Enforce a structured, repeatable SDLC workflow across AI coding agents — from requirements and design through implementation, testing, code review, and release — with persistent memory, multi-agent coordination, and verification gates.
AI DevKit · Exchange information with active Codex, Claude Code, and other AI agents using ai-devkit agent list, detail, and send. Use when an agent needs to find another active agent, read its recent context, send it information, or request information back.
AI DevKit · Proactively orchestrate running AI agents — scan statuses, assess progress, send next instructions, and coordinate multi-agent workflows. Use when users ask to manage agents, orchestrate work across agents, or check on agent progress.
AI DevKit · Update CHANGELOG.md Unreleased items from git commits since the latest release. Use when users ask to update changelog/release notes from recent commits, with one concise line per commit and commit/PR links.
AI DevKit · Design phase guidance for reviewing feature design against requirements. Use when the user wants to validate architecture, review design docs, resolve design trade-offs, or run dev-lifecycle phase 3.
AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimnpx claudepluginhub codeaholicguy/ai-devkitBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
English | 中文
The control plane for AI coding agents.

AI DevKit gives Claude Code, Codex CLI, Gemini CLI, opencode, Pi, Cursor, GitHub Copilot, Devin, and other coding agents one local-first operating layer: one config, one console, local memory retrieval, cross-agent communication, and composable engineering skills led by dev-lifecycle.
.ai-devkit.json reconciles setup across the coding tools your team usesagent console is a live TUI dashboard for supervising local agents across providersagent send lets you route prompts, logs, and test output to running agents@ai-devkit/memory stores decisions, conventions, and fixes in local SQLite so agents search when needed instead of carrying everything in every promptdev-lifecycle, verify, tdd, review, debugging, security, docs, and simplification skills combine into reliable workflowsThe future is many AI coding agents. AI DevKit is the layer that makes them manageable.
Run npx ai-devkit@latest init and your project gets:
| What you need | What AI DevKit installs |
|---|---|
| One setup source | .ai-devkit.json for the agents and workflow you choose |
| Running-agent visibility | agent list, agent detail, and agent console |
| Addressable agents | agent send, --stdin, --wait, and agent groups where supported |
| Retrieval-based memory | Local SQLite memory exposed through MCP and CLI, searched only when useful |
| Composable senior-engineer workflow | dev-lifecycle plus verification, TDD, debugging, review, security, docs, and simplification skills |
Developers whose AI coding setup has grown from one assistant into a small, messy team of agents:
CLAUDE.md / .cursor/rules / AGENTS.md / MCP setup per toolBefore AI DevKit, your agents are powerful but scattered. After AI DevKit, they have shared setup, a control surface, searchable memory, communication paths, and reusable skills that travel with your repo without bloating every prompt.
| Without AI DevKit | With AI DevKit |
|---|---|
| You manage agents as isolated terminal tabs | You supervise them from ai-devkit agent console |
| You hand-maintain every agent setup | One config reconciles agent files, skills, and MCP setup |
| You copy logs and context between sessions | agent send routes prompts and stdin to running agents |
| You repeat project rules in every chat | Agents retrieve relevant memory and docs only when useful |
| The agent jumps from prompt to code | dev-lifecycle guides requirements, design, planning, implementation, testing, and review |
| "Done" means the agent stopped editing | "Done" requires fresh verification output |
npx ai-devkit@latest init
One wizard. Pick your agents, install the control-plane pieces you need, and give every tool the same operating model. It writes project-local files you can review and commit. Re-run it whenever your agent list or workflow changes.
Here's what lands in your repo:
your-project/
├── .ai-devkit.json # single source of truth (re-run init anytime)
├── .claude/ # or .cursor/, .codex/, etc. per agent you picked
│ ├── skills/ # dev-lifecycle, verify, memory, tdd, ...
│ └── settings.json # MCP servers wired up (incl. @ai-devkit/memory)
└── docs/ai/
├── requirements/ # phase 1 — what to build, why
├── design/ # phase 2 — how it'll be built
├── planning/ # phase 3 — task-by-task plan
├── implementation/ # phase 4 — execution notes
└── testing/ # phase 5 — coverage strategy
AI DevKit ships a agent control plane for everyday multi-agent work:
# List running sessions across providers
ai-devkit agent list
# Open the live terminal UI
ai-devkit agent console
HelloAGENTS — The orchestration kernel that makes any AI CLI smarter. Adds intelligent routing, unified QA gates, safety guards, and notifications.
Persistent memory, shared standards, and structured workflows for AI coding agents. Detects project setup and injects agent context automatically.
Complete project development toolkit: 23 agents, 23 slash commands, 29 lifecycle hooks, and 69 reusable skills for Claude Code workflows
Production-grade engineering skills for AI coding agents — covering the full software development lifecycle from spec to ship.
11 agents, 35 skills, 18 commands, 9 hooks — spec-driven multi-agent orchestration for Claude Code, with optional cross-device semantic memory.
Repowire mesh usage skills for AI coding agents: cross-agent review and planning, delegate, usage patterns, and install/update. Backend-agnostic and parameterised on the agent you choose.