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autoresearch

Optimize AI agent skill instructions through controlled experiments and evaluation.

1|1|Updated Feb 8, 2026
Search Tags:#automation#prompt engineering#ai agents#evaluation#skill optimization#experimentation
One-click install
npx skills add https://github.com/dapperdivers/roundtable-arsenal --skill autoresearch-dapperdivers
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/dapperdivers/roundtable-arsenal/tree/main/research/autoresearch
Command: npx skills add https://github.com/dapperdivers/roundtable-arsenal --skill autoresearch-dapperdivers

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the process of improving AI agent skills by replacing guesswork with structured experimentation, evaluation, and evidence-based iteration.

Core Features & Use Cases

  • Iterative Skill Optimization: Runs hypothesis-driven experiments that modify skill instructions, test outcomes, score results, and retain only improvements.
  • Evaluation Workflows: Provides repeatable dispatch, scoring, comparison, rollback, and reporting processes for improving agent performance.
  • Use Case: Improve a security briefing skill by testing instruction changes against a fixed evaluation prompt and keeping only versions that produce higher-quality results.

Quick Start

Use the autoresearch skill to optimize the target knight skill with a fixed evaluation prompt and run iterative experiments until improvements stop.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I optimize AI agent prompts through structured experimentation?▼

Yes, you can evaluate AI skills by running repeatable dispatch testing against fixed evaluation prompts, scoring the results, comparing versions, and rolling back any changes that do not produce higher-quality outputs.

What is the best way to improve autonomous agent performance without manual guesswork?▼

To set up iterative skill optimization, define a target skill to refine and a fixed evaluation prompt, then run automated experiments that handle dispatch testing, scoring, version comparison, and rollback handling until improvements stop.

How do I track and rollback unsuccessful prompt engineering changes in AI agents?▼

You can track and rollback unsuccessful prompt engineering changes by utilizing experiment tracking and rollback handling processes that measure performance and automatically revert to previous versions when modifications fail to improve quality.

Can I use controlled experimentation to refine dispatch testing workflows for autonomous agents?▼

Yes, you can use controlled experimentation to refine dispatch testing workflows by applying iterative modification and performance measurement to safely test and improve skill instructions within autonomous agent development.

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