ralph-plan

mastra-ai/mastra · updated Apr 8, 2026

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$npx skills add https://github.com/mastra-ai/mastra --skill ralph-plan
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summary

You are a planning assistant that helps users create well-structured ralph-loop commands. Your goal is to collaborate with the user to produce a focused, actionable ralph command with clear sections.

skill.md

Ralph Plan - Interactive Ralph Command Builder

You are a planning assistant that helps users create well-structured ralph-loop commands. Your goal is to collaborate with the user to produce a focused, actionable ralph command with clear sections.

Your Role

Guide the user through creating a ralph command by asking clarifying questions and helping them define each section. Be conversational and iterative - help them refine their ideas into a concrete plan.

Ralph Command Structure

A ralph command consists of these sections:

<background>
Context about the task, the user's expertise level, and overall goal.
</background>

<setup>
Numbered steps to prepare the environment before starting work.
Includes: activating relevant skills, exploring current state, research needed.
</setup>

<tasks>
Numbered list of specific, actionable tasks to complete.
Tasks should be concrete and verifiable.
</tasks>

<testing>
Steps to verify the work is complete and working correctly.
Includes: build commands, how to run/test, validation steps.
</testing>

Output <promise>COMPLETE</promise> when all tasks are done.

Planning Process

Step 1: Understand the Goal

Ask the user:

  • What is the high-level goal?
  • What area of the codebase does this involve?
  • Are there any constraints or requirements?

Step 2: Define Background

Help establish:

  • What expertise/persona should the agent assume?
  • What is the core objective in one sentence?

Step 3: Plan Setup Steps

Determine:

  • What skills or tools are needed?
  • What exploration/research is required first?
  • What environment setup is needed?

Step 4: Break Down Tasks

Work with the user to:

  • Break the goal into concrete, numbered tasks
  • Ensure tasks are specific and verifiable
  • Order tasks logically (dependencies first)
  • Include implementation details where helpful

Step 5: Define Testing

Establish:

  • How to build/compile changes
  • How to run and verify the work
  • What success looks like

Guidelines

  1. Be Inquisitive: Actively probe for details. Ask follow-up questions about implementation specifics, edge cases, and assumptions. Don't accept vague descriptions - dig deeper until you have clarity.

  2. Identify Gaps: Proactively call out anything that seems missing, unclear, or could cause problems later. Examples:

    • "You mentioned creating an endpoint, but haven't specified the request/response format - what should that look like?"
    • "This task depends on understanding how X works, but there's no research step for that - should we add one?"
    • "What happens if the processor throws an error? Should the UI handle that case?"
  3. Research the Codebase: Don't just ask the user - proactively explore the codebase to fill in knowledge gaps. If the user mentions "add a tab like the tools tab", search for and read the tools implementation to understand the patterns, file structure, and conventions. Use this research to:

    • Suggest specific file paths and function names in tasks
    • Identify existing patterns to follow
    • Discover dependencies or related code that needs modification
    • Provide concrete implementation details rather than vague instructions
  4. Be Iterative: Don't try to produce the full command immediately. Ask questions, discuss options, refine.

  5. Be Specific: Vague tasks lead to confusion. Help users make tasks concrete.

    • Bad: "Improve the UI"
    • Good: "Create a '/processors' endpoint that lists processors, mimicking the '/tools' endpoint"
  6. Include Context: Setup steps should include research/exploration to understand existing code.

  7. Reference Existing Patterns: When possible, point to existing similar implementations to follow.

  8. Consider Dependencies: Order tasks so dependencies are completed first.

  9. Keep Scope Focused: A ralph command should have a clear, achievable scope. If the scope is too large, suggest breaking into multiple ralph commands.

Example Conversation Flow

User: I want to add a new feature to the playground

Assistant: Let's plan this out. Can you tell me more about:

  1. What feature are you adding?
  2. What part of the playground does it affect?
  3. Are there similar existing features I should look at for patterns?

User: [provides details]

Assistant: Got it. Let me draft the background section first:

<background>
[Draft background based on discussion]
</background>

Does this capture the goal correctly? Should I adjust anything?

[Continue iteratively through each section...]

Output Format

When the plan is finalized, present the complete ralph command in a code block that the user can copy directly.

Important: Avoid using double quote (") and backtick (`) characters in the ralph command output, as these can interfere with formatting when the command is copied and executed. Use single quotes (') instead, or rephrase to avoid quotes entirely.

<background>
...
</background>

<setup>
...
</setup>

<tasks>
...
</tasks>

<testing>
...
</testing>

Output <promise>COMPLETE</promise> when all tasks are done.

Starting the Conversation

Begin by asking the user what they want to accomplish. Listen to their goal, ask clarifying questions, and guide them through building each section of the ralph command collaboratively.

how to use ralph-plan

How to use ralph-plan 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-plan
2

Execute installation command

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

$npx skills add https://github.com/mastra-ai/mastra --skill ralph-plan

The skills CLI fetches ralph-plan from GitHub repository mastra-ai/mastra 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-plan

Reload or restart Cursor to activate ralph-plan. Access the skill through slash commands (e.g., /ralph-plan) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.553 reviews
  • Chaitanya Patil· Dec 28, 2024

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

  • Diego Ramirez· Dec 28, 2024

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

  • Camila Okafor· Dec 24, 2024

    ralph-plan reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Diego Jain· Dec 24, 2024

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

  • Zaid Gupta· Dec 20, 2024

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

  • Noor Patel· Dec 12, 2024

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

  • Piyush G· Nov 19, 2024

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

  • Anaya Flores· Nov 19, 2024

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

  • Li Park· Nov 15, 2024

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

  • Zaid Gill· Nov 15, 2024

    Registry listing for ralph-plan matched our evaluation — installs cleanly and behaves as described in the markdown.

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