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ai-factory-executor

Orchestrate GitHub issue implementation via isolated subagents for cloning, branching, and PR submission.

3|2|Updated Aug 1, 2025
Search Tags:#automation#github#orchestration#docker#ci-cd#ai-agent
One-click install
npx skills add https://github.com/ClearMeasureLabs/bootcamp-palermo-workorders --skill ai-factory-executor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-factory-executor
Source: https://github.com/ClearMeasureLabs/bootcamp-palermo-workorders/tree/main/.bob/skills/ai-factory-executor
Command: npx skills add https://github.com/ClearMeasureLabs/bootcamp-palermo-workorders --skill ai-factory-executor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dotnet-sdk-10.0, gh-cli, docker, git, and includes scripts (resource) components.

What problem does it solve?

This Skill solves the bottleneck of manual software development by autonomously implementing GitHub issues, managing feature branches, and monitoring CI/CD pipelines without human intervention.

Core Features & Use Cases

  • Autonomous Implementation: Automatically clones repositories, creates feature branches, and executes implementation tasks using AI agents.
  • Parallel Orchestration: Manages multiple concurrent subagents to process entire project backlogs simultaneously with full environment isolation.
  • Quality Assurance: Integrates automated quality gates including secret scanning, style analysis, and test suite execution before PR submission.

Quick Start

Run the ai-factory-executor skill to discover and autonomously implement all open issues labeled AI Factory in the current repository.

Frequently Asked Questions about ai-factory-executor

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

FAQPage Schema
How do I autonomously implement GitHub issues and automate pull request creation?▼

Autonomous GitHub issue implementation is achieved by spawning isolated Docker subagents that clone repositories, manage feature branches, and submit pull requests. The ai-factory-executor orchestrates this process to resolve backlogs without manual coding intervention.

Can I process multiple GitHub issues in parallel with full environment isolation?▼

Yes, parallel orchestration of multiple GitHub issues is supported using isolated subagents. This allows concurrent processing of entire project backlogs while maintaining strict environment separation through Docker containerization for each task.

Do I need Docker and .NET 10 to run automated GitHub issue resolution?▼

Docker, .NET 10 runtime, GitHub CLI, and Git are required dependencies for running automated GitHub issue resolution. These tools enable containerized task execution, branch management, and CI/CD pipeline monitoring.

What automated quality gates are enforced before a pull request is submitted?▼

Automated quality gates including secret scanning, code style analysis, and test suite execution are enforced before pull request submission. This ensures code quality and security standards are met autonomously during issue implementation.

How do I start automating issue resolution for my repository backlog?▼

Run the executor in your repository to discover and implement all open issues labeled AI Factory. It automatically clones the repository, creates feature branches, executes implementation tasks, and monitors CI/CD status for each issue.

When should I not use autonomous agents for GitHub issue implementation?▼

Autonomous agents for GitHub issue implementation are not suitable for tasks requiring deep domain context, complex architectural decisions, or manual database migrations. They target high-throughput, well-defined issue resolution rather than nuanced development workflows.

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