# Scientific Pipeline

> Design or refactor a scientific data pipeline with clear folder structure, reproducible environments, orchestration (Snakemake/Make), and diagnostic plots. Use when this capability is needed.

- Skill: `tomevault-io/scientific-pipeline` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/scientific-pipeline`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/scientific-pipeline/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/scientific-pipeline

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## Objectives
- Separate data processing from visualization.
- Make the pipeline deterministic, re-runnable, and inspectable.
- Make it obvious where data came from and whether outputs are stale.

## Checklist
1. **Directory structure**
   - Ensure `data/raw`, `data/processed`, `data/generated`, `src`, `tests`, `scripts`, `reports` exist.
2. **Environment**
   - Ensure dependencies are pinned/recorded (uv/conda/docker).
3. **Orchestration**
   - Prefer Snakemake or Make (or, at minimum, one `scripts/run_pipeline.sh` entrypoint).
   - Define inputs/outputs per step.
4. **Validation**
   - Add fast sanity checks and cheap diagnostics (saved PNGs).
   - Promote critical checks to tests.
5. **Reproducibility**
   - No hard-coded paths.
   - Fixed random seeds where randomness is used.

## Deliverables
- A single documented entrypoint to run the pipeline.
- Tests for at least the core transformation.
- A short note in `docs/DECISIONS.md` for any scientific choice.

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