agent-infra
Health Gecti
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 74 GitHub stars
Code Basarisiz
- process.env — Environment variable access in .agents/scripts/platform-adapters/platform-sync.js
- exec() — Shell command execution in .agents/scripts/task-short-id.js
- rm -rf — Recursive force deletion command in .agents/skills/archive-tasks/scripts/archive-tasks.sh
Permissions Gecti
- Permissions — No dangerous permissions requested
Bu listing icin henuz AI raporu yok.
Collaboration infrastructure for AI coding agents | AI 编程代理的协作基础设施
Agent Infra
Collaboration infrastructure for AI coding agents — skills, workflows, and sandboxes for Claude Code, Codex, Gemini CLI, and OpenCode.
From issue to merged PR in 11 commands. Define a requirement, let AI handle analysis, planning, coding, and three-stage review — you only step in when it matters.
English · 中文
Why agent-infra?
Teams increasingly mix Claude Code, Codex, Gemini CLI, OpenCode, and other AI TUIs in the same repository, but each tool tends to introduce its own commands, prompts, and local conventions. Without a shared layer, the result is fragmented workflows, duplicated setup, and task history that is difficult to audit.
agent-infra standardizes that shared infrastructure. It gives every supported AI TUI the same task lifecycle, the same skill vocabulary, the same project governance files, isolated development sandboxes, and the same upgrade path, so teams can switch tools without rebuilding process from scratch.
See it in Action
Once initialized, open the project in your AI TUI and install the latest skills:
/update-agent-infra
AI reads
.agents/.airc.json, auto-locates the installed template root, and syncs the latest skill manifests, managed files, and registry deterministically viasync-templates.js.
Scenario: Issue #42 reports "Login API returns 500 when email contains a plus sign". Here is the full fix lifecycle — AI does the heavy lifting, you stay in control:
/import-issue 42 # AI reads the issue, creates a task, extracts requirements
/analyze-task <task-id> # AI scans the codebase, finds the root cause, writes analysis.md
/review-analysis <task-id> # AI self-reviews: "Approved. 0 blockers — proceed to design."
/plan-task <task-id> # AI proposes a fix plan
/review-plan <task-id> # AI self-reviews the plan: "Approved. Ready for implementation."
You review the plan and reply in natural language:
The plan looks right, but don't change the DB schema.
Just fix it at the application layer in LoginService.
AI re-runs
/plan-taskto update the plan accordingly and confirms.
/code-task <task-id> # AI writes the fix, adds a test for [email protected] — green
/review-code <task-id> # AI reviews its own code: "0 blockers, 1 minor (missing JSDoc)."
/code-task <task-id> # AI fixes the minor issue and re-validates
/commit
/create-pr <task-id> # PR opened, auto-linked to issue #42
/complete-task <task-id> # task archived
11 commands. 1 natural-language correction. From issue to merged PR. That is the entire SOP — programming can have a standard operating procedure too.
Every command above works the same way in Claude Code, Codex, Gemini CLI, and OpenCode. Switch tools mid-task — the workflow state follows. For what each skill does under the hood, see Built-in AI Skills.
Key Features
- Multi-AI collaboration: one shared operating model for Claude Code, Codex, Gemini CLI, and OpenCode
- Bootstrap CLI + skill-driven execution: initialize once, then let AI skills drive day-to-day work
- Bilingual project docs: English-first docs with synchronized Chinese translations
- Template-source architecture:
templates/mirrors the rendered project structure - AI-assisted updates: template changes can be merged while preserving project-specific customization
Quick Start
1. Install agent-infra
Option A - npm (recommended)
npm install -g @fitlab-ai/agent-infra
Option B - Shell script
# Convenience wrapper — detects Node.js and runs npm install -g internally
curl -fsSL https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/install.sh | sh
Option C - Homebrew (macOS)
# Newer Homebrew refuses to load formulae from third-party taps until trusted,
# which silently blocks upgrades. Trust the tap once before installing.
brew trust fitlab-ai/tap
brew install fitlab-ai/tap/agent-infra
Updating agent-infra
npm update -g @fitlab-ai/agent-infra
# or, if installed via Homebrew:
brew upgrade agent-infra
Check your current version:
ai version
# or: agent-infra version
2. Initialize a new project
cd my-project
ai init
# or: agent-infra init
The CLI collects project metadata, installs the update-agent-infra seed command for all supported AI TUIs, and generates .agents/.airc.json.
aiis a shorthand foragent-infra. Both commands are equivalent.
3. Render the full infrastructure
Open the project in any AI TUI and run update-agent-infra:
| TUI | Command |
|---|---|
| Claude Code | /update-agent-infra |
| Codex | $update-agent-infra |
| Gemini CLI | /{{project}}:update-agent-infra |
| OpenCode | /update-agent-infra |
This detects the packaged template version and renders all managed files. The same command is used both for first-time setup and for future template upgrades.
Core Commands
The most-used lifecycle commands, in delivery order. The command prefix varies by TUI (/skill in Claude Code/OpenCode, $skill in Codex, /{{project}}:skill in Gemini CLI); the workflow semantics stay the same.
| Command | Purpose |
|---|---|
create-task / import-issue |
Start a task from a description or a GitHub Issue |
analyze-task → review-analysis |
Capture scope and risks, then review the analysis |
plan-task → review-plan |
Design the approach, then review the plan |
code-task → review-code |
Implement and test, then run a structured code review |
commit → create-pr → complete-task |
Commit, open a PR, and archive the task |
See the full catalog — task status, release, security, and project-maintenance skills — in Built-in AI Skills.
What You Get
After setup, your project gains a complete AI collaboration infrastructure:
my-project/
├── .agents/ # Shared AI collaboration config
│ ├── .airc.json # Central configuration
│ ├── workspace/ # Task workspace (git-ignored)
│ ├── skills/ # Built-in AI skills
│ ├── workflows/ # 4 prebuilt workflows
│ └── templates/ # Task and artifact templates
├── .claude/ # Claude Code config and commands
├── .gemini/ # Gemini CLI config and commands
├── .opencode/ # OpenCode config and commands
└── AGENTS.md # Universal AI agent instructions
Documentation
In-depth guides live under docs/en/:
- Architecture Overview — bootstrap CLI, end-to-end flow, layered architecture
- Platform Support — macOS, Linux, Windows; sandbox engines and resources
- Sandbox — sandbox aliases, host-sandbox file exchange, user-level dotfiles channel
- Built-in AI Skills — the full skill catalog by use case
- Custom Skills — create and sync project-specific skills
- Custom TUI Configuration — adapt agent-infra to non-built-in AI TUIs
- Prebuilt Workflows — the gated delivery lifecycle and example flow
- Configuration Reference —
.agents/.airc.json, external sources, version management - File Management Strategies — managed / merged / ejected update strategies
Contributing
See CONTRIBUTING.md for development guidelines.
License
Yorumlar (0)
Yorum birakmak icin giris yap.
Yorum birakSonuc bulunamadi