data-science-autoresearch

Design autonomous AI research systems that prepare data, train, evaluate, and log ML results.

Updated Apr 6, 2026

Search Tags:#automation #monitoring #provenance #reproducibility #data-pipeline #experiment-design #autoresearch

Ready to run

npx skills add https://github.com/scanady/autoresearch-lapsation --skill data-science-autoresearch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-science-autoresearch
Source: https://github.com/scanady/autoresearch-lapsation/tree/main/.agents/skills/data-science-autoresearch
Command: npx skills add https://github.com/scanady/autoresearch-lapsation --skill data-science-autoresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs end-to-end autonomous AI research systems that iteratively improve ML models by automatically preparing data, training, evaluating, and logging results.

Core Features & Use Cases

End-to-end autoresearch framework: fixed data preparation, training harness, evaluation harness, agent program, and a blessed runner to enforce single-run discipline.
Reproducible experiments: provenance stamping, environment manifest, and data lineage for auditable research automation.
Rapid iteration: deterministic evaluation, simple baseline to improve upon, and automated logging of results and decisions across cycles.

Quick Start

Provide a problem statement and data characteristics; the system will generate and run a baseline autonomous autoresearch loop.

Frequently Asked Questions about data-science-autoresearch

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

FAQPage Schema

How do I set up an autonomous AI research loop for ML models?▼

To set up an autonomous AI research loop, provide a problem statement and data characteristics to generate a baseline loop that automatically prepares data, trains, evaluates, and logs ML model results.

What is automated experiment provenance in machine learning pipelines?▼

Automated experiment provenance in ML pipelines tracks data lineage, stamps runs, and records environment manifests. This ensures reproducible experiments and auditable research automation across training and evaluation cycles.

How do I ensure reproducibility in automated ML training and evaluation?▼

Reproducibility in automated ML training requires deterministic evaluation and a blessed runner to enforce a single experiment protocol. Comprehensive provenance stamping and environment manifests guarantee auditable research automation.

Does this autonomous research framework require fixed data readiness protocols?▼

Yes, this autonomous research framework enforces fixed data readiness protocols and deterministic evaluation. A blessed runner enforces single-run discipline to ensure a standardized experiment protocol across automated cycles.

What is the best way to automate ML model iteration and data pipeline preparation?▼

The best way to automate ML model iteration is designing an end-to-end system that automatically prepares data pipelines, trains models, evaluates deterministically, and logs decisions. This enables rapid, reproducible experimentation.