# Ai Feature Prd > 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 budge… - **Type:** Skill - **Install:** `agentstack add skill-mohitagw15856-pm-claude-skills-ai-feature-prd` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [mohitagw15856](https://agentstack.voostack.com/s/mohitagw15856) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [mohitagw15856](https://github.com/mohitagw15856) - **Source:** https://github.com/mohitagw15856/pm-claude-skills/tree/main/plugins/pm-ai/skills/ai-feature-prd - **Website:** https://mohitagw15856.github.io/pm-claude-skills/ ## Install ```sh agentstack add skill-mohitagw15856-pm-claude-skills-ai-feature-prd ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # AI Feature PRD Skill AI features break the normal PRD because the system is probabilistic: it will be wrong sometimes, and the product must be designed around that, not in denial of it. This skill extends a standard PRD with the AI-specific sections that decide whether the feature is trustworthy — the UX of uncertainty, the eval bar, guardrails, and what happens when the model is wrong. ## Required Inputs Ask for these only if they aren't already provided: - **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? ## Reads from / Writes to the Brain If a [`professional-brain`](../professional-brain/SKILL.md) exists, read `context.md` (product, users, voice) and `knowledge/strategy.md` first; write the feature to `entities/` and any scoping decision to `decisions/`, each provenance-tagged. ## Output Format ### AI Feature PRD: [feature] **1. Problem & why AI** — the user problem, and why a model (not rules) is the right tool. If rules would do, say so. **2. Experience** — the core flow, and crucially the **UX of uncertainty**: how confidence is shown, how the user verifies/edits, and how errors are made cheap to recover from. AI features live or die here. **3. Model approach** — prompt / fine-tune / RAG / agent (link [`rag-design-doc`](../rag-design-doc/SKILL.md) or [`agent-spec`](../agent-spec/SKILL.md)), the model tier, and why. **4. Quality bar & evaluation** — the metrics and the explicit ship threshold; reference an [`ai-eval-plan`](../ai-eval-plan/SKILL.md). State the acceptable error rate given the stakes. **5. Guardrails & safety** — what the feature must never do, input/output filtering, and handling of harmful/PII/out-of-scope inputs. **6. Fallback behaviour** — what happens when the model is unsure, wrong, slow, or down: graceful degradation, "I'm not sure" states, human handoff. **No silent confident errors.** **7. Data flywheel** — how usage (and the 👍/👎 / edits) feed back into evaluation and improvement, with the privacy boundary. **8. Cost & latency** — the per-request budget and p95 target; reference an [`llm-cost-latency-budget`](../llm-cost-latency-budget/SKILL.md). **9. Rollout** — staged exposure (internal → %→ GA), the guardrail metrics watched, and the rollback trigger. ## 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 - [ ] A data flywheel is defined with its privacy boundary - [ ] Cost and p95 latency budgets are stated, not left to "we'll see" ## Anti-Patterns - [ ] 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 ## Based On Standard PRD practice (see [`prd-template`](../prd-template/SKILL.md)) extended for probabilistic systems — uncertainty UX, eval gates, guardrails, and graceful fallback. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [mohitagw15856](https://github.com/mohitagw15856) - **Source:** [mohitagw15856/pm-claude-skills](https://github.com/mohitagw15856/pm-claude-skills) - **License:** MIT - **Homepage:** https://mohitagw15856.github.io/pm-claude-skills/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-mohitagw15856-pm-claude-skills-ai-feature-prd - Seller: https://agentstack.voostack.com/s/mohitagw15856 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.