AnthropicのAIネイティブSDLC Playbookの既定の実装:あなたのコーディングエージェントに目標やアイデアを与え、フルライフサイクルを通してプロジェクトを足場し、駆動する - 計画、設計、ビルド、テスト、デプロイ、そして維持 - すべてのハンドオフで人間の承認ゲートで。

このレポは、一度に3つのことです。

• [skills/ai-native-sdlc/]の[[~/.codex/skills]]]にインストール可能でCodexスキル。
• [[~/.claude/skills]にインストール可能な同じフォルダであるClaudeコードスキル。
• スキルをバンドルするCodexプラグイン(.codex-plugin/plugin.json)は、チームがワークフローベースラインとして公開またはフォークすることができます。

もっと知る: フェーズバイフェーズの Playbook ・ 段階的な採用ガイド ・ 指示されたグラフ ・ エージェント org ・ 作業例

これについて

書き込みコードはもはやボトルネックではありません。エージェントは数時間で生成します。 ボトルネックは、コードの周りのプロセスに移動しました: 計画、レビュー、展開、およびガバナンスは、人間の速度と人間のスケールで実行されます。 このリポジトリはSDLCを再構築するので、それらのステージはビルドで維持します.

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