A reusable Codex playbook for structured AI agent workflows, architecture documentation, and project-specific coding guidance.
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Updated
Jun 5, 2026 - Python
A reusable Codex playbook for structured AI agent workflows, architecture documentation, and project-specific coding guidance.
A step-by-step lifecycle for building software with AI. Focused on context management and high-velocity engineering. Heartbeat → Think, Build, Review, Ship, Learn.
Practical playbook for AI-assisted engineering: rules, role separation, and guardrails for teams using Cursor/Copilot in production codebases (CC BY 4.0).
Reusable agent rules, templates, subagents, and skills for AI-assisted software delivery.
A collection of production-ready AI engineering skills and playbooks.
AI Product Engineering Skills Platform (APES) is an open library of Engineering Playbooks for AI agents. Each Skill is a single skill.md file that defines the methodology for completing one specific engineering task.
에이전트 기반 제품 개발 플레이북 — gridi가 코딩 에이전트를 실무 SDLC에 투입하는 방식·컨벤션·원칙 (민감정보 제외) · An AI-native engineering playbook
Practical engineering decisions, patterns, trade-offs, and case studies from building real products.
Vendor-neutral AI engineering playbook for architecture decisions, evaluation, release gates, security, and operations.
Frontend team engineering playbook — code standards, review guidelines, architecture decisions, onboarding
A publishable template repository for AI-assisted fullstack development workflows across Claude Code, Cursor, and Codex.
Personal software engineering playbook with reusable standards, templates, workflows, and best practices.
System Design Playbook 🎯 is a curated collection of real-world system design case studies inspired by the most frequently asked architecture questions in top tech company interviews such as Google, Amazon, Meta, Microsoft, and other FAANG companies.
A production-grade iOS Engineering Playbook covering architecture decisions, governance models, release strategy, technical debt management, and staff-level engineering practices.
A practical playbook for Docker and Kubernetes with production-ready backend examples using Python, FastAPI, PostgreSQL, and Redis.
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