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Install a full engineering team into your AI agent. 7 SDLC roles · 105+ skills · 18 areas — Claude Code, opencode, Cursor, Codex, kilocode, antigravity, Gemini.

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agentic — one AI engineering team for every coding agent

Adaptive SDLC setup for Claude Code, Codex and OpenCode. Native agents by default; workflows, helper agents and model routing only when the task needs them.

Codex · real runs Claude Code · real runs OpenCode · real runs Antigravity · configs Gemini · configs Cursor · configs Kilocode · configs

Lean profile · default Quality gate · test + lint JEV · workflow + model routing Plain answers · ASD-STE100-based MCP · Context7, MemPalace, Playwright Telegram · notifications Smart upgrade

Website · Releases · npm · Usage guide · Reference

agentic coverage and real-run report


The problem it solves

Teams now use several coding agents at once. Each one gets its own prompts, its own habits and its own idea of "done". The result is uneven: one agent writes tests, another skips them; one asks before touching infrastructure, another does not.

agentic gives every agent the same playbook:

  • 🧭 One process. A product owner, team lead, developer and QA agent hand work to each other with clear acceptance criteria — in Codex, Claude Code and OpenCode alike. The default lean profile keeps a short core and a quality gate; --profile team installs the full role team.
  • 🛡️ Shared guardrails. 85 rules and 73 workflows for backend, frontend, DevOps, data, ML, security and more, loaded only when the task needs them.
  • 🔁 Install once, keep in sync. agentic install writes the native files each agent expects; agentic upgrade keeps them current without overwriting your own edits.
  • 🔔 Know when work is done. Optional Telegram messages when an agent finishes or waits for you.

Runs on the agents you already use

Agent Status Evidence
✅ Codex Real runs 0.163.0 · 13 pilot runs + live proof of JEV model routing, 2026-10-20
✅ Claude Code Real runs 2.1.304 · 13 pilot runs, 2026-10-20
✅ OpenCode Real runs 2.0.29 · 13 pilot runs, 2026-10-20
☑️ Antigravity Configs ship Agents, rules and workflows are installed; no recorded run yet
☑️ Gemini Configs ship Agents and workflows are installed; no recorded run yet
☑️ Cursor · Kilocode Configs ship Rules and MCP settings are installed; no recorded run yet

"Real runs" means the installed instructions were executed by that agent's own CLI and the result was checked independently. Details per feature: platform matrix.


See agentic in action

agentic in action

A real agentic tui session, recorded key by key: pick Codex, Claude Code and OpenCode, add Context7, JEV and Telegram, choose DevOps infrastructure plus backend — then the same product-owner request runs in all three agents inside the freshly installed project. Every frame is captured from a real run.


Quick start

npx @jetrabbits/agentic@latest          # interactive installer

Or non-interactive, for all three verified agents:

npx @jetrabbits/agentic@latest install --project-dir . \
  --agent-os codex,claude,opencode --areas software \
  --specializations software.backend,software.general

Keep it current:

agentic upgrade            # new binary + refreshed project instructions, your edits preserved

Other install options (curl script, release binary, build from source): usage guide.


Your AI team

Installed with --profile team; the default lean profile adds only explorer and reviewer.

Role What it does
🎯 product-owner Turns a request into scope and acceptance criteria; accepts the result
🗺️ pm Plans milestones and risks for larger work
🏗️ team-lead Designs the approach and signs off on quality
💻 developer Implements the change with tests
🔍 qa Verifies the result and gives go / no-go
📐 analyst Clarifies requirements, scenarios and API contracts
⚙️ devops-engineer Handles CI/CD, infrastructure and deployment
🧹 instruction-reviewer · memory-curator Review the run afterwards and suggest guidance fixes

Pick the specializations your project needs — 10 software (backend, frontend, mobile, data, ML, security, …) and 8 DevOps (Kubernetes, CI/CD, observability, SRE, …).


Honest numbers

We measure instead of promising. Latest round: 7 small tasks (bugfixes, a feature, a question, a scope trap, a bugfix in a file that already fails make lint, a multi-module bugfix and feature), 3 runs each, 21 runs per agent and mode. Cells show accepted runs and cost per accepted run (Codex and OpenCode: API-equivalent estimate).

Agent Plain agent Lean (default) Lean + JEV Role team + JEV
Claude Code (sonnet) 18/21 · $0.092 21/21 · $0.095 21/21 · $0.097 21/21 · $0.073¹
Codex (gpt-5.6-terra) 18/21 · $0.059 21/21 · $0.072 21/21 · $0.103 18/21 · $0.301
OpenCode (Qwen3.8-Flash-Next) 19/21 · $0.026 19/21 · $0.041 17/21 · $0.046 18/21 · $0.047
  • The plain agent is the cheapest everywhere. Every plain failure is the lint task: it left make lint failing. Lean passed that task on every Claude Code and Codex run.
  • Lean + JEV accepts as many runs as lean, or fewer, and costs 1.02–1.43 × lean. It stays opt-in.
  • ¹ Prompt-cache effect: the role team's long fixed prefix was already cached by earlier runs.
  • Not measured yet: large multi-step tasks. The multi-module tasks did not make the plain agent fail.

All numbers, per task: comparison.


Learn more

Contributing

See CONTRIBUTING.md for authoring standards, templates and the pull request process.

License

See LICENSE if present, or contact the maintainers.

About

Install a full engineering team into your AI agent. 7 SDLC roles · 105+ skills · 18 areas — Claude Code, opencode, Cursor, Codex, kilocode, antigravity, Gemini.

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