UI Spec approval].

Step 6: Create and Approve an ADR Batch When Needed

When adrDecisionPoints is non-empty:

  1. Route shared/backend-owned points to technical-designer first, then frontend-owned points to technical-designer-frontend. Invoke each owner with exact inputs: document_to_create: ADRBatch; confirmed_requirement_context; its ordered decision_points confirmed in Step 4, unchanged; decision_materials as the corresponding Step 2 decisionMaterials.candidateDecisionPoints objects copied unchanged in that order; and ui_spec_path only when the approved UI Spec constrains that owner's decision. Run owner batches serially so each batch allocates ADR numbers after the preceding batch exists.
  2. Collect all returned paths and invoke dev-workflows-frontend:document-reviewer once with exact inputs: doc_type: ADRBatch, targets: [all paths], and confirmed_requirement_context. The reviewer follows the approved UI Spec cited by the ADRs when it can change the decision review.
  3. Route the reviewer verdict first: pass proceeds with issues: []; needs_revision applies Review Resolution, updates one ADR per path serially, and re-reviews the complete batch; rejected resolves the governing-source conflict before another review.
  4. Present one batch decision only after a pass review. [Stop: ADR batch approval].
  5. After user approval, set every ADR status to Accepted and verify the status update.

Step 7: Create the Frontend Design Doc

Create the complete frontend MVP implementation design from reviewed artifacts and unchanged repository/UI evidence; this keeps the Design Doc traceable to approved sources instead of an orchestrator-authored shadow design.

Invoke dev-workflows-frontend:technical-designer-frontend with exactly:

  • document_to_create: DesignDoc;
  • confirmed_requirement_context;
  • structural_scale;
  • applicable approved ui_spec_path exactly and selected external-resource reference records unchanged;
  • adr_paths: [accepted paths or []];
  • codebase_analysis: [complete Step 2 JSON unchanged];
  • ui_analysis: [complete Step 3 JSON unchanged; omit when absent].

The Design Doc owns the full component-to-service implementation and retains all applicable downstream safeguards.

Step 8: Verify, Review, and Approve

Keep verifier observations unchanged so corrections remain traceable to observed repository evidence instead of becoming orchestrator-authored design instructions.

Invoke dev-workflows-frontend:code-verifier with doc_type: design-doc and document_path as the Design Doc path returned by technical-designer-frontend, unchanged, to verify current premises and feasibility while treating planned behavior as intent. Apply Review Resolution before document review; send applied findings to a fresh technical-designer-frontend update invocation with Operation Mode: update, Existing Document: [Design Doc path], and correction_findings: [complete findings unchanged except for their dispositions]. The designer applies its review-triggered bounded self-verification gate for an unverified decision-changing premise; this fresh designer is the sole correction specialist and selects the evidence route. Rerun verification after an applied correction with the previous complete result, dispositions, and correction diff or paths as prior_feedback, then build one verification_evidence object from the latest result. Continue at the Review Resolution convergence condition.

Invoke dev-workflows-frontend:document-reviewer with exact inputs: doc_type: DesignDoc; target as the returned Design Doc path unchanged; review_context: creation; original user requirements unchanged as requirements_verbatim; the Step 1 confirmed_requirement_context unchanged; the same unchanged codebase_analysis and optional ui_analysis supplied to the designer; and Step 8 verification_evidence unchanged. The reviewer follows an applicable UI Spec and accepted ADR paths cited by the Design Doc only when they can change an in-scope finding.

  • pass: continue.
  • needs_revision: apply Review Resolution, update through a fresh technical-designer-frontend invocation using the existing path and complete applied findings, and rerun verification/review for the affected boundary.
  • rejected: resolve technical governing-source conflicts through Review Resolution; ask the user only when confirmed outcome, desired-future requirements, and non-goals cannot all remain true and the user must choose which changes.

Invoke dev-workflows-frontend:design-sync with source_design as the returned Design Doc path unchanged, apply Review Resolution to actionable conflicts, and report SKIPPED distinctly when only one Design Doc exists.

Present the applicable UI Spec, Design Doc, accepted ADR paths, resolved limitations/declines, and sync result. [Stop: Design approval].

Completion Criteria

  • External and prototype evidence was requested only when it controlled a current decision.
  • Scope and Structural Scale were confirmed from outcomes and responsibility boundaries.
  • ADRs exist only for points passing both filters, and the batch received one review and approval.
  • An applicable UI Spec and a complete frontend Design Doc exist regardless of ADR need.
  • Applicable existing UI behavior, contracts, assumptions, states, equivalence, and verification safeguards reached the Design Doc.
  • Review Resolution routed only needs_revision issues into correction work.
  • All stop points received explicit user confirmation.

Frequently asked questions

What does the Recipe Front Design AI skill do?

Execute from repository evidence through applicable UI Spec and optional ADR decisions to complete frontend Design Doc approval

Why use Recipe Front Design on TypingMind?

Because you install it once and use it with any model. Recipe Front Design 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 Front Design 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-front-design. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Recipe Front Design?

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 Front Design?

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

Is the Recipe Front Design 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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