autoresearch

Name: autoresearch
Availability: InStock
Author: ewallyw

Iterate missions through evaluator contracts with JSON logging.

Updated May 31, 2026

Search Tags:#autoresearch #evaluator contract #mission optimization #evaluation logging #stateful loop

Ready to run

npx skills add https://github.com/Ewallyw/claude-config-public --skill autoresearch-ewallyw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autoresearch
Source: https://github.com/Ewallyw/claude-config-public/tree/main/claude-config-master/claude-config-master/skills/autoresearch
Command: npx skills add https://github.com/Ewallyw/claude-config-public --skill autoresearch-ewallyw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a stateful improvement loop for missions, allowing for iterative enhancement while adhering to strict evaluator contracts and durable experiment logging.

Core Features & Use Cases

Stateful Improvement Loop: Iteratively refine missions based on evaluation results.
Strict Evaluator Contract: Ensures structured JSON output with required pass and optional score.
Durable Experiment Logging: Stores evaluation JSON and markdown decision logs for review and analysis.
Use Case: For a machine learning project, use this Skill to iteratively improve model performance with strict evaluation and detailed logging.

Quick Start

To start the autoresearch skill with a mission, use the command: autoresearch --mission-dir path/to/mission.

Frequently Asked Questions about autoresearch

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

FAQPage Schema

How do I set up an iterative improvement loop for machine learning missions?▼

An iterative improvement loop for machine learning missions iteratively refines models based on evaluation results while adhering to strict evaluator contracts. This approach ensures structured JSON output and durable experiment logging for persistent review.

What is a strict evaluator contract for structured JSON output?▼

A strict evaluator contract for structured JSON output mandates that evaluation results include a required `pass` boolean and an optional `score` field. This enforces rigorous, evaluator-driven iterative improvement by standardizing how evaluation metrics are logged and reviewed.

How do I log experiment decisions and evaluation results for persistent review?▼

To log experiment decisions and evaluation results for persistent review, the system stores evaluation JSON and markdown decision logs in durable storage. This allows for structured, long-term analysis of evaluator-driven iterative improvements across multiple runs.

Can I use a stateful improvement loop without external dependencies?▼

Yes, you can use a stateful improvement loop without external dependencies. The system operates with no listed dependencies, relying solely on its internal scripts and assets to manage the bounded, evaluator-driven iterative enhancement process.

What is the best way to run an evaluator-driven mission optimization?▼

The best way to run an evaluator-driven mission optimization is to execute the process with a specified mission directory. This triggers the stateful loop to apply strict evaluator contracts, log results in JSON format, and store markdown decision logs for review.

When should I not use a stateful loop for mission optimization?▼

You should not use a stateful loop for mission optimization if your task requires unbounded iterations or lacks clear pass/fail criteria. The system is specifically designed for bounded, evaluator-driven iterative improvement with strict JSON output contracts.