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PRPs agentic eng

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Basic Information

This repository is a practical library of assets, templates and runbooks for agentic AI engineering optimized for Claude Code. It codifies the Product Requirement Prompt (PRP) methodology which combines a PRD-like goal and justification with in-prompt codebase context, explicit implementation strategy and deterministic validation gates so an AI coding agent can deliver a vertical slice of working software on the first pass. The repo includes preconfigured Claude slash commands, PRP templates, example PRPs, an ai_docs folder for piped documentation, a PRP runner script and recommended project structure and CLAUDE.md guidance. It targets engineering teams and developers who want reproducible, production-aware AI workflows and provides concrete steps to copy resources into an existing project or to clone and start a new project. The emphasis is on shifting quality left with executable tests and clear context to reduce brittle or incomplete AI output.

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App URL
https://github.com/Wirasm/PRPs-agentic-eng
Github Repository
https://github.com/Wirasm/PRPs-agentic-eng/blob/main/README.md

App Details

Features
The repo ships twelve preconfigured Claude Code slash commands for creating and executing PRPs, planning, advanced specs, reviews, refactors, pull request creation, onboarding and debugging. It contains PRP templates including a comprehensive prp_base.md, spec and planning templates, and example PRPs from workshops. A PRPs/scripts/prp_runner.py runner supports interactive development, headless CI execution and streaming JSON output for monitoring. The ai_docs directory centralizes library and environment documentation to be piped into prompts. The README includes project structure recommendations, a CLAUDE.md template for project conventions, CI/CD integration examples and guidance for running parallel Claude sessions. Validation gates and best practices such as ruff, mypy and pytest checks are described to automate quality control.
Use Cases
This repository helps teams produce higher quality AI-generated code by supplying structured, information-dense prompts plus the contextual artifacts the model needs to act correctly. By combining context, implementation blueprints and validation loops, it reduces trial-and-error cycles and increases the chance of a one-pass implementation. The included commands and runner let engineers execute PRPs locally or in CI, stream results for monitoring, and automate test and lint gates to catch defects early. Templates and ai_docs make it easier to standardize onboarding and project conventions, while examples and best practices guide designers of PRPs on which details and gotchas to include. Overall it operationalizes prompt engineering into repeatable developer workflows oriented toward production-ready outcomes.

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