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autoresearch

Run iterative mutations and binary scoring to refine Claude Code skill prompts.

46|7|Updated Oct 12, 2025
Search Tags:#prompt optimization#evaluation#autonomous testing#code enhancement#AI skill refinement
One-click install
npx skills add https://github.com/adriannoes/awesome-vibe-coding --skill autoresearch-adriannoes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/adriannoes/awesome-vibe-coding/tree/main/cursor-and-claude/skills/autoresearch
Command: npx skills add https://github.com/adriannoes/awesome-vibe-coding --skill autoresearch-adriannoes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the difficulty of manually tuning prompts and improving Claude Code skills by enabling autonomous iterative experimentation and refinement.

Core Features & Use Cases

  • Automated Prompt Optimization: Repeatedly tests and mutates skill prompts to enhance output quality.
  • Evaluation and Scoring: Uses binary eval criteria to reliably measure improvements.
  • Use Case: A developer wants to improve a code generation skill's accuracy by automatically discovering the best prompt modifications without manual trial and error, especially useful for complex or multi-step tasks.

Quick Start

Describe the skill to optimize, provide test prompts and eval criteria, then run the autoresearch loop to automatically improve the skill over multiple iterations.

Frequently Asked Questions about autoresearch

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

FAQPage Schema
How do I automate prompt optimization for Claude Code skills?▼

Automate prompt optimization by configuring a skill with test prompts and binary evaluation criteria, then running an autonomous loop that mutates and scores outputs to select the best prompt modifications without manual intervention.

What is autonomous prompt tuning and how does it work?▼

Autonomous prompt tuning iteratively mutates skill prompts, evaluates the outputs against defined binary criteria, and selects the highest-scoring modifications to progressively enhance AI task performance.

How do I evaluate and benchmark AI skills without manual trial and error?▼

Evaluate and benchmark AI skills by providing test prompts and binary eval criteria to an autonomous testing loop, which repeatedly runs mutations and scores outputs to reliably measure performance improvements.

Can I improve code generation accuracy by automatically discovering the best prompt modifications?▼

Yes, you can improve code generation accuracy by running autonomous iterative experimentation that tests prompt mutations and selects the modifications that yield the highest scores against your binary evaluation criteria.

Do I need to manually monitor the skill refinement loop once it starts?▼

No manual intervention is required once configured; the autonomous testing loop handles iterative mutations, scoring against binary criteria, and selecting the best prompt modifications automatically.

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