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Recipe Front Adjust

Community
shinpr
recipe-front-adjust

Adjust an already-implemented UI in-session with verification against the design source

Overview

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

Use it in TypingMind

Enable Recipe Front Adjust 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 Adjust 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 Adjust 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.

Context: UI adjustment on already-implemented features. The verification loop (edit → check against the design source → refine) runs in the parent session.

Execution Pattern

Core Identity: "I am a guided executor. I run the adjustment and the verification loop myself; subagents handle one-shot tasks."

Execution Protocol:

  1. Delegate to subagents (one-shot calls): quality-fixer-frontend.
  2. Run in the parent session (multi-step loops and user dialogs): external-resource hearing via AskUserQuestion, write-set inspection, scale judgment, adjustment-context approval, adjustment edits, verification against the design source, iteration until acceptance.
  3. Stop at every [Stop: ...] marker before proceeding.

Execution Gate

Complete Steps 1-7 in order for the confirmed adjustment outcome. Advance only through the current step's stated evidence, quality result, or user stop; skip work only when its stated condition is false. Report completion after every applicable Completion Criterion and retained-limitation retry is satisfied.

Workflow Overview

Adjustment request → conditional external resource evidence
                     existing-pattern and write-set inspection
                     structural boundary judgment on candidate write set
                     local existing-pattern adjustment → [Stop]
                     adjustment + verification (parent session)
                     quality-fixer-frontend (subagent: typecheck/lint/test)
                     commit

Scope Boundaries

Included in this skill:

  • External resource hearing per the external-resource-context skill
  • Existing-pattern and write-set inspection in the parent session
  • Structural boundary judgment via documentation-criteria
  • Adjustment edits and verification against the design source (run in this session)
  • Quality verification via quality-fixer-frontend
  • Commit the confirmed adjustment outcome

Responsibility Boundary: This skill completes when the confirmed adjustment is committed after its quality cycle and any retained proof limitation receives its final retry. Adjustment work is end-to-end within this recipe; parent session owns edits, verification loops, quality-result routing, and commits.

Escalation Boundary: Escalate to the full frontend design phase when the request crosses a responsibility or approved UI boundary, requires a complete Design Doc, or contains a technical choice that passes documentation-criteria's Choice and Durability filters.

Adjustment request: $ARGUMENTS

Execution Flow

Step 1: External Resource Hearing

Execute Skill: external-resource-context before running the hearing protocol.

Run the hearing protocol only when external evidence can change the current adjustment target or verification result. Otherwise continue with the existing repository/UI Spec evidence and record no external references.

Step 2: Determine the Route and Write Set

Execute Skill: documentation-criteria.

Inspect the named or current UI and the smallest sufficient repository evidence needed to identify the likely write set and preserved visible behavior. Include a generated artifact only when repository tooling shows that a candidate write triggers its generator. When the UI Spec creation condition applies, route to recipe-front-design and stop. Otherwise record the evidence-backed candidate write set for this existing-pattern adjustment.

Step 3: Scale Judgment

  1. Read the candidate write set from Step 2.
  2. Apply Structural Scale to the confirmed outcome and responsibility boundary. Use write-set count as supporting evidence only:
    • 0 files: The adjustment request did not map to any existing file. Escalate to the user with the message "No write target identified from the adjustment request. Please clarify which component(s) should change, or run the full frontend design phase if this is a new feature." Stop this recipe.
    • Direct adjustment: One coherent UI outcome follows existing component, state, interaction, and verification patterns inside one responsibility boundary. Continue directly to adjustment context even when generated or tightly coupled files increase the count.
    • Design required: The change crosses a responsibility or approved UI contract, coordinates independently valuable outcomes, or needs a durable technical choice between credible alternatives. Escalate to the full frontend design phase.

Step 4: Adjustment Context

No work plan. Build a minimal adjustment context for the parent session:

  • Adjustment request (verbatim)
  • Existing UI pattern and preserved visible behavior relevant to the adjustment
  • Evidence-backed affected files list from the candidate write set
  • External resources fetched_summary and access methods that the verification loop will use

Present the adjustment context to the user for review.

  • [STOP]: User confirms the adjustment context covers the work.

Step 5: Adjustment + Verification (parent session)

Execute Skill: frontend-ai-guide before planning or applying adjustment edits. Execute Skill: typescript-rules before planning or applying adjustment edits. Execute Skill: implementation-approach before planning or applying adjustment edits. Execute Skill: test-implement before adding or changing tests.

Implement the confirmed adjustment outcome across its affected files:

  1. Plan the edit from the confirmed adjustment context and relevant external resource (e.g., design origin's fetched_summary).
  2. Apply the edit using Edit / Write / MultiEdit on the affected files.
  3. Verify against external sources using whichever access method docs/project-context/external-resources.md declares for each axis:
    • Design origin: compare current rendering against the design source via the declared access method (e.g., design-tool MCP, WebFetch from a public URL, file read from a specification path)
    • Visual rendering: capture screenshot or run a smoke check via the declared visual verification method (e.g., browser MCP, E2E test runner CLI invoked via Bash, dev-server URL inspection, Storybook URL)
    • Design system tokens / variants: confirm against the declared design system source (e.g., design-system MCP, package import, Storybook URL, internal documentation path)
  4. Refine and re-verify until the adjustment matches the design source, or matches the user-confirmed adjustment target when no separate design source exists.
  5. When the complete adjustment matches the confirmed target, proceed to Step 6.

When the project-tier file declares no automated verification mechanism for an axis, ask the user to confirm the result manually, or use file-based comparison when a specification file is available.

Step 6: Quality Verification

  • Invoke quality-fixer-frontend using Agent tool
    • subagent_type: "dev-workflows-frontend:quality-fixer-frontend"
    • description: "Quality verification for confirmed adjustment"
    • direct_scope: Copy the confirmed adjustment request and preserved visible behavior from Step 4 unchanged.
    • governing_sources: Pass the existing UI and design source references used for the adjustment unchanged.
    • observable_verification: Pass the confirmed adjustment request from Step 4 and applicable acceptance criteria from the governing sources unchanged.
    • Pass qualityCommand when available (caller first, otherwise current task).
  • Route the quality-fixer-frontend response by status:
    • pass → proceed to Step 7
    • stub_detected → return to Step 5 to complete the confirmed adjustment, then re-invoke quality-fixer-frontend
    • verification_incomplete → retain the complete result for final retry and proceed to Step 7
    • blocked → Apply subagents-orchestration-guide Specialist Result Acceptance using the result's semantic evidence, changed files, and repository state

Step 7: Commit

Before committing, use repository state at the commit boundary as the primary evidence and account for every actual change by mapping it to the confirmed adjustment, preserved pattern, or a necessary dependency, test, or generated artifact. Every required change is ready for the adjustment commit, accidental changes introduced during the adjustment are removed, and existing worktree changes unrelated to the confirmed adjustment remain intact.

Commit the confirmed adjustment after pass or verification_incomplete. For the latter, derive and append one Verification-Limitation: <reason> and Verification-Affected: <affected check or command> trailer pair per retained limitation.

On continuation, reconstruct retained limitations from the verification trailers on commits already completed for this request. After the adjustment is committed, retry each retained verification limitation once with quality-fixer-frontend. When the retry returns pass, remove that limitation from retained state. Commit any resulting fixes through Steps 6→7, and include only a repeated limitation in the completion report.

Completion Criteria

  • External resource hearing executed (project-tier file written or update explicitly skipped)
  • UI Spec applicability and the candidate write set were determined from the requested UI and sufficient repository evidence
  • Structural boundary judgment applied; changes requiring complete design or a qualifying durable decision escalated
  • Adjustment context, including the affected files, was presented and confirmed once
  • The confirmed adjustment outcome is implemented; each declared verification mechanism ran, received manual confirmation where required, or retained its exact proof limitation after final retry
  • The confirmed adjustment completed quality-fixer-frontend before commit; retained proof limitations were retried and reported
  • The confirmed adjustment is committed

Output Example

Frontend adjustment completed.
- External resources: docs/project-context/external-resources.md (updated|unchanged)
- UI evidence: existing pattern [path], external sources [fetched|partial|not_recorded]
- Scale: direct existing-pattern adjustment
- Adjustment commit: [commit hash]
- Quality status: all passed | [remaining proof limitations]

Frequently asked questions

What does the Recipe Front Adjust AI skill do?

Adjust an already-implemented UI in-session with verification against the design source

Why use Recipe Front Adjust on TypingMind?

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

Which AI models can use Recipe Front Adjust?

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 Adjust?

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

Is the Recipe Front Adjust 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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