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Prd Planner

Community
zhaono1
prd-planner

Creates PRDs using persistent file-based planning. Use when user explicitly says "PRD", "product requirements document", or "产品需求文档". Combines PRD methodology with planning-with-files to avoid context switching.

Overview

Publisherzhaono1
Repositoryagent-playbook
Skill nameprd-planner
Stars
79
Forks
12
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

    Published by zhaono1 on GitHub. Read the source before you install it.

Installation

Install the Prd Planner 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/zhaono1/agent-playbook.git /tmp/agent-playbook
mkdir -p .claude/skills
cp -r /tmp/agent-playbook/skills/prd-planner .claude/skills/prd-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd Planner 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 Prd Planner 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 Prd Planner 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.

PRD Planner

A PRD creation skill that uses persistent file-based planning to maintain coherent thinking and avoid "left-brain vs right-brain" context switching issues.

When This Skill Activates

This skill activates when you:

  • Explicitly say "PRD", "prd", "create a PRD", or "产品需求文档"
  • Say "product requirements document" or "产品需求"
  • Mention "write a PRD for..."
  • Say "PRD planning" or "PRD 设计"

If user says "design solution" or "architecture design" without mentioning PRD, use architecting-solutions instead.

The Core Philosophy

"PRD creation should be traceable, coherent, and persistent - not scattered across context switches."

This skill combines:

  • PRD methodology (from architecting-solutions)
  • File-based persistence (from planning-with-files)

To create a single, coherent PRD creation workflow that doesn't lose context.

4-File Pattern for PRD Creation

For every PRD project, create FOUR files:

Pick a SCOPE (short, unique, kebab-case slug) and use it as a prefix for all files.

text
docs/{scope}-prd-notes.md     → Store research, requirements, findings, options
docs/{scope}-prd-task-plan.md → Track PRD creation phases and progress
docs/{scope}-prd.md           → Product requirements (what & why)
docs/{scope}-tech.md          → Technical design (how)

File Purposes

FilePurposeAudienceUpdated When
{scope}-prd-notes.mdRaw research, requirements, architecture options (A/B/C)Self + reviewersNew information gathered
{scope}-prd-task-plan.mdTrack progress, phases, checkboxes, timestampsPM + dev leadEach phase completion
{scope}-prd.mdProduct requirements (what & why), user flowsPM + stakeholders + devsAfter requirements are clear
{scope}-tech.mdTechnical design (API, data flow, implementation)Developers + architectsAfter architecture is decided

Workflow

┌─────────────────────────────────────────────────────────────────┐
│                     PRD Creation Workflow                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. Initialize → Create 4 files with template                   │
│  2. Requirements → Gather to {scope}-prd-notes.md               │
│  2.5 Edge Cases → Scan codebase, infer patterns, ask smartly    │
│  3. Analysis → Research best practices, save to notes           │
│  4. Design → Propose architecture options (A/B/C), save to notes │
│  5. PRD → Write product requirements to {scope}-prd.md          │
│  6. Tech → Write technical design to {scope}-tech.md            │
│  7. Validate → Review with user, finalize                       │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘
           All thinking persisted to files
              No context switching

Step 1: Initialize

Create the four files with templates:

{scope}-prd-task-plan.md

markdown
# PRD Task Plan: {Feature Name}

## Goal
Create a PRD and technical design for {feature description}.

## Owner
{User name/role}

## Phases
- [x] Phase 1: Initialize files ✓
- [ ] Phase 2: Gather requirements (CURRENT)
- [ ] Phase 3: Research & analysis
- [ ] Phase 4: Design solution
- [ ] Phase 5: Write PRD
- [ ] Phase 6: Write technical design
- [ ] Phase 7: Validate & finalize

## Status
**Currently in Phase 2** - Gathering requirements from user

## Progress Log
- {timestamp} - Phase 1 complete: Files initialized

{scope}-prd-notes.md

markdown
# PRD Notes: {Feature Name}

## Raw Requirements
(Add user requirements as they emerge)

## Constraints
(Add technical, business, time constraints)

## Inferred Patterns (from codebase)

| Edge Case | Source | Pattern Applied |
|-----------|--------|-----------------|
| (Filled after Step 2.5 codebase scan) | | |

## Edge Cases

### Auto-handled (following codebase patterns)
- (Filled after Step 2.5 analysis)

### Confirmed by User
- (Filled after user confirms edge case decisions)

### Open Questions
- (Track questions to ask user)

## Research Findings
(Add research on best practices, similar solutions)

## Architecture Options

- Option A: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

- Option B: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

- Option C: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

**Selected**: Option {X}

{scope}-prd.md

markdown
# PRD: {Feature Name}

> Status: DRAFT
> Last updated: {timestamp}

## Table of Contents
- [Problem Statement](#problem-statement)
- [Goals and Non-Goals](#goals-and-non-goals)
- [Success Criteria](#success-criteria)
- [Scope](#scope)
- [Requirements](#requirements)
- [User Flows](#user-flows)
- [Implementation Plan](#implementation-plan)

---

## Problem Statement
_To be filled after requirements gathering_

## Goals and Non-Goals
### Goals
- {Specific achievable outcomes}

### Non-Goals
- {Explicit exclusions}

## Success Criteria
_To be filled with measurable criteria_

## Scope
### In Scope
- {Specific items included}

### Out of Scope
- {Specific items excluded}

... (rest of PRD sections)

{scope}-tech.md

markdown
# Technical Design: {Feature Name}

> Status: DRAFT
> Last updated: {timestamp}

## Overview
{High-level technical approach}

## Key Components
{List major components and their responsibilities}

## API Design
{API signatures, request/response formats}

## Data Flow
{How data flows through the system}

## Implementation Details
{Specific implementation notes}

## Migration Plan
{If applicable, how to migrate from existing system}

Step 2: Gather Requirements

Ask clarifying questions and save responses to {scope}-prd-notes.md:

Core Questions to Ask

  1. Problem: What problem are we solving?
  2. Users: Who will use this?
  3. Success: How do we know it's successful?
  4. Constraints: Any technical/time/budget constraints?

Save each answer to {scope}-prd-notes.md under appropriate section.

Always update {scope}-prd-task-plan.md after gathering info:

markdown
- [x] Phase 2: Gather requirements ✓
- [ ] Phase 2.5: Edge case analysis (CURRENT)
- [ ] Phase 3: Research & analysis

Step 2.5: Context-Aware Edge Case Analysis

Before asking users about edge cases, scan the codebase first to infer existing patterns. This reduces redundant questions and ensures consistency with the project.

Detailed reference: See references/edge-case-analysis.md for full scanning commands and output formats.

Quick Process

  1. Scan codebase for existing patterns (delete strategy, error handling, empty states, pagination)
  2. Identify requirement type (CRUD, State Workflow, Async, Data Display, Form, File)
  3. Generate smart assumptions - patterns found in code don't need user confirmation
  4. Ask only when needed - no precedent, multiple patterns, or business decision required

When to Ask Users

ConditionAction
Pattern exists in codebaseAuto-apply, no question needed
No precedent foundAsk user with options
Multiple conflicting patternsAsk user to choose
Business rule requiredAsk user

Output to Notes File

Update {scope}-prd-notes.md with:

markdown
## Inferred Patterns (from codebase)
| Edge Case | Source | Pattern Applied |
|-----------|--------|-----------------|
| Delete | `src/models/User.ts:45` | Soft delete |

## Edge Cases
### Auto-handled (following codebase patterns)
- Empty list → Use existing EmptyState component

### Confirmed by User
- Concurrent edit: Last write wins (confirmed {date})

Update task plan:

markdown
- [x] Phase 2.5: Edge case analysis ✓
- [ ] Phase 3: Research & analysis (CURRENT)

Step 3: Research & Analysis

Research best practices and save to {scope}-prd-notes.md:

bash
# Search for similar implementations
grep -r "keyword" packages/ --include="*.ts"

# Search web for best practices
web search "best practices for {feature}"

Save findings to {scope}-prd-notes.md → Research Findings section.

Step 4: Design Solution

Propose architecture with trade-offs, save to {scope}-prd-notes.md:

markdown
## Architecture Options

- Option A: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

- Option B: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

- Option C: {Description}
  - Pros: {Advantages}
  - Cons: {Disadvantages}

**Selected**: Option {X} - because {reason}

Step 5: Write PRD

Read {scope}-prd-notes.md and synthesize into polished PRD:

markdown
1. Read {scope}-prd-notes.md to understand:
   - Requirements gathered
   - Research findings
   - Architecture decision (which option was selected)

2. Write {scope}-prd.md with:
   - Clear problem statement
   - Goals and Non-Goals (explicit exclusions)
   - Measurable success criteria (specific numbers/timings)
   - Scope (In Scope / Out of Scope)
   - Functional requirements
   - Non-functional requirements
   - User flows
   - Implementation plan (high level)

3. Reference tech doc: "See {scope}-tech.md for technical design"

Step 6: Write Technical Design

markdown
1. Read {scope}-prd-notes.md for selected architecture option

2. Write {scope}-tech.md with:
   - Overview (technical approach summary)
   - Key Components (what pieces, responsibilities)
   - API Design (signatures, contracts)
   - Data Flow (how data moves through system)
   - Implementation Details (specific notes)
   - Migration Plan (if applicable)

Step 7: Validate & Finalize

Review with user:

  1. Present PRD summary
  2. Ask for feedback
  3. Incorporate changes
  4. Mark Phase 7 complete

Important Rules

RuleBadGood
Use FilesKeep in memorySave to {scope}-prd-notes.md
Update PlanMove on without updateUpdate task-plan.md with checkbox
Read Before DecideDecide from memoryRead notes first
Separate DocsMix PRD + TechPRD for "what", Tech for "how"
Include OptionsJump to solutionDocument 2-3 options with pros/cons

Phase Transitions

Update {scope}-prd-task-plan.md after each phase with checkbox ✓ and timestamp.

Completing a PRD

Mark all phases complete, set status to "✅ COMPLETE", log final deliverables.

File Cleanup (Optional)

After PRD is complete:

  • Keep {scope}-prd-notes.md for reference (shows decision process)
  • Archive {scope}-prd-task-plan.md or delete
  • Final outputs are {scope}-prd.md and {scope}-tech.md

Quick Start Template

markdown
# PRD Task Plan: {Feature}

## Goal
Create PRD and technical design for {description}

## Phases
- [ ] Initialize 4 files
- [ ] Gather requirements
- [ ] Research & analysis
- [ ] Design solution (A/B/C options)
- [ ] Write PRD
- [ ] Write technical design
- [ ] Validate & finalize

## Status
Phase 1: Initializing files

Why This Works

ProblemSolution
Context switchingAll thinking in files, read anytime
Lost requirementsSaved to {scope}-prd-notes.md immediately
Inconsistent PRDsSame process, same structure
"Left brain vs right brain"One coherent workflow
Re-explaining contextFiles contain full context
Mixed concernsPRD (product) separate from Tech (implementation)
Hidden decisionsArchitecture options A/B/C documented

References


Auto-Trigger (Follow-up Metadata)

When this skill completes, record or run supported follow-ups:

  1. self-improving-agent (background) - Propose reusable patterns
  2. session-logger (auto) - Save session context when supported

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 Prd Planner AI skill do?

Creates PRDs using persistent file-based planning. Use when user explicitly says "PRD", "product requirements document", or "产品需求文档". Combines PRD methodology with planning-with-files to avoid context switching.

Why use Prd Planner on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaono1/agent-playbook/tree/main/skills/prd-planner. 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 Prd Planner?

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 Prd Planner?

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

Is the Prd Planner AI skill free?

Yes. It is published on GitHub by zhaono1 under the MIT 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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