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openui-autoresearch

Orchestrate machine learning research campaigns with hypothesis generation and contract enforcement.

1|Updated Jul 12, 2026
Search Tags:#research#machine learning#training#experimentation#autoresearch#openui
One-click install
npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill openui-autoresearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openui-autoresearch
Source: https://github.com/Tyler-R-Kendrick/slm-training/tree/main/.agents/skills/openui-autoresearch
Command: npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill openui-autoresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the challenge of managing complex, multi-stage machine learning research campaigns by enforcing strict, evidence-based workflows and preventing unverified model training.

Core Features & Use Cases

  • Campaign Execution: Orchestrates the full lifecycle of an experiment, from literature discovery and hypothesis generation to data synthesis and RL readiness validation.
  • Contract Enforcement: Ensures all research adheres to non-negotiable architecture invariants and requires documented, peer-reviewed evidence before any model promotion.
  • Use Case: A researcher can use this to initialize a new training campaign, generate a matrix of five distinct, grounded hypotheses, and validate the results against frozen benchmarks before committing to a production-ready model.

Quick Start

Initialize a new research campaign by running the autoresearch script with your specific objective and primary metric defined in the command arguments.

Frequently Asked Questions about openui-autoresearch

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

FAQPage Schema
How do I automate machine learning research campaigns and ensure evidence-grounded hypotheses?▼

Automated machine learning research campaigns are orchestrated by enforcing strict architectural contracts and generating evidence-grounded hypotheses. This skill manages the full lifecycle from literature discovery to data synthesis, requiring documented evidence before model promotion.

How do I prevent unverified model training during machine learning experimentation?▼

Unverified model training is prevented through strict architectural contract enforcement and automated meta-gate benchmarks. The workflow requires documented, peer-reviewed evidence and RL readiness validation before any model is promoted to production.

Can I validate experiment results against frozen benchmarks before committing to a production model?▼

Yes, experiment results are validated against frozen benchmarks during the RL readiness validation phase. This ensures research integrity and reproducibility before a model is promoted to a production-ready state.

How do I generate a matrix of distinct hypotheses for a new machine learning training campaign?▼

A matrix of five distinct, grounded hypotheses is generated automatically when you initialize a new research campaign. The process uses literature discovery and evidence-grounded hypothesis generation to ensure research validity.

What is the best way to manage multi-stage machine learning experimentation workflows?▼

Multi-stage experimentation workflows are managed by orchestrating the full lifecycle of experiment selection, data synthesis, and validation. This approach uses predefined lineage harnesses to ensure research integrity and reproducibility.

Do I need predefined lineage harnesses to run autonomous machine learning research?▼

Yes, predefined lineage harnesses are required to ensure research integrity and reproducibility. The autonomous research process mandates adherence to these harnesses and automated meta-gate benchmarks to validate experiments.

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