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research-loop

Plan, run, and evaluate AI/ML experiments with pre-registration and multi-lens metrics.

1|Updated Apr 25, 2026
Search Tags:#bootstrap#pre-registration#experimental-design#multi-lens#research-loop#iqm#seed-variance
One-click install
npx skills add https://github.com/amit-timalsina/research-loop --skill research-loop-amit-timalsina
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-loop
Source: https://github.com/amit-timalsina/research-loop/tree/main
Command: npx skills add https://github.com/amit-timalsina/research-loop --skill research-loop-amit-timalsina

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides a structured methodology for planning, executing, and interpreting AI/ML experiments with a focus on scientific rigor, ensuring hypotheses are literature-grounded, pre-registered, and evaluated across multiple lenses before any production decision.

Core Features & Use Cases

  • Enforces literature-informed hypothesis framing, preregistration, and a multi-lens evaluation panel (IQM + bootstrap CIs) to avoid overreliance on a single metric.
  • Supports a two-tier workflow (exploratory vs confirmatory) to preserve velocity while delivering robust, reproducible results across seeds, surfaces, and configurations.
  • Provides templates (assets) and references to standard protocols (pre-registration, eval rigor, bindings) to accelerate adoption in teams.

Quick Start

Fill out an experiment card before running any compute and follow a fixed seed budget with a multi-lens evaluation panel.

Frequently Asked Questions about research-loop

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

FAQPage Schema
How do I design reproducible AI experiments with pre-registration?▼

Reproducible AI experiments require literature-grounded hypotheses, explicit experiment cards, pre-registration, and documented bindings to ensure consistency across teams and configurations.

What is the best way to evaluate ML models across multiple metrics?▼

Multi-lens evaluation avoids overreliance on a single metric by applying an evaluation panel that uses IQM and bootstrap confidence intervals across at least five seeds.

How do I structure exploratory vs confirmatory testing in machine learning?▼

A two-tier cadence separates exploratory testing to preserve velocity from confirmatory testing, which enforces pre-registration and rigorous evaluation before production decisions.

How many seeds do I need for reliable bootstrap confidence intervals in ML?▼

Reliable bootstrap confidence intervals require a fixed seed budget of at least five seeds, ensuring that IQM calculations and multi-lens evaluations remain statistically robust.

Why does my AI experiment lack reproducibility across different configurations?▼

Reproducibility fails without documented bindings and explicit experiment cards, which bind hypotheses, pre-registration, and seed budgets to specific configurations across teams.

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