autoresearch

Automate iterative keep/discard experiment loops over a configured repository.

11|1|Updated Feb 23, 2026
One-click install
npx skills add https://github.com/jimezsa/opencolab --skill autoresearch-jimezsa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/jimezsa/opencolab/tree/main/projects/SKILLS/autoresearch
Command: npx skills add https://github.com/jimezsa/opencolab --skill autoresearch-jimezsa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Iterative keep/discard experiment loops over a single, explicitly configured repository, enabling disciplined, human-in-the-loop experimentation without manual drift.

Core Features & Use Cases

  • Bounded, repeatable experiment cycles with clear guardrails and branch management.
  • Enforces edits only to the configured file and confines operations to the target repo.
  • Provides structured reporting of outcomes to inform keeps/discards and next steps.

Quick Start

Configure the repo_path, editable_file_path, run_command, and metric_rule, then run a single bounded autoresearch loop to evaluate results.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I automate iterative experiment loops in a git repository?▼

You can automate iterative experiment loops by configuring a repository path, a single editable file, a run command, and a metric rule to execute bounded cycles and record outcomes.

What is the best way to manage keep or discard decisions for code experiments?▼

Managing keep or discard decisions is handled by running one experiment at a time over disposable branches, enforcing repo confinement, and recording structured outcomes to inform next steps.

Can I run multiple experiments simultaneously across different files in my repo?▼

No, you cannot run multiple experiments simultaneously; the automation enforces running one experiment at a time and restricts edits to a single configured editable file.

Do I need to provide a results file to evaluate experiment outcomes?▼

Providing a results file is necessary for recording outcomes; the automation evaluates the configured metric rule and writes the experiment results to the specified file when provided.

What are the limitations of using automated experimentation workflows?▼

Limitations include strict repo confinement, restriction to a single editable file, running only one experiment at a time, and requiring disposable branches for controlled bounded cycles.