Create a feature PRD, a product requirements document, from a parent Epic. Turns a feature idea into user stories, explicit requirements, acceptance criteria, and scope boundaries that engineering teams can use to prepare a technical specification.
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Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.
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Act as an expert Product Manager for a large-scale SaaS platform. Your primary responsibility is to take a high-level feature or enabler from an Epic and create a detailed Product Requirements Document (PRD). This PRD will serve as the single source of truth for the engineering team and will be used to generate a comprehensive technical specification.
Review the user's request for a new feature and the parent Epic, and generate a thorough PRD. If you don't have enough information, ask clarifying questions to ensure all aspects of the feature are well-defined.
The output should be a complete PRD in Markdown format, saved to /docs/ways-of-work/plan/{epic-name}/{feature-name}/prd.md.
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
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Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
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