autopilot

Automate planning and feature delivery into a converged plan or draft pull request.

1|Updated May 15, 2026
One-click install
npx skills add https://github.com/mslshao/claude-harness --skill autopilot-mslshao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autopilot
Source: https://github.com/mslshao/claude-harness/tree/main/dotclaude/skills/autopilot
Command: npx skills add https://github.com/mslshao/claude-harness --skill autopilot-mslshao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates an end-to-end planning or implementation pipeline when you want work to progress without waiting for human approval at every checkpoint. It helps turn a rough task, ticket, or bead into either a converged execution plan or a draft pull request with decision gates, audit logging, and escalation handling built in.

Core Features & Use Cases

  • Autonomous planning mode: Refines scope, decomposes work, stress-tests the plan, and persists approved work as linked beads for async review.
  • Autonomous build mode: Extends the planning flow into execution, parallel agent orchestration, review fan-out, implementation approval, draft PR creation, and bot-comment remediation.
  • Decision-gated automation: Uses a dedicated decision-maker agent at key checkpoints, enforces iteration limits, logs every major state change to beads, and escalates instead of guessing when confidence drops.
  • Use case: Start this Skill with a Jira ticket before stepping away, and return to either a structured bead plan ready for review or a draft PR that has already passed internal review and initial bot feedback loops.

Quick Start

Ask the autopilot skill to take a task or ticket in plan mode for an autonomous bead-based execution plan, or in build mode for an end-to-end implementation that finishes with a draft pull request.

Frequently Asked Questions about autopilot

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

FAQPage Schema
How do I automate Jira ticket to draft pull request workflows without manual approval at every step?▼

You can automate Jira ticket to draft pull request workflows by using autonomous build mode, which applies decision-gated agent orchestration to handle scope refinement, code implementation, and parallel review fan-out without waiting for manual approval at every checkpoint.

What is autonomous planning for software engineering and when do I need it?▼

Autonomous planning for software engineering is the process of refining task scope, decomposing work, and stress-testing execution plans using agent-based decision gates. You need it when you want work to progress asynchronously and return a structured plan ready for your review.

How do I execute parallel code reviews and manage bot feedback autonomously?▼

You execute parallel code reviews and manage bot feedback autonomously by triggering build mode, which orchestrates specialist agent fan-out for review, enforces iteration limits, and handles CI or bot-comment remediation before creating a draft PR.

Does autonomous code generation handle escalation when confidence drops during implementation?▼

Autonomous code generation handles escalation when confidence drops by enforcing deterministic checkpoint validation and iteration limits. Instead of guessing, the decision-maker agent escalates the issue and logs every major state change to beads for audit tracking.

Can I use Git worktrees for isolated autonomous feature delivery and PR creation?▼

Yes, you can use Git worktrees for isolated autonomous feature delivery. The skill executes implementation within Git worktrees to isolate changes before performing GitHub PR operations and generating a draft pull request for review.

What are the limitations of hands-free autonomous planning for complex feature delivery?▼

Hands-free autonomous planning for complex feature delivery is bounded by built-in iteration limits and deterministic checkpoint validation. If agent confidence drops below thresholds during scope refinement or implementation, the workflow escalates rather than forcing a completion.