ralph-tui-create-json

subsy/ralph-tui · updated Apr 8, 2026

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$npx skills add https://github.com/subsy/ralph-tui --skill ralph-tui-create-json
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summary

Convert PRDs to prd.json format for ralph-tui autonomous execution.

  • Extracts quality gates (universal and UI-specific commands) from PRD and appends them to every story's acceptance criteria
  • Outputs a flat JSON structure with \"name\" and \"userStories\" at root level, ready for ralph-tui run --prd <path>
  • Enforces right-sized stories completable in one agent iteration; splits oversized work into schema, backend, and UI layers
  • Sets up story dependencies via dependsOn array to
skill.md

Ralph TUI - Create JSON Tasks

Converts PRDs to prd.json format for ralph-tui autonomous execution.

Note: This skill is bundled with ralph-tui's JSON tracker plugin. Future tracker plugins (Linear, GitHub Issues, etc.) will bundle their own task creation skills.

⚠️ CRITICAL: The output MUST be a FLAT JSON object with "name" and "userStories" at the ROOT level. DO NOT wrap content in a "prd" object or use "tasks" array. See "Schema Anti-Patterns" section below.


The Job

Take a PRD (markdown file or text) and create a prd.json file:

  1. Extract Quality Gates from the PRD's "Quality Gates" section
  2. Parse user stories from the PRD
  3. Append quality gates to each story's acceptance criteria
  4. Set up dependencies between stories
  5. Output ready for ralph-tui run --prd <path>

Step 1: Extract Quality Gates

Look for the "Quality Gates" section in the PRD:

## Quality Gates

These commands must pass for every user story:
- `pnpm typecheck` - Type checking
- `pnpm lint` - Linting

For UI stories, also include:
- Verify in browser using dev-browser skill

Extract:

  • Universal gates: Commands that apply to ALL stories (e.g., pnpm typecheck)
  • UI gates: Commands that apply only to UI stories (e.g., browser verification)

If no Quality Gates section exists: Ask the user what commands should pass, or use a sensible default like npm run typecheck.


Output Format

The JSON file MUST be a FLAT object at the root level:

{
  "name": "[Project name from PRD or directory]",
  "branchName": "ralph/[feature-name-kebab-case]",
  "description": "[Feature description from PRD]",
  "userStories": [
    {
      "id": "US-001",
      "title": "[Story title]",
      "description": "As a [user], I want [feature] so that [benefit]",
      "acceptanceCriteria": [
        "Criterion 1 from PRD",
        "Criterion 2 from PRD",
        "pnpm typecheck passes",
        "pnpm lint passes"
      ],
      "priority": 1,
      "passes": false,
      "notes": "",
      "dependsOn": []
    },
    {
      "id": "US-002",
      "title": "[UI Story that depends on US-001]",
      "description": "...",
      "acceptanceCriteria": [
        "...",
        "pnpm typecheck passes",
        "pnpm lint passes",
        "Verify in browser using dev-browser skill"
      ],
      "priority": 2,
      "passes": false,
      "notes": "",
      "dependsOn": ["US-001"]
    }
  ]
}

CRITICAL: Schema Anti-Patterns (DO NOT USE)

The following patterns are INVALID and will cause validation errors:

❌ WRONG: Wrapper object

{
  "prd": {
    "name": "...",
    "userStories": [...]
  }
}

This wraps everything in a "prd" object. DO NOT DO THIS. The "name" and "userStories" fields must be at the ROOT level.

❌ WRONG: Using "tasks" instead of "userStories"

{
  "name": "...",
  "tasks": [...]
}

The array is called "userStories", not "tasks".

❌ WRONG: Complex nested structures

{
  "metadata": {...},
  "overview": {...},
  "migration_strategy": {
    "phases": [...]
  }
}

Even if the PRD describes phases/milestones/sprints, you MUST flatten these into a single "userStories" array.

❌ WRONG: Using "status" instead of "passes"

{
  "userStories": [{
    "id": "US-001",
    "status": "open"  // WRONG!
  }]
}

Use "passes": false for incomplete stories, "passes": true for completed.

✅ CORRECT: Flat structure at root

{
  "name": "Android Kotlin Migration",
  "branchName": "ralph/kotlin-migration",
  "userStories": [
    {"id": "US-001", "title": "Create Scraper interface", "passes": false, "dependsOn": []},
    {"id": "US-002", "title": "Implement WeebCentralScraper", "passes": false, "dependsOn": ["US-001"]}
  ]
}

Story Size: The #1 Rule

Each story must be completable in ONE ralph-tui iteration (~one agent context window).

Ralph-tui spawns a fresh agent instance per iteration with no memory of previous work. If a story is too big, the agent runs out of context before finishing.

Right-sized stories:

  • Add a database column + 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 can't describe the change in 2-3 sentences, it's too big.


Dependencies with dependsOn

Use the dependsOn array to specify which stories must complete first:

{
  "id": "US-002",
  "title": "Create API endpoints",
  "dependsOn": ["US-001"],  // Won't be selected until US-001 passes
  ...
}

Ralph-tui will:

  • Show US-002 as "blocked" until US-001 completes
  • Never select US-002 for execution while US-001 is open
  • Include "Prerequisites: US-001" in the prompt when working on US-002

Correct dependency order:

  1. Schema/database changes (no dependencies)
  2. Backend logic (depends on schema)
  3. UI components (depends on backend)
  4. Integration/polish (depends on UI)

Acceptance Criteria: Quality Gates + Story-Specific

Each story's acceptance criteria should include:

  1. Story-specific criteria from the PRD (what this story accomplishes)
  2. Quality gates from the PRD's Quality Gates section (appended at the end)

Good criteria (verifiable):

  • "Add status column to tasks table with default 'open'"
  • "Filter dropdown has options: All, Open, Closed"
  • "Clicking delete shows confirmation dialog"

Bad criteria (vague):

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

Conversion Rules

  1. Extract Quality Gates from PRD first
  2. Each user story → one JSON entry
  3. IDs: Sequential (US-001, US-002, etc.)
  4. Priority: Based on dependency order (1 = highest)
  5. dependsOn: Array of story IDs this story requires
  6. All stories: passes: false and empty notes
  7. branchName: Derive from feature name, kebab-case, prefixed with ralph/
  8. Acceptance criteria: Story criteria + quality gates appended
  9. UI stories: Also append UI-specific gates (browser verification)

Output Location

Default: ./tasks/prd.json (alongside the PRD markdown files)

This keeps all PRD-related files together in the tasks/ directory.

Or specify a different path - ralph-tui will use it with:

ralph-tui run --prd ./path/to/prd.json

Example

Input PRD:

# PRD: Task Priority System

Add priority levels to tasks.

## Quality Gates

These commands must pass for every user story:
- `pnpm typecheck` - Type checking
- `pnpm lint` - Linting

For UI stories, also include:
- Verify in browser using dev-browser skill

## User Stories

### US-001: Add priority field to database
**Description:** As a developer, I need to store task priority.

**Acceptance Criteria:**
- [ ] Add priority column: 1-4 (default 2)
- [ ] Migration runs successfully

### US-002: Display priority badge on task cards
**Description:** As a user, I want to see task priority at a glance.

**Acceptance Criteria:**
- [ ] Badge shows P1/P2/P3/P4 with colors
- [ ] Badge visible without hovering

### US-003: Add priority filter dropdown
**Description:** As a user, I want to filter tasks by priority.

**Acceptance Criteria:**
- [ ] Filter dropdown: All, P1, P2, P3, P4
- [ ] Filter persists in URL

Output prd.json:

how to use ralph-tui-create-json

How to use ralph-tui-create-json on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add ralph-tui-create-json
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/subsy/ralph-tui --skill ralph-tui-create-json

The skills CLI fetches ralph-tui-create-json from GitHub repository subsy/ralph-tui and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/ralph-tui-create-json

Reload or restart Cursor to activate ralph-tui-create-json. Access the skill through slash commands (e.g., /ralph-tui-create-json) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.734 reviews
  • Liam Haddad· Dec 28, 2024

    ralph-tui-create-json fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Pratham Ware· Dec 24, 2024

    ralph-tui-create-json is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Yusuf Malhotra· Dec 12, 2024

    ralph-tui-create-json has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Layla Li· Nov 19, 2024

    We added ralph-tui-create-json from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Nov 15, 2024

    Keeps context tight: ralph-tui-create-json is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Arya Khanna· Nov 11, 2024

    I recommend ralph-tui-create-json for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Olivia Chawla· Nov 3, 2024

    Useful defaults in ralph-tui-create-json — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Yusuf Sethi· Oct 22, 2024

    ralph-tui-create-json is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Layla Abbas· Oct 10, 2024

    Solid pick for teams standardizing on skills: ralph-tui-create-json is focused, and the summary matches what you get after install.

  • Dhruvi Jain· Oct 6, 2024

    ralph-tui-create-json has been reliable in day-to-day use. Documentation quality is above average for community skills.

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