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Ralph

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hellangleZ
ralph

Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.

Overview

PublisherhellangleZ
Repositoryburn-in-cceverywhere-ralph
Skill nameralph
Stars
112
Forks
9
Bundled files
Instructions only
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 hellangleZ on GitHub. Read the source before you install it.

Installation

Install the Ralph 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/hellangleZ/burn-in-cceverywhere-ralph.git /tmp/burn-in-cceverywhere-ralph
mkdir -p .claude/skills
cp -r /tmp/burn-in-cceverywhere-ralph/skills/ralph .claude/skills/ralph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ralph 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 Ralph 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 Ralph 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.

Ralph PRD Converter

Converts existing PRDs to the prd.json format that Ralph uses for autonomous execution.


The Job

Take a PRD (markdown file or text) and convert it to prd.json in the current project root directory (where you would run ralph.sh).

IMPORTANT:

  • Output to ./prd.json (project root), NOT ~/.claude/skills/ralph/prd.json
  • Also create empty ./progress.txt if it doesn't exist
  • The project root is where the user is currently working (check with pwd)

Output Format

json
{
  "project": "[Project Name]",
  "branchName": "ralph/[feature-name-kebab-case]",
  "description": "[Feature description from PRD title/intro]",
  "userStories": [
    {
      "id": "US-001",
      "title": "[Story title]",
      "description": "As a [user], I want [feature] so that [benefit]",
      "acceptanceCriteria": [
        "Criterion 1",
        "Criterion 2",
        "Typecheck passes"
      ],
      "priority": 1,
      "passes": false,
      "notes": ""
    }
  ]
}

Story Size: The Number One Rule

Each story must be completable in ONE Ralph iteration (one context window).

Ralph spawns a fresh Amp instance per iteration with no memory of previous work. If a story is too big, the LLM runs out of context before finishing and produces broken code.

Right-sized stories:

  • Add a database column and migration
  • Add a UI component to an existing page
  • Update a server action with new logic
  • Add a filter dropdown to a list

Too big (split these):

  • "Build the entire dashboard" - Split into: schema, queries, UI components, filters
  • "Add authentication" - Split into: schema, middleware, login UI, session handling
  • "Refactor the API" - Split into one story per endpoint or pattern

Rule of thumb: If you cannot describe the change in 2-3 sentences, it is too big.


Story Ordering: Dependencies First

Stories execute in priority order. Earlier stories must not depend on later ones.

Correct order:

  1. Schema/database changes (migrations)
  2. Server actions / backend logic
  3. UI components that use the backend
  4. Dashboard/summary views that aggregate data

Wrong order:

  1. UI component (depends on schema that does not exist yet)
  2. Schema change

Acceptance Criteria: Must Be Verifiable

Each criterion must be something Ralph can CHECK, not something vague.

Good criteria (verifiable):

  • "Add status column to tasks table with default 'pending'"
  • "Filter dropdown has options: All, Active, Completed"
  • "Clicking delete shows confirmation dialog"
  • "Unit tests written with >80% coverage"
  • "All tests pass"
  • "Typecheck passes"

Bad criteria (vague):

  • "Works correctly"
  • "User can do X easily"
  • "Good UX"
  • "Handles edge cases"

MANDATORY Acceptance Criteria

For EVERY story, include these at the end:

"Unit tests written for all new functions/components",
"All tests pass (npm test)",
"Test coverage >80% for new code",
"Typecheck passes",
"Lint passes with no errors"

For UI stories (components, pages), also include:

"Browser verification using chrome-devtools MCP - navigate to page and verify UI works"

For the FINAL story in the PRD, also include:

"Full test suite passes (all project tests)",
"Full E2E test with chrome-devtools MCP - test complete user flows",
"Build succeeds (npm run build)",
"Application works end-to-end"

Exceptions (can skip unit tests for):

  • Stories that ONLY configure environment variables or API keys
  • Stories that ONLY modify config files with no logic
  • For external API calls: mock the API, don't skip tests entirely

Conversion Rules

  1. Each user story becomes one JSON entry
  2. IDs: Sequential (US-001, US-002, etc.)
  3. Priority: Based on dependency order, then document order
  4. All stories: passes: false and empty notes
  5. branchName: Derive from feature name, kebab-case, prefixed with ralph/
  6. Always add: "Typecheck passes" to every story's acceptance criteria

Splitting Large PRDs

If a PRD has big features, split them:

Original:

"Add user notification system"

Split into:

  1. US-001: Add notifications table to database
  2. US-002: Create notification service for sending notifications
  3. US-003: Add notification bell icon to header
  4. US-004: Create notification dropdown panel
  5. US-005: Add mark-as-read functionality
  6. US-006: Add notification preferences page

Each is one focused change that can be completed and verified independently.


Example

Input PRD:

markdown
# Task Status Feature

Add ability to mark tasks with different statuses.

## Requirements
- Toggle between pending/in-progress/done on task list
- Filter list by status
- Show status badge on each task
- Persist status in database

Output prd.json:

json
{
  "project": "TaskApp",
  "branchName": "ralph/task-status",
  "description": "Task Status Feature - Track task progress with status indicators",
  "userStories": [
    {
      "id": "US-001",
      "title": "Add status field to tasks table",
      "description": "As a developer, I need to store task status in the database.",
      "acceptanceCriteria": [
        "Add status column: 'pending' | 'in_progress' | 'done' (default 'pending')",
        "Generate and run migration successfully",
        "Unit tests written for migration and schema",
        "All tests pass (npm test)",
        "Test coverage >80% for new code",
        "Typecheck passes",
        "Lint passes with no errors"
      ],
      "priority": 1,
      "passes": false,
      "notes": ""
    },
    {
      "id": "US-002",
      "title": "Display status badge on task cards",
      "description": "As a user, I want to see task status at a glance.",
      "acceptanceCriteria": [
        "Each task card shows colored status badge",
        "Badge colors: gray=pending, blue=in_progress, green=done",
        "Unit tests written for StatusBadge component",
        "All tests pass (npm test)",
        "Test coverage >80% for new code",
        "Typecheck passes",
        "Lint passes with no errors",
        "Browser verification using chrome-devtools MCP - navigate to task list and verify badges display correctly"
      ],
      "priority": 2,
      "passes": false,
      "notes": ""
    },
    {
      "id": "US-003",
      "title": "Add status toggle to task list rows",
      "description": "As a user, I want to change task status directly from the list.",
      "acceptanceCriteria": [
        "Each row has status dropdown or toggle",
        "Changing status saves immediately",
        "UI updates without page refresh",
        "Unit tests written for status toggle functionality",
        "All tests pass (npm test)",
        "Test coverage >80% for new code",
        "Typecheck passes",
        "Lint passes with no errors",
        "Browser verification using chrome-devtools MCP - toggle status and verify it saves"
      ],
      "priority": 3,
      "passes": false,
      "notes": ""
    },
    {
      "id": "US-004",
      "title": "Filter tasks by status (FINAL)",
      "description": "As a user, I want to filter the list to see only certain statuses.",
      "acceptanceCriteria": [
        "Filter dropdown: All | Pending | In Progress | Done",
        "Filter persists in URL params",
        "Unit tests written for filter functionality",
        "All tests pass (npm test)",
        "Test coverage >80% for new code",
        "Typecheck passes",
        "Lint passes with no errors",
        "Browser verification using chrome-devtools MCP - test filter dropdown",
        "Full test suite passes (all project tests)",
        "Full E2E test with chrome-devtools MCP - test complete task status flow: create task, change status, filter by status",
        "Build succeeds (npm run build)",
        "Application works end-to-end"
      ],
      "priority": 4,
      "passes": false,
      "notes": ""
    }
  ]
}

Archiving Previous Runs

Before writing a new prd.json, check if there is an existing one from a different feature:

  1. Read the current prd.json if it exists
  2. Check if branchName differs from the new feature's branch name
  3. If different AND progress.txt has content beyond the header:
    • Create archive folder: archive/YYYY-MM-DD-feature-name/
    • Copy current prd.json and progress.txt to archive
    • Reset progress.txt with fresh header

The ralph.sh script handles this automatically when you run it, but if you are manually updating prd.json between runs, archive first.


Checklist Before Saving

Before writing prd.json, verify:

  • Previous run archived (if prd.json exists with different branchName, archive it first)
  • Each story is completable in one iteration (small enough)
  • Stories are ordered by dependency (schema → backend → UI)
  • EVERY story has these acceptance criteria:
    • "Unit tests written for [specific functionality]"
    • "All tests pass (npm test)"
    • "Test coverage >80% for new code"
    • "Typecheck passes"
    • "Lint passes with no errors"
  • UI stories also have: "Browser verification using chrome-devtools MCP - [specific action]"
  • FINAL story also has:
    • "Full test suite passes (all project tests)"
    • "Full E2E test with chrome-devtools MCP - [test complete user flow]"
    • "Build succeeds (npm run build)"
    • "Application works end-to-end"
  • Final story title includes "(FINAL)" suffix
  • Acceptance criteria are verifiable (not vague)
  • No story depends on a later story

Frequently asked questions

What does the Ralph AI skill do?

Convert PRDs to prd.json format for the Ralph autonomous agent system. Use when you have an existing PRD and need to convert it to Ralph's JSON format. Triggers on: convert this prd, turn this into ralph format, create prd.json from this, ralph json.

Why use Ralph on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hellangleZ/burn-in-cceverywhere-ralph/tree/main/skills/ralph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ralph?

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 Ralph?

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

Is the Ralph AI skill free?

It is published on GitHub by hellangleZ. Check the repository for licensing terms. 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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