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Prp Generator

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henkisdabro
prp-generator

Generate comprehensive Product Requirement Plans (PRPs) for feature implementation with thorough codebase analysis and external research. Use when the user requests a PRP, PRD, or detailed implementation plan for a new feature. Conducts systematic research, identifies patterns, and creates executable validation gates for one-pass implementation success. Do NOT use for client discovery, requirements gathering, or scope definition - those need a separate clarification pass before the PRP step.

Overview

Publisherhenkisdabro
Repositorywookstar-claude-plugins
Skill nameprp-generator
Stars
88
Forks
12
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by henkisdabro on GitHub. Read the source before you install it.

Installation

Install the Prp Generator 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/henkisdabro/wookstar-claude-plugins.git /tmp/wookstar-claude-plugins
mkdir -p .claude/skills
cp -r /tmp/wookstar-claude-plugins/plugins/developer/skills/prp-generator .claude/skills/prp-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prp Generator 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 Prp Generator 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 Prp Generator 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.

PRP Generator

Overview

Generates comprehensive Product Requirement Plans (PRPs) that enable AI agents to implement features in a single pass. Combines systematic codebase analysis with external research to create detailed, context-rich implementation blueprints.

When to Use

  • User requests a PRP, PRD, or detailed implementation plan
  • User asks to "plan out" or "design" a complex feature
  • Beginning significant feature development that benefits from structured planning
  • User provides a feature description file and asks for implementation guidance

Core Principle

Context is Everything: The implementing agent only receives the PRP content, training data knowledge, codebase access, and WebSearch. Your PRP must be self-contained with all necessary context, specific references, and executable validation gates.

Workflow

Phase 1: Understand the Feature

  1. Read the feature request - if a file path is given, read it completely; if verbal, clarify requirements
  2. Clarify ambiguities - use AskUserQuestion for unclear requirements, confirm tech stack, verify integration points
  3. Identify the core problem being solved and acceptance criteria

Phase 2: Codebase Analysis (Mandatory)

Goal: Understand existing patterns, conventions, and integration points.

Systematically analyse the codebase across five dimensions:

AreaWhat to Capture
Similar featuresFile paths, line numbers, code snippets, adaptations needed
ArchitectureDirectory conventions, component organisation, state management, API patterns
Coding conventionsTypeScript usage, component patterns, styling, import ordering, naming
Test patternsFramework, file naming, mock strategies, coverage expectations
ConfigurationDependencies, build setup, path aliases, TypeScript settings

For detailed sub-steps, examples, and documentation templates, see references/codebase-analysis-guide.md.

Also refer to references/research_methodology.md for the full research process.

Phase 3: External Research (Mandatory)

Goal: Find best practices, documentation, examples, and gotchas.

Research across four areas:

AreaKey Actions
Library documentationFind official docs for the SPECIFIC version from package.json; note version-specific gotchas
Implementation examplesSearch GitHub, StackOverflow, official examples; prefer recent, production-grade code
Best practicesSearch "[technology] best practices [current year]"; check OWASP for security
Performance and securityBundle size implications, runtime patterns, vulnerabilities, accessibility

Always document exact URLs, versions, and specific sections. See references/research_methodology.md for detailed guidance.

Phase 4: Ultra-Thinking (Critical)

STOP AND THINK DEEPLY BEFORE WRITING THE PRP.

Analyse integration points, implementation ordering, validation strategy, and context completeness. Verify the PRP will enable one-pass implementation without questions.

For the full set of analysis questions and the quality checklist, see references/quality-assessment.md.

Phase 5: Generate the PRP

Use assets/prp_template.md as the base structure. Populate all sections:

  1. Metadata - feature name, timeline, confidence score (1-10), date
  2. Executive Summary - 2-3 sentences with core value proposition
  3. Research Findings - codebase analysis (file:line refs) and external research (URLs, versions)
  4. Technical Specification - architecture, components, data models, API endpoints
  5. Implementation Blueprint - prerequisites, step-by-step with pseudocode, file changes, error handling, edge cases
  6. Testing Strategy - unit, integration, and manual testing approaches
  7. Validation Gates - must be EXECUTABLE commands (e.g. npm run test && npm run build)
  8. Success Criteria - clear, measurable checklist

Phase 6: Quality Scoring

Score the PRP for one-pass implementation success:

ScoreMeaning
9-10Exceptionally detailed, all context included, clear path, executable gates
7-8Very good, minor gaps, mostly clear implementation path
5-6Adequate, some ambiguity, may require clarification
3-4Incomplete research, missing context, unclear path
1-2Insufficient for implementation

If score is below 7: Go back and improve the PRP before delivering.

Phase 7: Save and Deliver

  1. Save the PRP to PRPs/[feature-name].md (kebab-case, create directory if needed)
  2. Deliver summary to user with: brief feature summary, file location, confidence score with rationale, and next steps

Common Pitfalls

PitfallBadGood
Vague references"There's a similar component somewhere""See UserProfile at src/components/UserProfile.tsx:45-67"
Missing versions"Use React Query""Use @tanstack/react-query v5.28.0"
Non-executable gates"Run tests and make sure they pass"npm run test && npm run build
Generic advice"Follow React best practices""Use named exports (see src/components/Button.tsx:1)"
Incomplete researchSkipping codebase analysisThoroughly document existing patterns
Missing gotchasAssuming smooth implementationDocument known issues and edge cases

Example Usage

User: "Create a PRP for adding dark mode support to the application"

  1. Clarify: "Should dark mode preference persist across sessions? Should it respect system preferences?"
  2. Research codebase for theme-related code
  3. Research external resources (dark mode best practices, library options)
  4. Ultra-think about implementation approach
  5. Generate comprehensive PRP using template
  6. Score the PRP
  7. Save to PRPs/dark-mode-support.md
  8. Deliver summary with confidence score

Resources

ResourceDescription
assets/prp_template.mdBase template for all PRPs
references/research_methodology.mdDetailed research guidance and best practices
references/codebase-analysis-guide.mdDetailed codebase analysis sub-steps and examples
references/quality-assessment.mdUltra-thinking analysis questions and quality checklist

Key Reminders

  • Research is mandatory - never skip codebase or external research
  • Be specific - always include file paths, line numbers, URLs, versions
  • Think deeply - Phase 4 (Ultra-Thinking) is critical for success
  • Validate everything - all validation gates must be executable
  • Score honestly - if confidence is below 7, improve the PRP
  • Context is king - the implementer only has what you put in the PRP

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prp Generator AI skill do?

Generate comprehensive Product Requirement Plans (PRPs) for feature implementation with thorough codebase analysis and external research. Use when the user requests a PRP, PRD, or detailed implementation plan for a new feature. Conducts systematic research, identifies patterns, and creates executable validation gates for one-pass implementation success. Do NOT use for client discovery, requirements gathering, or scope definition - those need a separate clarification pass before the PRP step.

Why use Prp Generator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/henkisdabro/wookstar-claude-plugins/tree/main/plugins/developer/skills/prp-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prp Generator?

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 Prp Generator?

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

Is the Prp Generator AI skill free?

It is published on GitHub by henkisdabro. 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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