Ring:Planning Frontend Refactor logo

Ring:Planning Frontend Refactor

Organization
LerianStudio
ring:planning-frontend-refactor

Planning a frontend refactor: audits an existing React/Next.js frontend against Ring standards (architecture, design system, accessibility, testing) and produces a prioritized task list (findings.md + tasks.md) for ring:running-dev-cycle-frontend. Plans only — no edits. Use when an existing frontend needs to meet standards or an audit is requested. Skip for greenfield, single-file fixes, or backend (use ring:planning-backend-refactor).

Overview

PublisherLerianStudio
Repositoryring
Skill namering:planning-frontend-refactor
Stars
215
Forks
28
Bundled files
Instructions only
LicenseApache-2.0
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 LerianStudio on GitHub. Read the source before you install it.

Installation

Install the Ring:Planning Frontend Refactor 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/LerianStudio/ring.git /tmp/ring
mkdir -p .claude/skills
cp -r /tmp/ring/dev-team/skills/planning-frontend-refactor .claude/skills/lerianstudio-ring-planning-frontend-refactor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Planning Frontend Refactor 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 Ring:Planning Frontend Refactor 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 Ring:Planning Frontend Refactor 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.

Dev Refactor Frontend

When to use

  • User wants to refactor existing frontend project to follow standards
  • Legacy React/Next.js codebase needs modernization
  • Frontend project audit requested

Skip when

  • Greenfield project → Use /ring:planning-small-features or /ring:planning-large-features instead
  • Single file fix → Use ring:running-dev-cycle-frontend directly
  • Backend-only project → Use ring:planning-backend-refactor instead

Sequence

Runs before: ring:running-dev-cycle-frontend

Analyzes existing frontend codebase against Ring/Lerian standards and generates refactoring tasks for ring:running-dev-cycle-frontend.

You orchestrate. Agents analyze. NEVER use Bash/Grep/Read to analyze code — dispatch agents.

Gap Principle

Every divergence from Ring standards = a mandatory gap. No exceptions.

All divergences → FINDING-XXX → REFACTOR-XXX task → ring:running-dev-cycle-frontend input.

Architecture Pattern Applicability

Project TypeApply Frontend Standards?
Full React/Next.js App✅ YES — all frontend.md sections
Design System Library✅ YES
Landing page / static⚡ PARTIAL — directory + styling only
Utility / config package❌ NO

Standards Loading

Pre-fetch before any step:

WebFetch: https://raw.githubusercontent.com/LerianStudio/ring/main/CLAUDE.md
WebFetch: https://raw.githubusercontent.com/LerianStudio/ring/main/dev-team/docs/standards/frontend.md
WebFetch: testing-accessibility.md, testing-visual.md, testing-e2e.md, testing-performance.md

STOP if any fetch fails.

Execution Steps

Step 1: Validate Prerequisites

  • Check docs/PROJECT_RULES.md exists → STOP if missing
  • Detect UI library mode: read package.json
    • @your-org/design-systemdesign-system
    • Otherwise → fallback-only
  • If go.mod and no React → STOP: use ring:planning-backend-refactor

Step 2: Generate Codebase Report

Dispatch ring:codebase-explorer:

Generate comprehensive codebase report: project structure, React/Next.js patterns,
component architecture, state management, forms, styling, testing approach,
package.json dependencies. Output: docs/ring:planning-frontend-refactor/{timestamp}/codebase-report.md

Step 3: Dispatch Frontend Specialist Agents (parallel)

Verify codebase-report.md exists before dispatching.

Dispatch all 3 in ONE message:

yaml
Task 1: ring:frontend (MODE: ANALYSIS only)
  - Load frontend.md via WebFetch
  - Check all 19 sections per standards-coverage-table.md
  - Flag framework/library mismatches vs standards
  - File size enforcement: >1000 lines = ISSUE-XXX
  - UI Library Mode: {ui_library_mode}
  - Output: Standards Coverage Table + ISSUE-XXX per finding

Task 2: ring:qa-frontend (MODE: ANALYSIS only)
  - Check all 19 testing sections (ACC, VIS, E2E, PERF)
  - UI Library Mode: {ui_library_mode}
  - Output: Standards Coverage Table + ISSUE-XXX for gaps

Task 3: ring:ui-engineer (MODE: ANALYSIS only)
  - Check design system component usage compliance
  - If ui_library_mode = "fallback-only", check custom component WCAG 2.1 AA accessibility, responsive/layout fallback behavior, and design-token/theme fallback usage
  - For fallback-only mode, output ISSUE-XXX per violation plus a short note that frontend and qa-frontend own baseline implementation/testing coverage
  - Output: ISSUE-XXX for non-compliant usage

Step 4: Map Findings → Tasks

After all agents complete:

  1. Save reports to docs/ring:planning-frontend-refactor/{timestamp}/
  2. Map each ISSUE-XXX → FINDING-XXX
  3. Generate findings.md
  4. Map each FINDING-XXX → REFACTOR-XXX (1:1)
  5. Generate tasks.md (ring:running-dev-cycle-frontend compatible)

Findings template:

markdown
## FINDING-001: {Pattern Name} in {file_path}
- **Severity:** CRITICAL | HIGH | MEDIUM | LOW
- **File:** {path}:{line}
- **Current:** {code or description}
- **Expected:** {Ring standard}

Step 5: Visual Report + User Approval

Generate visual HTML summary → ring:visualizing. Present to user. Wait for explicit APPROVED.

Step 6: Save + Handoff

Save all artifacts. Handoff to ring:running-dev-cycle-frontend.

Severity Reference

SeverityCriteria
CRITICALSecurity risk, WCAG legal issue, build broken
HIGHMissing server components, Lighthouse < 80, wrong pattern
MEDIUMClient component overuse, missing snapshots
LOWNaming conventions, file organization

Frequently asked questions

What does the Ring:Planning Frontend Refactor AI skill do?

Planning a frontend refactor: audits an existing React/Next.js frontend against Ring standards (architecture, design system, accessibility, testing) and produces a prioritized task list (findings.md + tasks.md) for ring:running-dev-cycle-frontend. Plans only — no edits. Use when an existing frontend needs to meet standards or an audit is requested. Skip for greenfield, single-file fixes, or backend (use ring:planning-backend-refactor).

Why use Ring:Planning Frontend Refactor on TypingMind?

Because you install it once and use it with any model. Ring:Planning Frontend Refactor 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 Ring:Planning Frontend Refactor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/dev-team/skills/planning-frontend-refactor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ring:Planning Frontend Refactor?

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 Ring:Planning Frontend Refactor?

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

Is the Ring:Planning Frontend Refactor AI skill free?

Yes. It is published on GitHub by LerianStudio under the Apache-2.0 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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