What do I need to use AI Feature PRD?

The user problem; What "good" looks like; Inputs available; Trust level needed.

Is AI Feature PRD free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

AI Feature PRD

Q: How do I know the AI Feature PRD output is good?

The skill verifies itself against its own quality checks: The PRD designs for the model being wrong — there's an explicit fallback, not just the happy path; The UX shows uncertainty and lets the user verify/correct cheaply; There's an explicit quality bar tied to the stakes (a medical answer and a tweet draft are not the same bar); Guardrails name what the feature must never do.

🔵 Stable🕐 updated 2026-10-06 🔷 SkillSpec L3 pm-ai

Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model approach, eval criteria, guardrails, fallback behaviour, the data flywheel, and cost/latency budget.

▶ Run it free — no key needed 📝 Grade your existing draft 📱 Use as an app View SKILL.md ↗ What changed

🗣 Say this to your agent

“Spec an AI/LLM feature.”“Write a PRD for a feature that uses a model.”“Plan an AI capability.”

What to give it

▸The user problem — and why an AI/probabilistic approach fits it (vs. deterministic rules).

▸What "good" looks like — to the user, and the cost of a wrong answer (low-stakes vs. high-stakes).

▸Inputs available — context/data the model can use; privacy constraints.

▸Trust level needed — can the user verify the output, or must it be near-perfect?

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

✓The PRD designs for the model being wrong — there's an explicit fallback, not just the happy path

✓The UX shows uncertainty and lets the user verify/correct cheaply

✓There's an explicit quality bar tied to the stakes (a medical answer and a tweet draft are not the same bar)

✓Guardrails name what the feature must never do

✓A data flywheel is defined with its privacy boundary

✓Cost and p95 latency budgets are stated, not left to "we'll see"

⚠️ What it refuses to do

Do not design only the happy path — a probabilistic feature without a fallback is a feature that fails loudly in production

Do not hide uncertainty behind a confident UI — overclaimed confidence is how AI features lose user trust permanently

Do not use AI where deterministic rules are better, cheaper, and more reliable — "AI" is not the goal

Do not set one quality bar for all stakes — calibrate the acceptable error rate to the cost of being wrong

Do not ship without a rollback trigger and guardrail metrics — a probabilistic system needs a kill switch

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y -p pm-claude-skills pm-claude-skills-mcp

Related skills

🔌 Embed this skill

Drop this on your blog, docs, or site — it renders a "Run this skill" card:

<div data-pm-skill="ai-feature-prd"></div>
<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>

💬 Discussion

AI Feature PRD is one of 1307 open-source professional AI agent skills — all SkillSpec L3. Try them all in the browser · ⭐ Star on GitHub · Browse the full catalog