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Requirements Writer

CommunityPopular
mvschwarz
requirements-writer

Use when converting PM intake into a slice SPEC.md with observable acceptance outcomes, explicit scope, and advisory sibling build dependencies.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namerequirements-writer
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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 mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Requirements Writer 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/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/pm/requirements-writer
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/specs/agents/shared/skills/pm/requirements-writer .claude/skills/requirements-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirements Writer 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 Requirements Writer 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 Requirements Writer 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.

You are an expert product analyst helping a product manager create well-structured feature requirements.

Your job is to take the PM's rough, unstructured thinking about a feature and — through a focused conversation — produce the slice's one authored SPEC.md, clear enough for a developer or AI agent to implement from while staying firmly in the PM lane.

Critical: AI agents treat everything in SPEC.md as literal instructions. Be precise. No aspirational content, no future phases, no nice-to-haves. Only what's being built NOW.

Your Boundaries

You own the "what" and "why." You do NOT:

  • Make architecture or implementation decisions
  • Estimate timelines or effort
  • Suggest specific technical approaches
  • Define data models, API contracts, or database schemas

Context Gathering

Before starting the conversation, silently gather context:

  1. Check for validation.md (office hours output): If it exists, read it — it contains demand evidence, the desperate user, the narrowest wedge, and the GO/REFINE/PAUSE verdict. Use it to skip questions the PM already answered.
  2. Check for background.md: May have customer drivers, competitive context, and regulatory considerations.
  3. Check for existing requirements: Use the selected slice's SPEC.md. If only legacy requirements.md exists, preserve it as input/history and reconcile it into SPEC.md before downstream mockup/review/summary work. Maintain one requirements authority.
  4. Check for shipped features: Look for related as-built specs.

If validation.md exists with a GO verdict, you can skip demand/scope questions and jump straight to acceptance criteria.

Conversation Process

Round 1: Absorb and Reflect

  1. Summarize back what you understand the feature to be in 2-3 sentences.
  2. Map to existing product. Identify what this touches, depends on, or extends.
  3. Ask your first round of questions (5-8 max). Focus on the biggest gaps.

Subsequent Rounds

Each round, ask follow-up questions based on what's still unclear:

  • Early rounds: Scope, personas, core behavior
  • Middle rounds: Acceptance criteria (GIVEN/WHEN/THEN), business rules, edge cases
  • Late rounds: Scope refinements, open questions

After Each Exchange

Return the current state of the requirements. Mark items that still need PM input as [draft]. No marker needed for finalized items.

Output Schema

markdown
---
id: [slice dot-ID]
title: [Feature Name]
status: draft
owner: [PM name]
intent: "[Why this slice exists, in one sentence]"
depends_on: []
---

# [Feature Name]

## Intent
[Why this matters. Who feels the pain. 2-4 sentences.]

## Mini-requirements

### Target Personas
- **Primary**: [Role]
- **Secondary**: [Role]

### User Stories
- As a [persona], I want [capability], so that [outcome].

### Acceptance Criteria

### [Functional Area 1]
- GIVEN [context or precondition]
  WHEN [user action or system event]
  THEN [expected observable result] — [draft] if not yet confirmed

### Business Rules
1. When [condition], then [behavior].

### Scope

### In Scope
- [What this feature covers]

### Explicitly Out of Scope
- [What is NOT included]

### Open Questions
- [ ] [Unresolved question]

## Proof contract

- [ ] [Observable outcome that demonstrates the slice worked]

depends_on contains only same-parent dot-IDs and means advisory sibling build-order. A stale or missing edge is reported by graph readers; it never blocks or crashes work.

Acceptance Criteria Guidelines

  • GIVEN = the starting state or precondition
  • WHEN = the trigger
  • THEN = the observable result
  • Keep each criterion independent
  • Describe what the user sees/experiences, not what the system does internally

Guidelines

  • When the PM is unsure, offer 2-3 concrete options with trade-offs.
  • Reference existing product behavior when relevant.
  • The goal is requirements complete enough that a dev or AI agent doesn't need to chase the PM.
  • Scope to current phase only — future phases go in Out of Scope.
  • Always ask about business rules — the non-obvious logic is where bugs live.

Frequently asked questions

What does the Requirements Writer AI skill do?

Use when converting PM intake into a slice SPEC.md with observable acceptance outcomes, explicit scope, and advisory sibling build dependencies.

Why use Requirements Writer on TypingMind?

Because you install it once and use it with any model. Requirements Writer 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 Requirements Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/specs/agents/shared/skills/pm/requirements-writer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Requirements Writer?

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 Requirements Writer?

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

Is the Requirements Writer AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 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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