Готовая к наследованию реализация SDLC от Anthropic: дайте своему агенту по кодированию цель или идею, и он будет поддерживать проект на протяжении всего жизненного цикла — планирование, проектирование, строительство, тестирование, развертывание и обслуживание — с человеческими воротами одобрения при каждой передаче.

Это репо сразу три вещи:

**Кодексный навык ** в skills/ai-native-sdlc/, устанавливаемый в ~/.codex/skills. Claude Code skills — та же папка, устанавливаемая в ~/.claude/skills.

• Плагин Codex ([T3]] в корне РЕПО), который объединяет навык, поэтому команды могут публиковать или раскрывать его в качестве базового уровня рабочего процесса.

**Узнать больше: ** Phase-by-phase playbook · Руководство по поэтапному принятию · Workflow как направленный граф · Agent org · Worked example

##Что это такое

Написание кода больше не является узким местом — агенты производят его за несколько часов. Узкое место занял процесс вокруг кода: планирование, обзор, развертывание и управление по-прежнему работают с человеческой скоростью и человеческим масштабом. Эта репо перерабатывает SDLC, чтобы эти этапы не отставали от сборки.

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