scientific-classification

Classify scientific objects, detect patterns, and categorize data across astronomy, biology, and social sciences

564 stars

Best use case

scientific-classification is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Classify scientific objects, detect patterns, and categorize data across astronomy, biology, and social sciences

Teams using scientific-classification 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/scientific-classification/SKILL.md --create-dirs "https://raw.githubusercontent.com/beita6969/ScienceClaw/main/skills/scientific-classification/SKILL.md"

Manual Installation

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

How scientific-classification Compares

Feature / Agentscientific-classificationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Classify scientific objects, detect patterns, and categorize data across astronomy, biology, and social sciences

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

# Scientific Classification & Detection

## Purpose
Classify scientific objects and detect patterns using established taxonomies and classification schemes.

## Key Datasets
- **SDSS Stellar Classification** (Allanatrix/Astro): 100K objects from SDSS DR17 — Stars, Galaxies, Quasars with photometric features (u, g, r, i, z magnitudes, redshift)
- **Social Bias Frames** (allenai/social_bias_frames): Allen AI SBIC corpus for detecting implicit social biases in text

## Protocol
1. **Feature extraction** — Identify relevant features for classification task
2. **Taxonomy mapping** — Map to standard classification scheme
3. **Classification** — Apply appropriate classifier with confidence scores
4. **Validation** — Cross-validate against known labeled examples
5. **Edge case analysis** — Flag ambiguous or borderline cases

## Classification Domains
- **Astronomical objects**: Stellar spectral types (OBAFGKM), galaxy morphology (Hubble), AGN types
- **Biological taxonomy**: Species classification, protein families, cell types
- **Chemical compounds**: Functional groups, drug classes, toxicity levels
- **Text classification**: Sentiment, bias detection, topic classification
- **Image classification**: Histopathology, satellite imagery, microscopy

## Rules
- Report classification confidence and alternative labels
- Use domain-standard taxonomies (not ad-hoc categories)
- Handle multi-label and hierarchical classification
- Document decision boundaries and feature importance

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