bluesky-data-collection

Bluesky experimental orchestration skill for scan automation, data collection, and metadata management

509 stars

Best use case

bluesky-data-collection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Bluesky experimental orchestration skill for scan automation, data collection, and metadata management

Teams using bluesky-data-collection should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/bluesky-data-collection/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/physics/skills/bluesky-data-collection/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/bluesky-data-collection/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How bluesky-data-collection Compares

Feature / Agentbluesky-data-collectionStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Bluesky experimental orchestration skill for scan automation, data collection, and metadata management

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Bluesky Data Collection

## Purpose

Provides expert guidance on Bluesky for experimental orchestration, including scan automation and data management.

## Capabilities

- Scan plan execution
- Adaptive scanning
- Databroker integration
- Live visualization
- Hardware abstraction (ophyd)
- Jupyter integration

## Usage Guidelines

1. **Scan Plans**: Use built-in or custom scan plans
2. **Adaptive Scans**: Implement adaptive scanning strategies
3. **Data Storage**: Configure databroker for data management
4. **Visualization**: Set up live plotting during scans
5. **Hardware**: Define devices with ophyd

## Tools/Libraries

- Bluesky
- ophyd
- databroker

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