prd-builder
Skill0 starsChanged 6 months agoMITView on GitHub
Opinionated PRD generator that forces a TL;DR, hypothesis + counter-hypothesis, explicit Out-of-scope cuts with reasoning, and a mandatory 3-question intake when the brief is a one-liner. Use instead of generic PRD generators when you want a doc that fights back against vague asks - the 3-question intake and counter-hypothesis are non-negotiable here. Triggers on phrases like "draft a PRD with hypothesis and metrics", "PRD that pushes back on vague asks", "structured product spec from fuzzy ask", "PRD for X", "feature spec for X", "turn this into a PRD I can react to", "напиши PRD по фиче X", "сделай спеку с гипотезой".
4 other projects use a version of this.
Adopted by
Most recently taken up by
- SayandipBagchi/prd-buildertheir own version, first seen Sep 14, 2026
- BitCrowder/ai-product-dev-skillstheir own version, first seen Jul 26, 2026
- timdevai/proteustheir own version, first seen Jun 10, 2026
- ZeroPointRepo/CRHQ-PRDstheir own version, first seen May 10, 2026
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- ZeroPointRepo/CRHQ-PRDs★ 22same name
- BitCrowder/ai-product-dev-skills★ 1same name
- timdevai/proteus★ 1same name
- SayandipBagchi/prd-builder★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- ai-eval-scorerSkill
- deep-market-researchSkill
- feedback-triageSkill
- prd-criticSkill
- product-trio-sessionSkill
- synthetic-user-interviewSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 4 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
4
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
- Very specific: line 25
When the problem is unclear and needs discovery first (run deep-market-research or synthetic-user-interview first)
- Distinctive: line 39
If only a one-liner is given, ask three sharp questions before writing:
- Distinctive: line 44
Do not write the PRD until these have at least rough answers.
- Distinctive: line 73
If "Out" is empty, the scope is undefined.
- Distinctive: line 120
Missing open questions: every PRD has assumptions.
- Very specific: line 125
Run prd-critic on the output before sharing with eng or exec.
“Common” is measured: kept by at least 6 in 10 of the 3 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.
Files
PRD Builder
Purpose
Most PRDs fail in the first 30 seconds because the reader can't tell what problem they solve, for whom, and how success will be measured. This skill produces a PRD where those answers are front-loaded and every section pulls weight.
This is for product PRDs (consumer or B2B SaaS framing). For enterprise B2B delivery specs (use cases, FRD, traceability), use a BA-style spec writer instead.
When to use
- Feature ask from leadership ("we should do X") with no underlying spec
- Fuzzy problem from a stakeholder that needs structure before discussion
- Drafting a PRD to circulate for reactions, not a final delivery contract
- Turning your own thinking into a doc others can poke at
When NOT to use
- For detailed engineering specs (architecture, API contracts)
- For one-line tickets in a sprint
- When the problem is unclear and needs discovery first (run
deep-market-researchorsynthetic-user-interviewfirst)
Inputs
Required:
- Problem statement, feature ask, or short description
Optional but strongly improves output:
- Target user / persona
- Current workflow being replaced or augmented
- Business context (strategy, OKRs this serves)
- Known constraints (tech, legal, timeline)
- Adjacent features or systems
If only a one-liner is given, ask three sharp questions before writing:
- Who hurts today and how do they currently cope?
- What outcome would tell us this worked?
- What's the hardest tradeoff you already see?
Do not write the PRD until these have at least rough answers.
Process
Phase 1: Frame the problem
- One-paragraph problem statement in plain language
- Current state: what the user does today, with friction
- Why now: what changed in market, tech, or business to make this worth doing now
Phase 2: Hypothesis
- "We believe that [change] will result in [outcome] for [user/business]"
- Why this hypothesis is non-obvious (if it's obvious, the doc isn't worth writing)
- Counter-hypothesis: what would we expect to see if we're wrong
Phase 3: Solution sketch
- What it is, in 3-5 bullets max
- What it is NOT (boundary clarity)
- Optional: 2-3 alternatives considered and why rejected
Phase 4: Scope
Three explicit lists:
- Must: in scope for v1
- Nice: nice to have, cut if needed
- Out: explicitly not in scope, with one-line reasoning
If "Out" is empty, the scope is undefined.
Phase 5: Success metrics
- North metric: the one number that proves this worked
- Input metrics: what we'd see moving first if it's working
- Guardrail metrics: what should NOT get worse (regressions to watch)
- Each metric with a directional target ("up", "down 20%", "above baseline")
Vague metrics ("improve engagement") are not allowed.
Phase 6: Open questions and assumptions
- 3-7 open questions where the PRD is making assumptions
- For each: what we're assuming, why we're assuming it, what would change if wrong
Phase 7: Risks and tradeoffs
- Top 3 things that could kill this
- Top 3 second-order effects (what changes in adjacent systems)
- Explicit tradeoff: what are we choosing not to optimize
Output
# PRD: [Feature name]
**Author**: [user] | **Status**: Draft | **Date**: [today]
## TL;DR (3 sentences max)
## Problem
## Hypothesis
## Solution sketch
## Scope: Must / Nice / Out
## Success metrics
## Open questions
## Risks and tradeoffs
## Appendix: discarded alternativesThe TL;DR is non-optional and must work as a standalone summary. If a reader stops after TL;DR, they should still know what's being proposed and why.
Common failure modes to avoid
- Solution before problem: writing the spec before the problem is sharp
- Vague metrics: "improve UX", "drive growth" - useless without numbers
- Empty "Out" section: scope without explicit cuts isn't scope
- Missing open questions: every PRD has assumptions. Hiding them doesn't make them less risky.
- No tradeoff: a PRD that claims pure upside is lying to itself
Recommended companion
Run prd-critic on the output before sharing with eng or exec. The pair catches what either alone misses.
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
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.
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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.

