skills/prd-generator/SKILL.md
Produces production-ready Product Requirements Documents (PRDs) for software systems and AI-powered features, with clear problem framing, measurable outcomes, scoped functionality, testable requirements, and explicit risks. Use when the user wants to write a PRD, define requirements, plan a feature, or turn a vague product idea into an implementation-ready specification.
npx skillsauth add jaktestowac/awesome-copilot-for-testers prd-generatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill produces high-quality, professional PRDs that serve as a single source of truth for product, design, engineering, QA, and leadership teams.
The PRD balances business goals, user needs, and technical execution, and supports both traditional software systems and AI-driven products.
Adapted from the prd skill in github/awesome-copilot (MIT).
When invoked, this skill:
Use this skill when the user wants to:
A PRD must never be generated immediately from a single prompt. Reduce uncertainty and align expectations first.
Before discovery, classify the PRD to adapt structure and rigor:
The chosen strategy determines depth, level of detail, and validation rigor.
Ask clarifying questions before drafting, using a structured approach (Who / What / Why / When / How):
Ask targeted questions only where the answer would change the PRD. When answers are unavailable or the user wants to move fast, record the gaps as explicit entries in the Assumptions section and proceed.
Produce the PRD following the mandatory output schema in ./resources/prd-template.md - exact section structure, order, and the worked example live there.
Before finalizing, verify:
Avoid subjective language.
Bad: "Fast", "Easy to use", "High quality"
Good: "P95 latency ≤ 200ms for 10k records", "100% Lighthouse accessibility score", "≥90% precision on benchmark queries"
Every major requirement must indicate:
AI systems must define offline evaluation and runtime monitoring.
./resources/prd-template.md - the mandatory PRD output schema, section-by-section guidance, and a worked examplerequirements-test-coverage-mapper - when the finished PRD should be mapped to test coverageverifying-acceptance-criteria - when acceptance criteria from the PRD need verification against a builddesigning-functional-tests - when the PRD should expand into concrete test scenariosThis skill is complete when:
testing
Tests the customization assets themselves - skills, prompts, custom agents, instructions - the way a product is tested: activation cases that check an asset fires when it should and stays quiet when it should not, output-contract cases, safety cases, collision cases between assets competing for the same trigger, a weighted rubric scored blind, and a baseline-versus-candidate gate before an edit ships. Use when a skill is edited and nobody knows whether behaviour changed, when two skills fight over the same request, when a description is being tuned for discoverability, when a collection has grown past manual spot-checking, or when the request mentions skill evals, prompt regression, or "does this skill actually work".
development
Shapes QA output for the person who has to act on it: result and blocker in the first two lines, one decision per report, findings ordered by what they cost, the long artifact in a file and the decisions in the message, and magnitude stated in units the reader can count. Use when a report is accurate but nobody acts on it, when a finding set is too long to read under time pressure, when the same findings must be retold for a developer, a release manager, and an on-call engineer, or when the request mentions "too long", "make this readable", "just tell me what to do", "so what", or "summarize this for stakeholders". Pairs with unslop-answers, which makes the same report honest.
testing
Verifies that the lines and branches a change actually touched are executed by tests, using LCOV or Cobertura diff coverage instead of whole-repo percentages, and escalates uncovered high-risk changes into a blocking finding. Use when a pull request needs a coverage gate that unrelated tests cannot satisfy, when total coverage looks healthy but the diff is untested, when wiring diff coverage into CI, or when someone claims a change is covered because the suite is green.
development
Cuts AI tells from test code: tests that pass without proving anything, tautological assertions, mock-only tests, hardcoded waits, coverage theater, vague names, swallowed errors, retries used as fixes. Use whenever test code is written, changed, or reviewed, including tests produced as a side effect of a feature task, and when the request mentions "review these tests", "are these tests any good", "this test always passes", "this suite is flaky", or "clean up these tests". Must always apply to test code.