# Scientific Active Learning

> Active learning skill. Uncertainty sampling, Query-by-Committee, expected model change, pool-based/stream-based, batch active learning, GP-based active learning with ARD-RBF kernel, dimension-adaptive convergence, stopping criteria, and model improvement pipeline.

- Skill: `nahisaho/scientific-active-learning` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add nahisaho/scientific-active-learning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-active-learning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra, Coding & Dev Tools
- Author: nahisaho (https://skillmd.com/u/nahisaho)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nahisaho/scientific-active-learning

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# Active learning

Active learning skill. Uncertainty sampling, Query-by-Committee, expected model change, pool-based/stream-based, batch active learning, GP-based active learning with ARD-RBF kernel, dimension-adaptive convergence, stopping criteria, and model improvement pipeline.

## Use This Skill When

- Uncertainty sampling.
- Query-by-Committee.
- Expected model change.
- Pool-based/stream-based.
- Batch active learning.

## Required Inputs

- Research objective, decision target, or hypothesis.
- Available data, source constraints, and domain assumptions.
- Required outputs, success metrics, and deadline or reproducibility constraints.

## Workflow

1. Confirm scope, assumptions, and the exact artifact set to save.
2. Apply the narrowest domain method that answers the request with defensible evidence.
3. Save code, tables, figures, and intermediate outputs to files instead of chat-only output.
4. State limitations, uncertainty, and any validation or sensitivity checks performed.
5. Append skill selection, handoff I/O, and file writes to `logs/process-log.jsonl`.

## Deliverables

- `report.md`: concise method, results, interpretation, and file inventory in the user's language.
- `results/`: structured outputs, metrics, model artifacts, or extracted findings.
- `figures/`: English-only charts, diagrams, or panels when visual output is needed.
- `data/`: processed or derived datasets when transformation occurs.

## Quality Gates

- The selected method matches the scientific question and stated assumptions.
- Outputs are reproducible, saved to files, and traceable from inputs to conclusions.
- Missing data, uncertainty, bias, and hard limits are made explicit.
- `report.md` and `logs/process-log.jsonl` reference the generated artifacts.