Deliver Prd logo

Deliver Prd

Organization
product-on-purpose
deliver-prd

Creates a comprehensive Product Requirements Document that aligns stakeholders on what to build, why, and how success will be measured. Use when specifying features, epics, or product initiatives for engineering handoff.

Overview

Publisherproduct-on-purpose
Repositorypm-skills
Skill namedeliver-prd
Stars
717
Forks
92
Bundled files
4
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.

  • 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 product-on-purpose on GitHub. Read the source before you install it.

Installation

Install the Deliver Prd 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/product-on-purpose/pm-skills/tree/main/skills/deliver-prd
  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/product-on-purpose/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/skills/deliver-prd .claude/skills/deliver-prd
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deliver Prd 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 Deliver Prd 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 Deliver Prd 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.

Product Requirements Document (PRD)

A Product Requirements Document is the primary specification artifact that communicates what to build and why. It bridges the gap between problem understanding and engineering implementation by providing clear requirements, success criteria, and scope boundaries. A good PRD enables engineering to build the right thing while maintaining flexibility on implementation details.

When to Use

  • After problem and solution alignment, before engineering work begins
  • When specifying features, epics, or product initiatives for handoff
  • When multiple teams need to coordinate on a shared deliverable
  • When stakeholders need to approve scope before investment
  • As reference documentation during development and QA

When NOT to Use

  • The problem is still unframed or contested -> use define-problem-statement first; a PRD assumes an agreed problem
  • You need a one-page pitch to align stakeholders on an approach -> use develop-solution-brief; the PRD comes after that alignment
  • You only need the work broken into tickets for a sprint -> use deliver-user-stories
  • You are recording a technical or architectural decision -> use develop-adr

Instructions

When asked to create a PRD, follow these steps:

  1. Summarize the Problem Start with a brief recap of the problem being solved. Link to the problem statement if available. Ensure readers understand why this work matters before diving into what to build.

  2. Define Goals and Success Metrics Articulate what success looks like. Include specific, measurable metrics with baselines and targets. These metrics should connect directly to the problem being solved.

  3. Outline the Solution Describe the proposed solution at a high level. Focus on user-facing functionality and key capabilities. Include enough detail for stakeholders to evaluate the approach without over-specifying implementation.

  4. Detail Functional Requirements Break down what the system must do. Use user stories or requirement statements. Each requirement should be testable - someone should be able to verify if it's met.

  5. Link AI Behavior to Evidence (only when the feature's output comes from a model) Skip this step entirely for deterministic features. When a model produces the output, the behavior varies run to run, so "it works" is a claim that needs evidence attached. Name the behaviors that are requirements of this release and, for each one, how it will be shown to hold and at what threshold. Refusal and abstention get their own rows: a model has no dependable default for either, so what the feature does when it should not answer is a requirement, not an implementation detail. Score each component of a multi-step feature separately, because an end-to-end pass rate hides which step failed. State how the case set was sized as a method, never as a borrowed number.

  6. Define Scope Boundaries Explicitly state what's in scope, out of scope, and deferred to future iterations. Clear scope prevents scope creep and sets realistic expectations.

  7. Address Technical Considerations Note any technical constraints, architectural decisions, or integration requirements. Don't design the system, but surface considerations engineering needs to know.

  8. Write the Agent Execution Contract (only when an executor will implement this without the authoring context) Skip this step entirely when the implementers were in the room. A coding agent, an outside contractor, or a team picking this up cold cannot infer what you already know, so declare it: which sources are authoritative and which wins when two disagree, what must not be touched and why, how each FR-n is verified and by whom, and the conditions where the executor must stop and escalate rather than decide. Name an owner for every escalation; an escalation with no addressee becomes a guess. A requirement with no verification is not ready to hand off, because nothing distinguishes "done" from "the executor believes it is done".

  9. Identify Dependencies and Risks List external dependencies, assumptions, and risks that could impact delivery. Include mitigation strategies where applicable.

  10. Propose Timeline and Milestones Outline key phases and checkpoints. This helps stakeholders understand the delivery plan without committing to specific dates prematurely.

Project Memory Contract

Active only when .claude/pm-skills.local.md exists. With no file, ignore this section entirely and behave exactly as described above.

  • Reads: active_initiative, and any interpretation artifacts recording personas or research findings. Use them instead of asking for context that has already been produced. If none exist, ask as normal and never invent a persona to fill the gap.
  • Writes: the PRD as a decision artifact, and its committed scope boundaries and success metrics to the ## Decisions section.
  • Posture: propose the entry and wait for confirmation before writing, unless memory_auto_append: true is set, in which case append and echo what was written.
  • Write discipline: re-read the file immediately before writing, never from the copy that produced the proposal. If it changed in between, merge your entry into the current state and re-propose rather than overwriting; add only your own entry and leave every other field and section byte-identical. Nothing enforces this at runtime and the file is gitignored, so a careless whole-file write loses another session's work with no way to recover it.

This is the reader half of the demonstration loop: when discover-interview-synthesis has already recorded personas, do not ask the user to supply them again.

Output Format

Use the template in references/TEMPLATE.md to structure the output. A complete PRD fills every template section: Overview; Goals & Success Metrics; User Stories; Scope; Solution Design; Technical Considerations; Dependencies & Risks; Timeline & Milestones; Open Questions; the Appendix when supporting material exists; AI Behavior and Evaluation when the feature's output comes from a model; and the Agent Execution Contract when an executor will implement it without the authoring context.

The last three are conditional by design. A PRD that omits a section its feature does not need is complete; a PRD that includes an empty one is not.

Quality Checklist

Before finalizing, verify:

  • Problem and "why now" are clearly articulated
  • Success metrics are specific and measurable
  • Scope boundaries are explicit (in/out/future)
  • Requirements are testable and unambiguous
  • Technical considerations are surfaced without over-specifying
  • Dependencies and risks are documented with owners
  • Document is readable in under 15 minutes
  • If the output comes from a model: every behavior requirement has an evaluation and a threshold, and refusal and abstention are among them
  • If an evaluation set is sized: each slice's floor says what coverage it commits to and why that slice earns it, and no slice result is reported as a failure rate or as a bound on one
  • If an executor will implement this without the authoring context: every FR-n has a verification and a verifier, and every escalation condition names an owner

Examples

See references/EXAMPLE.md for a completed example.

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 Deliver Prd AI skill do?

Creates a comprehensive Product Requirements Document that aligns stakeholders on what to build, why, and how success will be measured. Use when specifying features, epics, or product initiatives for engineering handoff.

Why use Deliver Prd on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/product-on-purpose/pm-skills/tree/main/skills/deliver-prd. 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 Deliver Prd?

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 Deliver Prd?

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

Is the Deliver Prd AI skill free?

Yes. It is published on GitHub by product-on-purpose 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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