1000-create-prd

A rule for creating a Product Requirements Document (PRD), a written description of what a product feature should do and why. It requires clarifying questions before producing and saving the document.

In plain words
What is it for?
Use it to gather requirements, write a detailed Markdown PRD, and save it in the project's tasks directory.
Why use it?
It turns a brief request into clearer requirements that a developer can understand and implement. Asking questions first helps expose missing goals, scope, and constraints.

Cursor rule

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/jabrena/cursor-rules-tasks/1000-create-prd
Clone the repo
git clone --depth 1 https://github.com/jabrena/cursor-rules-tasks
Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 886 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00009 $0.00886
Opus 5 $0.00005 $0.00443
Sonnet 5 $0.00002 $0.00177
Haiku 4.5 $0.00001 $0.00089

Measured 2d ago against content hash efd084997d5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

1000-create-prd scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

92% identical to create_prd — 15 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

1000-create-prd.mdc · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Rule: Generating a Product Requirements Document (PRD)

Goal

To guide an AI assistant in creating a detailed Product Requirements Document (PRD) in Markdown format, based on an initial user prompt. The PRD should be clear, actionable, and suitable for a junior developer to understand and implement the feature.

Process

  1. Receive Initial Prompt: The user provides a brief description or request for a new feature or functionality.
  2. Ask Clarifying Questions: Before writing the PRD, the AI must ask clarifying questions to gather sufficient detail. The goal is to understand the "what" and "why" of the feature, not necessarily the "how" (which the developer will figure out).
  3. Generate PRD: Based on the initial prompt and the user's answers to the clarifying questions, generate a PRD using the structure outlined below.
  4. Save PRD: Save the generated document as [feature-name]-PRD.md inside the /tasks directory.

Clarifying Questions (Examples)

The AI should adapt its questions based on the prompt, but here are some common areas to explore:

  • Problem/Goal: "What problem does this feature solve for the user?" or "What is the main goal we want to achieve with this feature?"
  • Target User: "Who is the primary user of this feature?"
  • Core Functionality: "Can you describe the key actions a user should be able to perform with this feature?"
  • User Stories: "Could you provide a few user stories? (e.g., As a [type of user], I want to [perform an action] so that [benefit].)"
  • Acceptance Criteria: "How will we know when this feature is successfully implemented? What are the key success criteria?"
  • Scope/Boundaries: "Are there any specific things this feature should not do (non-goals)?"
  • Data Requirements: "What kind of data does this feature need to display or manipulate?"
  • Edge Cases: "Are there any potential edge cases or error conditions we should consider?"

PRD Structure

The generated PRD should include the following sections:

Read the full file on GitHub · 61 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 61 lines · 9 tokens per session scan A efd084997d5d

Subscribe to this mod's changes

1000-create-prd is a cursor rule published in the GitHub repository jabrena/cursor-rules-tasks (1 stars, last pushed 1y ago), licensed Apache-2.0. It adds 9 tokens to every session and 886 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to create_prd, differing in 15 lines, and is treated as a copy.