autoresearch

Orchestrate autonomous loops to plan, execute, verify, and learn from codebase changes.

Updated Apr 3, 2026

Search Tags:#automation #planning #devops #iteration #autoresearch #git-memory

Ready to run

npx skills add https://github.com/carlosrobertodevops/dev-machine-config --skill autoresearch-carlosrobertodevops

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/carlosrobertodevops/dev-machine-config/tree/main/claude/skills/autoresearch
Command: npx skills add https://github.com/carlosrobertodevops/dev-machine-config --skill autoresearch-carlosrobertodevops

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Claude Autoresearch automates goal-directed iteration, enabling self-contained loops that plan, execute, verify, and learn from codebase changes. It supports bounded or unlimited iterations, interactive setup gates, and keeps memory of past experiments for continuous improvement.

Core Features & Use Cases

Autonomous loop orchestration: Goal → Scope → Verify → Iterate with optional iterative limits and interactive prompts.
Interactive planning and guardrails: Plan, guard, and launch autoresearch runs with safety checks before execution.
Multi-tool chaining: Seamlessly hand off results to downstream autoresearch tools (plan, security, debug, fix, scenario, predict, ship, learn).
Git-backed memory: Read past experiments, revert or reuse successful changes, and learn from failures.

Quick Start

Create a ready-to-run autoresearch invocation and run it.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I automate codebase changes with autonomous iteration loops?▼

Autonomous iteration loops automate codebase changes by orchestrating a goal-directed cycle to plan, execute, verify, and learn from modifications. You define a goal and scope, then the loop runs bounded or unlimited iterations with interactive safety gates before execution.

What is goal-directed iteration in software engineering automation?▼

Goal-directed iteration is an automation process where an agent continuously loops through planning, executing, and verifying codebase changes until a specified objective is met. It uses interactive setup gates to ensure safety before running bounded or unlimited iterations.

How do I set up guardrails for automated code modification agents?▼

You set up guardrails for automated code modification using interactive planning and setup gates before execution. This allows you to define the scope, verify safety checks, and establish iterative limits to safely launch autonomous runs.

Can I use Git memory to revert autonomous code changes from past experiments?▼

Yes, you can use Git-backed memory to read past experiments, revert unsuccessful autonomous code changes, or reuse successful modifications. This feature enables continuous improvement by allowing the system to learn from previous failures.

What is the best way to chain DevOps automation tasks end-to-end?▼

The best way to chain DevOps automation tasks is using multi-tool chaining to hand off results to downstream tools. This enables seamless end-to-end automation by passing outputs to plan, security, debug, fix, scenario, predict, ship, and learn tools.