ai-autonomy

Automate AI-driven development workflows with git-based state tracking and logging.

7|Updated Jan 27, 2026
One-click install
npx skills add https://github.com/wsxwj123/opencode-skills-backup --skill ai-autonomy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-autonomy
Source: https://github.com/wsxwj123/opencode-skills-backup/tree/main/ai-autonomy
Command: npx skills add https://github.com/wsxwj123/opencode-skills-backup --skill ai-autonomy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, python-dotenv, and includes scripts (resource) and references (resource) components.

What problem does it solve?

AI 自主开发系统让团队能够在任意项目中为 AI 注入自治能力,实现持续开发、跨会话任务跟进与无人值守执行。

Core Features & Use Cases

核心特性包括两种工作模式:嵌入式模式(每个会话自动读取任务继续开发)和脚本循环模式(无人值守批量执行)。工作流包含初始化、任务分派、执行、验证、交接日志与版本控制等环节,支持自定义模板、任务工单、进度记录和多智能体协作。典型用例包括在新项目中注入自治能力,读取 feature_list.json 获取待办任务并自动推进开发;在需要长时间无人工干预时,使用 .autonomy/run_autonomy.py 进行循环驱动。

Quick Start

在任意项目文件夹中执行“初始化自治”以进入嵌入式模式,随后通过会话指令让 AI 继续工作,或运行 run_autonomy.py 启动全自动循环。

Frequently Asked Questions about ai-autonomy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up continuous AI-driven autonomous development workflows in my software project?

Continuous AI-driven autonomous development workflows are set up by initializing the autonomy system in your project directory, which creates task tracking files like feature_list.json to enable hands-free, multi-agent task execution.

Can I run AI agents for unattended batch execution of software tasks?

Yes, unattended batch execution of software tasks is supported through the script-loop mode. You execute the run_autonomy.py script to drive AI agents continuously without manual intervention across multiple development sessions.

How does AI agent state tracking work across different coding sessions?

AI agent state tracking across coding sessions works by using git-based state tracking alongside progress.txt and feature_list.json files, ensuring the workflow resumes correctly from the last completed task.

What is the difference between embedded mode and script-loop mode for AI automation?

Embedded mode automatically reads tasks to continue development within each active session, whereas script-loop mode drives unattended batch execution by running the run_autonomy.py script for fully autonomous loops.

Do I need Python dependencies like httpx to enable autonomous AI workflows?

Yes, Python dependencies including httpx and python-dotenv are required to enable autonomous AI workflows, providing the necessary environment configuration and HTTP communication capabilities for the multi-agent coordination.

What are the limitations of using git-based state tracking for multi-agent workflows?

Git-based state tracking for multi-agent workflows relies on structured files like feature_list.json and progress.txt for task assignment and validation, meaning improper logging or file conflicts can disrupt the orchestration of autonomous task execution.