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A template and workflow for feeding AI coding assistants structured context — project rules, code examples, and validation gates — instead of one-off prompts. Centers on Product Requirements Prompts (PRPs) that an agent generates, then executes.

Introduction

Most failures with AI coding agents aren't the model being dumb — they're the model being starved. Hand it a clever one-line prompt and it guesses; hand it the same structured context a new human hire would get and it ships. This repo is the smallest concrete demonstration of that bet: a folder of conventions plus two slash commands, nothing you install.

What Sets It Apart
  • It splits planning from doing. /generate-prp reads your INITIAL.md and the codebase to write a Product Requirements Prompt — a full implementation blueprint — and only then does /execute-prp build against it. So the expensive reasoning happens once, reviewably, before a line of code exists.
  • PRPs bundle documentation links, known gotchas, and explicit validation gates the agent must pass, so it self-corrects in a loop instead of handing you code that silently breaks.
  • The examples/ folder is a first-class input: the agent mimics your existing patterns rather than inventing its own conventions.
  • It is deliberately framework-free — plain Markdown files (CLAUDE.md, INITIAL.md, PRPs/) any Claude Code user can copy in an afternoon.
Great Fit / Look Elsewhere

Great fit if you already use Claude Code and keep re-explaining the same conventions every session, or you want repeatable multi-step feature builds rather than chat-by-chat coding. Look elsewhere if you want a runtime library, an autonomous agent that manages its own memory, or support beyond Claude Code — this is a methodology and a starter template, not a tool you run.

Information

  • Websitegithub.com
  • AuthorsCole Medin
  • Published date2025/07/02

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