智能代理框架 是 AI Skill Hub 本期精选Cursor规则之一。综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
智能代理框架 是一套专为 Cursor AI 代码编辑器定制的编程规范规则集。通过精心设计的规则约束,它能引导 AI 生成符合团队标准的高质量代码,减少人工审查成本,保障代码风格一致性。适合个人开发者快速提升代码质量,也适合团队统一 AI 辅助编程的规范。
智能代理框架 是一套专为 Cursor AI 代码编辑器定制的编程规范规则集。通过精心设计的规则约束,它能引导 AI 生成符合团队标准的高质量代码,减少人工审查成本,保障代码风格一致性。适合个人开发者快速提升代码质量,也适合团队统一 AI 辅助编程的规范。
# 在 Cursor 中安装规则 # 1. 打开 Cursor → Cmd+Shift+P # 2. 搜索 "Open Cursor Settings" 或直接进入 Settings # 3. 找到 "Rules for AI" / "Cursor Rules" 配置项 # 4. 粘贴本工具的规则内容 # 也可以克隆仓库后本地导入 git clone https://github.com/KbWen/agentic-os # 按照 README 中的说明添加规则文件
# 命令行使用
agentic-os --help
# 基本用法
agentic-os input_file -o output_file
# Python 代码中调用
import agentic_os
# 示例
result = agentic_os.process("input")
print(result)
# agentic-os 配置文件示例(config.yml) app: name: "agentic-os" debug: false log_level: "INFO" # 运行时指定配置文件 agentic-os --config config.yml # 或通过环境变量配置 export AGENTIC_OS_API_KEY="your-key" export AGENTIC_OS_OUTPUT_DIR="./output"
<p align="center"> <strong>"Done." — your AI coding agent, about code it didn't test.</strong><br/> A rules file <em>asks</em> your agent to behave. Agentic OS <strong>checks that it did</strong> — leaked secrets and a green check over zero tests fail your git hooks and CI; a skipped review or phase shows up when the validator reads the work trail. Backstops you control, not the agent's own word. </p>
<p align="center"> <strong>A governance-first layer for AI coding agents</strong> — guardrails and a gated workflow for Claude Code, Codex, Cursor, Copilot, Antigravity, or any Markdown-reading agent. </p>
<p align="center"> <a href="https://github.com/KbWen/agentic-os/releases"><img src="https://img.shields.io/github/v/release/KbWen/agentic-os?style=flat-square&label=release" alt="Release"/></a> <a href="https://github.com/KbWen/agentic-os/actions/workflows/validate.yml"><img src="https://img.shields.io/github/actions/workflow/status/KbWen/agentic-os/validate.yml?branch=main&style=flat-square&label=CI" alt="CI"/></a> <a href="https://github.com/KbWen/agentic-os/actions/workflows/security.yml"><img src="https://img.shields.io/github/actions/workflow/status/KbWen/agentic-os/security.yml?branch=main&style=flat-square&label=Security" alt="Security"/></a> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/license-MIT-22c55e?style=flat-square" alt="MIT"/></a> · <a href="docs/README_zh-TW.md">繁體中文</a> · <a href="CONTRIBUTING.md">Contributing</a> · <a href="CHANGELOG.md">Changelog</a> </p>
<p align="center"> <img src="docs/assets/concept-hero.png" alt="An AI coding agent confidently claims 'Done. Tests pass. Shipping it.' and Agentic OS stamps the claim '[citation needed]'. Agentic OS demands evidence for what your AI agent claims — leaked secrets, missing tests, skipped reviews — through git hooks and CI instead of taking the agent's word." width="820"/> </p>
<p align="center"><sub>It checks the evidence behind what your AI coding agent claims — secrets, tests, reviews — through your git hooks and CI. Here's a gate firing:</sub></p>
<p align="center"> <img src="docs/assets/workflow-demo.gif" alt="An AI coding agent in a terminal claims a task is done and tries to ship it; the Agentic OS gate returns verdict FAIL because the work trail has no review or test evidence, blocks the ship, and only passes after review, tests, and evidence are recorded." width="780"/> </p>
The /bootstrap, /review, and /ship above are plain text prompts — your agent maps them to the workflow files in the repo, so they run the same in Cursor or Codex as in Claude Code.
Or run a gate yourself, no install — the credential scan that catches a leaked key before it reaches git history:
bash demo/run.sh # Windows (PowerShell): pwsh demo/run.ps1
<p align="center"> <img src="docs/assets/demo-gate.gif" alt="Terminal recording of the real credential gate: an AI agent writes config.env containing a leaked aws_access_key_id and reports 'Done - config added.'; Agentic OS runs scan_credentials.py, which detects the credential with the value redacted, and the commit is BLOCKED — the agent said done, the machine said no. Reproduce with bash demo/run.sh." width="820"/> </p>
<details> <summary>Full terminal output</summary>
An AI agent wrote this file and reported: "Done — config added."
----------------------------------------------------------------
DB_HOST=prod.internal
aws_access_key_id = AKIA****************
----------------------------------------------------------------
Without a gate, that commit lands and the key is in git history forever.
Agentic OS runs this before the commit is allowed:
$ scan_credentials.py config.env
CREDENTIAL PATTERN(S) DETECTED (values redacted):
config.env:2: aws-access-key-id
Rotate the exposed secret, remove it from the change, then retry.
Commit BLOCKED. The agent said "done"; the machine said no — and it
redacted the value instead of echoing your secret back at you.
</details>
Your agent can still cut a corner. What it can't do is get a leaked secret, a green check over zero tests, or a skipped review past the hooks and CI — those run whether it cooperates or not. The key above is generated at runtime and redacted on output, so the demo never stores a real secret.
git clone https://github.com/KbWen/agentic-os.git
./agentic-os/installers/deploy_brain.sh --dry-run /path/to/your-project # preview, no changes
./agentic-os/installers/deploy_brain.sh /path/to/your-project # deploy
Then tell your agent: "Read AGENTS.md and follow it. Do not claim completion until /review and /test pass." — followed by /bootstrap and your task.
| Your starting point | First command |
|---|---|
| Brand-new project, multi-feature idea | /spec-intake |
| Existing repo adopting Agentic OS | /audit (read-only, zero risk) |
| Single concrete task | /bootstrap |
Existing files are never overwritten (saved as .acx-incoming sidecars to merge). Windows / no-Python mode, updating, customizing without conflicts, turning the CI floor into a required check, and the full entry-point templates → docs/INSTALL.md.
A rules file — Cursor Rules, a plain AGENTS.md — is a prompt the agent can ignore. Agentic OS keeps that discipline (plan before editing, no unasked-for refactors) and adds a layer the agent doesn't control:
| Failure mode | What catches it | Where |
|---|---|---|
| A secret committed to history | scan_credentials.py (shown above) | pre-commit hook + CI |
| "Tests pass" with no tests | CI runs the real suite | pull request |
| A phase skipped with no evidence | validate.sh reads the work trail | pre-commit (local) |
The third row is the part a rules file can't reach: validate.sh parses each task's work log and fails if a required phase was skipped or its evidence is missing. The local pre-commit hook is opt-in and you can --no-verify past it; the three required CI checks (Framework Validation, ShellCheck, Check Markdown Links) are the floor that can't be skipped — they must pass before any PR merges. The security scanning jobs (credential scan, SAST, dependency audit) run on every PR but are not required merge checks unless you add them to branch protection. The Security badge above is this repo running the same credential and SAST gates on its own every push.
What is Agentic OS? An open-source governance framework for AI coding agents. It gives agents like Claude Code, Codex, Cursor, Copilot, and Antigravity a repeatable workflow — plan, build, review, test, ship — and enforces gates so they can't skip steps or call a task "done" without verifiable evidence.
How do I stop an AI agent from skipping tests or shipping unverified code? That's the core of it. The credential scan, the test suite, and the phase/evidence validator run in your git hooks and CI — so a leaked secret, a missing test, or a skipped review fails the commit or the build, regardless of what the agent reports. The agent can still cut a corner; it just can't get that corner past the checks it doesn't control.
How is it different from Cursor Rules or a plain AGENTS.md file? A rules file tells the agent how to behave, and the agent can ignore it. Agentic OS adds the workflow and the checks that hold it to that behavior: phase sequencing, evidence requirements, scope discipline, and a single source of truth that remembers decisions across sessions. The skills and discipline are still guidance the agent follows; what's enforced is the part that fails your commit or CI — leaked secrets, missing tests, a skipped phase.
Does it lock me into one AI vendor? No. It's model-agnostic Markdown — native entry points for Claude Code (CLAUDE.md), Codex (AGENTS.md), and Gemini / Antigravity (GEMINI.md), and it works with Cursor, Copilot, and any other LLM agent through the same workflow files.
Is it free? Yes — MIT licensed. Fork it and ship it.
高质量的AI编码代理框架,具有自动化和可扩展性
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,智能代理框架 在Cursor规则赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | agentic-os |
| 原始描述 | 开源Cursor规则:Governance framework for AI coding agents. It runs them through a five-step work。⭐20 · Python |
| Topics | agent-frameworkai-agentpython |
| GitHub | https://github.com/KbWen/agentic-os |
| License | MIT |
| 语言 | Python |
收录时间:2026-06-19 · 更新时间:2026-06-20 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端