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Prd Writing

Community
shawnpang
prd-writing

When the user needs to define a product feature, write a product requirements document, or translate an idea into a structured spec.

Overview

Publishershawnpang
Repositorystartup-founder-skills
Skill nameprd-writing
Stars
368
Forks
43
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Prd Writing 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.
    https://github.com/shawnpang/startup-founder-skills/tree/main/skills/prd-writing
  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/shawnpang/startup-founder-skills.git /tmp/startup-founder-skills
mkdir -p .claude/skills
cp -r /tmp/startup-founder-skills/skills/prd-writing .claude/skills/prd-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd Writing 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 Writing 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 Writing 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 Writing

When to Use

Activate when a founder or PM needs to turn a product idea, feature request, or strategic initiative into a structured Product Requirements Document. This includes situations where the user says things like "write a PRD," "spec out this feature," "define requirements for X," or "I need to document what we're building."

Context Required

  • From startup-context: company stage, target customer segments, current product state, team size, technical constraints.
  • From the user: the feature or initiative to spec, known user problems it addresses, any prior research or customer feedback, desired timeline, and scope preference (lightweight vs. full PRD).

Workflow

  1. Clarify scope level -- Ask whether this needs a lightweight PRD (early-stage exploration, 2-3 pages) or a full PRD (committed initiative, 5-8 pages). Default to lightweight if the company is pre-product-market-fit.
  2. Gather inputs -- Collect the problem statement, target users, any existing research, success criteria, and known constraints. Identify key contacts and their roles.
  3. Draft the 8-section PRD -- Write each section sequentially using the template below. Use accessible language suitable for a broad audience including engineering, design, and leadership.
  4. Flag assumptions -- Explicitly list key assumptions underlying each section. For each, state what evidence supports it and what would invalidate it.
  5. Review and refine -- Present the draft, invite feedback, and iterate on specific sections. State the PRD version and last-updated date.

Output Format

A structured PRD document with 8 sections:

Section Template

  1. Summary -- 2-3 sentence overview of what is being built and why it matters. Write for a broad audience.
  2. Contacts -- Key stakeholders with their roles and relevant context about their involvement.
  3. Background -- Context on the problem space: why now, what changed, what enables this initiative. Include competitive context on how others handle the same problem.
  4. Objective -- Goals, business and customer benefits, and strategic alignment. Define SMART success metrics tied to OKRs. Use the format: "Enable [user segment] to [action] resulting in [measurable outcome]."
  5. Market Segment(s) -- Define target users by problems and needs, not demographics. Describe primary and secondary segments with size estimates.
  6. Value Proposition(s) -- Map customer jobs addressed, gains provided, and pain points eliminated. Show competitive differentiation using frameworks like Value Curve analysis.
  7. Solution -- Feature descriptions, UX/prototypes, wireframes, user flows, and technology details when relevant. Include out-of-scope items explicitly. Document assumptions. Enumerate at least 5 edge cases.
  8. Release -- Phased rollout plan using relative timeframes (not exact dates). Define MVP vs. future iterations, feature flags, rollback criteria, and review checkpoints.

For lightweight PRDs, sections 2, 3, and 8 can be condensed to 2-3 sentences each.

Frameworks & Best Practices

  • Problem before solution. Spend 40% of the document on sections 1-5 (the "why") before touching section 7 (the "what"). A PRD that jumps to the solution is a spec, not a PRD.
  • One objective, not five. A PRD with multiple objectives is multiple PRDs. Split them. Each PRD should have a single primary metric it moves.
  • Market segments defined by needs. Describe who this is for based on the problems they face and jobs they hire the product to do, not by demographics or firmographics alone.
  • Value Proposition clarity. For each segment, explicitly state the customer jobs addressed, gains provided, and pains eliminated. Use the Value Curve to show where you differentiate from competitors.
  • Data-driven specificity. Replace vague language with specific numbers. "Improve retention" is not a metric; "Increase D7 retention from 25% to 35% within 8 weeks of launch" is.
  • Scope creep guard. Explicitly list what is NOT in scope. Revisit the out-of-scope list when stakeholders propose additions.
  • Relative timeframes over dates. Use phases and relative windows rather than exact calendar dates. This prevents false precision and allows flexibility.
  • Assumption tracking. List the top 3 assumptions underlying the PRD. For each, state supporting evidence and what would invalidate it.
  • Audience awareness. Engineers need technical constraints and edge cases. Designers need user flows and personas. Executives need the summary and metrics. Write for all three in a single document.
  • Living document. State the PRD version and last-updated date. PRDs that never change were never read.
  • Lightweight PRD triggers: pre-PMF exploration, hackathon projects, internal tools, experiments with <2 week timelines.
  • Full PRD triggers: cross-team initiatives, features with external dependencies, anything touching payments or compliance.

Related Skills

  • roadmap-planning -- Chain after writing PRDs to slot the initiative into the broader roadmap with dependencies and timelines.
  • mvp-scoping -- Chain before writing a PRD to determine what to include in v1 vs. defer to later releases.
  • user-research-synthesis -- Chain before writing a PRD to ground the Background and Market Segments sections in real customer data.

Examples

Example 1: Lightweight PRD prompt

User: "We need a PRD for adding Slack notifications to our project management tool."

Good output excerpt:

Summary: Add configurable Slack notifications so that teams using ProjectFlow are alerted to task updates, mentions, and deadline changes without leaving their primary communication tool. This addresses the #1 feature request from our Q3 customer survey (38% of respondents).

Objective: Enable team leads on paid plans to receive real-time project updates in Slack, resulting in a 20% reduction in average response time to task assignments within 6 weeks of launch.

Market Segment: Teams of 10+ coordinating across tools, whose primary pain is context-switching between project management and communication platforms.

Out of scope: Microsoft Teams integration, custom notification templates, Slack bot commands.

Example 2: Full PRD prompt

User: "Write a full PRD for our new self-serve onboarding flow. We're losing 60% of signups before they complete setup."

Good output excerpt:

Background: Current onboarding requires 7 steps and takes an average of 12 minutes. Hotjar recordings show 45% of users abandon at the "connect data source" step. Competitor X reduced their onboarding to 3 steps in Q2 and reported a 2x improvement in activation. Our support team handles 30+ onboarding tickets per week, costing approximately $4,500/month.

Value Proposition: Eliminate the "connect data source" friction by offering a sample dataset that lets users experience core value before committing to integration. Differentiated from Competitor X which still requires immediate data connection.

Release:

  • Phase 1: Internal dogfood with the team (2 weeks)
  • Phase 2: 10% of new signups via feature flag
  • Phase 3: 50% rollout if activation rate > 45%
  • Phase 4: GA if no P0 bugs and support ticket volume decreases
  • Rollback trigger: activation rate drops below current 40% baseline

Frequently asked questions

What does the Prd Writing AI skill do?

When the user needs to define a product feature, write a product requirements document, or translate an idea into a structured spec.

Why use Prd Writing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shawnpang/startup-founder-skills/tree/main/skills/prd-writing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prd Writing?

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 Writing?

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

Is the Prd Writing AI skill free?

Yes. It is published on GitHub by shawnpang 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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