Anthropic의 AI-Native SDLC Playbook의 기성적인 구현: 당신의 코딩 대리인에게 목표 또는 아이디어를 주고, 그것 비계는 풀 라이프사이클을 통해 프로젝트를 실행합니다 - 계획, 디자인, 구조, 시험, 배치 및 유지 - 모든 handoff에 인간적인 승인 문으로.

이 재포는 한 번에 세 가지입니다:

• skills/ai-native-sdlc/]에서 ** Codex 기술**를 설치, ~/.codex/skills에 설치.
• A Claude Code Skill - 같은 폴더, ~/.claude/skills에 설치 가능.
• A ** 코덱 플러그인** ([T3]]]) 묶음 스킬을 묶음, 그래서 팀은 게시하거나 작업 흐름 기본으로 포크 할 수 있습니다.

** 더 이상: ** Phase-by-phase playbook · Staged 채택 가이드 · 지시 그래프로 Workflow · Agent org · 작업 예

이것에 대해

글쓰기 코드는 더 이상 Bottleneck이 없습니다 - 에이전트는 시간에 생산합니다. Bottleneck은 코드 주위에 프로세스로 이동: 계획, 검토, 배포, 그리고 지배는 여전히 인간 속도와 인간적인 규모에서 실행. 이 repo는 SDLC를 재생합니다 그래서 그 단계는 구조로 유지합니다.

AI-Native SDLC — reusable workflow repo

A ready-to-inherit implementation of Anthropic’s AI-Native SDLC playbook: give your coding agent a goal or idea, and it scaffolds and drives the project through the full lifecycle — planning, design, build, test, deploy, and maintain — with human approval gates at every handoff.

This repo is three things at once:

• A Codex skill at skills/ai-native-sdlc/, installable into ~/.codex/skills.
• A Claude Code skill — the same folder, installable into ~/.claude/skills.
• A Codex plugin (.codex-plugin/plugin.json at the repo root) that bundles the skill, so teams can publish or fork it as their workflow baseline.

Learn more: Phase-by-phase playbook · Staged adoption guide · Workflow as a directed graph · Agent org · Worked example

What this is about

Writing code is no longer the bottleneck — agents produce it in hours. The bottleneck moved to the process around the code: planning, review, deployment, and governance still run at human speed and human scale. This repo reworks the SDLC so those stages keep up with the build. The loop is Plan → Design → Build → Test → Deploy → Maintain; every stage ends by committing a versioned artifact the next stage reads, human judgment concentrates at gates instead of line-by-line review, guardrails run as deterministic hooks rather than habits, and continuous evals replace stage-gate QA. The operating principle, in one sentence: the agent can do everything up to the production gate, but never crosses it.

Graph engineering

The loop is a directed graph, not a linear pipeline. Plays and artifacts are nodes, gates are human approval points, and triggers are the edges that fire the next stage: an accepted intent fires Design, an approved spec fires plan mode, a merged PR fires the pipeline, and a breached control band writes the next intent. Treating the workflow as a graph makes it automatable, parallelizable (independent branches run in separate worktrees), and auditable (node history is the record). The plays also form a separate adoption graph — start at the leaf plays (capture intent, CLAUDE.md, feedback loop, hooks, plan mode) and build outward. The machine-readable form is skills/ai-native-sdlc/assets/workflow-graph.example.yaml; full detail in skills/ai-native-sdlc/references/graph.md.

The workflow

Plan → Design → Build → Test → Deploy → Maintain
  ↑                                              │
  └────────────────── back to Plan ←─────────────┘

Each phase ends by committing a versioned artifact; the next phase starts by