agentleFS
RandyRawgustRandyRawgust/ai-knowledge-base-template/.claude/skills/to-prd/SKILL.md

to-prd

Skill1 starsChanged 6 months agoMITView on GitHub

Turn the current conversation context into a PRD (problem statement, user stories, implementation decisions, testing decisions). Use when Andy wants to crystallize what we've been discussing into a written PRD - no interview, just synthesis.

132 other projects use a version of this, 94 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use decision-mapping, request-refactor-plan and 7 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • FabienDehopreSkill

    decision-mapping

    Turn a loose idea into a sequenced map of investigation tickets, then drive them to resolution one at a time.

    Also in 8 projects that use this

  • simo97Skill

    request-refactor-plan

    Use this skill when user wants to create a refactor plan.

    Also in 23 projects that use this

  • simo97Skill

    edit-article

    The user will invoke this skill to help them edit an article.

    Also in 23 projects that use this

  • kennethkeimSkill

    ubiquitous-language

    Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

    Also in 23 projects that use this

  • AnasX7Skill

    scaffold-exercises

    Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

    Also in 22 projects that use this

  • +4 more from simo97

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 127 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check with Andy that these modules match expectations.

    line 20
  2. Distinctive:

    Write the PRD using the template below.

    line 22
  3. Very specific:

    If the project has GitHub Issues set up and gh CLI is available: submit as a GitHub issue, return the URL.

    line 25
  4. Very specific:

    Always also log a row in 03 - Skills & Rules/Agents/Activity Log.md so Andy can find it later.

    line 27

“Common” is measured: kept by at least 6 in 10 of the 127 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

To PRD

This skill takes the current conversation context and codebase understanding and produces a PRD. Do NOT interview Andy — just synthesize what you already know from the conversation.

When to use

Triggers: write a PRD, turn this into a PRD, crystallize this, make this into a doc, synthesize what we've discussed.

Process

  1. Explore the repo to confirm current state if you haven't already.
  2. Sketch the major modules you'd need to build or modify. Actively look for opportunities to extract deep modules that can be tested in isolation.

A deep module is one which encapsulates a lot of functionality behind a small, testable interface that rarely changes (vs. a shallow module where the interface is nearly as complex as the implementation).

Check with Andy that these modules match expectations. Ask which modules he wants tests written for.

  1. Write the PRD using the template below.

Output destination

  • If the project has GitHub Issues set up and gh CLI is available: submit as a GitHub issue, return the URL.
  • Otherwise: save to the project at docs/prd/<MM-DD-YY>-<slug>.md (create docs/prd/ if missing).
  • Always also log a row in 03 - Skills & Rules/Agents/Activity Log.md so Andy can find it later.

Template

# <Title>

## Problem Statement

The problem the user is facing, from the user's perspective.

## Solution

The solution to the problem, from the user's perspective.

## User Stories

A long, numbered list. Each story: "As a <persona>, I want <capability>, so that <outcome>."

Example: "As a mobile bank customer, I want to see balances on my accounts, so that I can make better informed decisions about my spending."

Cover all aspects of the feature.

## Implementation Decisions

- Modules to be built / modified
- Interfaces of those modules
- Technical clarifications from the developer
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions

Do NOT include file paths or code snippets — they go stale fast.

## Testing Decisions

- What makes a good test here (external behavior only, not implementation details)
- Which modules will be tested
- Prior art in the codebase (similar tests we'll mirror)

## Out of Scope

What is explicitly NOT being addressed.

## Further Notes

Anything else worth capturing.

Source

Imported from mattpocock/skills — 05-21-26. Adapted: destination defaults to docs/prd/ filesystem path when GitHub isn't configured, and always logs to Andy's Activity Log.

Integrates with

  • 03 - Skills & Rules/Skills/to-issues.md — break the PRD into vertical-slice tickets
  • 03 - Skills & Rules/Rules/playbook-request.md — playbook for new work that consumes this PRD
  • 03 - Skills & Rules/Rules/engineering-doctrine.md — research-first, no speculative features

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.