Context failures — missing examples, unclear rules, or absent validation — are the most common reason AI coding assistants produce unusable code. This template treats context as engineering: a packaged set of docs, examples, global rules, and validation commands so an assistant can take a feature request and produce testable work rather than guessing at intent. (github.com)
What Sets It Apart
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Opinionated project structure that treats examples as first-class inputs. Rather than relying on a single prompt, the template expects curated example files in an examples/ folder so the assistant can imitate and reuse real code patterns — so what: reduces ambiguity and accelerates correct implementations. (github.com)
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PRP (Product Requirements Prompt) workflow: use INITIAL.md to capture a feature request and generate a comprehensive PRP the assistant can execute. So what: turns a high-level request into an actionable, validated implementation plan the model can follow. (github.com)
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Claude Code–centric commands and settings (custom .claude/ commands). So what: includes ready-made slash commands and examples for Claude Code users while remaining adaptable to other LLM assistants. (github.com)
Who it's for + Tradeoffs
Great fit if you: maintain or build AI coding assistants, run Claude Code workflows, or want a reproducible way to convert feature requests into implementable PRs. It helps teams standardize rules (CLAUDE.md), examples, and validation loops so assistants follow project conventions. (github.com)
Look elsewhere if you: need a full GUI client or a standalone production agent framework — this repo is a template and guidance repo (not a packaged runtime or deployed service) and expects you to integrate its structure into your own tooling. Also note it centers examples and commands around Claude Code; adapting to other platforms may require changes. (github.com)
Where It Fits
Use this as the contextual backbone for AI-assisted feature work: pair it with your CI, local runtimes, or agent orchestration so generated PRPs become real pull requests and tests. It sits between prompt notes and a full engineering handoff — the middle layer that makes model outputs actionable.
How It Works (high-level)
The repository provides templates (INITIAL.md), example locations, global rules (CLAUDE.md), and command definitions under .claude/. The expected flow is: author writes a focused INITIAL feature request, run a generate-prp command to expand it into a PRP, then execute and validate the PRP using example-driven tests and rules. This separates intent, examples, and validation so assistants can operate deterministically rather than relying on ad-hoc prompts. (github.com)
