knowledge-discovery

Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data

IndexedMIT★ 1,019

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data

Use cases: data

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Files it touches

~/.claude/skills/knowledge-discovery

What's inside

Skill name: knowledge-discovery
Description: Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data
License: MIT
Instructions: 31 lines in SKILL.md · view on GitHub

Files (1)

SKILL.md

SKILL.md (first lines)

# Knowledge Discovery & Graphs

## Purpose
Discover hidden patterns, build knowledge graphs, and extract novel insights from structured and unstructured data.

## Key Datasets
- **WALS** (wals.info): World Atlas of Language Structures — 192 linguistic features across 2,679 languages in CLDF format (CC-BY 4.0)
- **HistWords** (nlp.stanford.edu/projects/histwords): Historical word embeddings tracking semantic change across 4 languages over centuries (.npy/.pkl format)

## Protocol
1. **Data exploration** — Profile data, identify patterns, check distributions
2. **Feature engineering** — Create derived features, temporal features, cross-references
3. **Pattern detection** — Apply clustering, association rules, anomaly detection
4. **Knowledge graph construction** — Build entity-relation graphs from discovered patterns
5. **Insight generation** — Interpret patterns in domain context
6. **Validation** — Verify discoveries against known phenomena

## Discovery Types
- **Linguistic typology**: Cross-linguistic universals, language family features, areal patterns
- **Semantic change**: Word meaning evolution, neologism tracking, conceptual drift
- **Scientific trends**: Emerging research topics, citation patterns, collaboration networks
- **Biomedical discovery**: Drug repurposing candidates, gene-disease associations

## Rules
- Distinguish between correlation and causation in discovered patterns
- Report statistical significance and effect sizes
- Validate against domain expertise and existing literature
- Handle missing data transparently
- For knowledge graphs, use standard ontologies (RDF, OWL) when possible

From beita6969/ScienceClaw, MIT.

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Stars: 1,019
Forks: 109
Open issues: 1
Last commit (this folder): Mar 12, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License: MIT
Repository: github.com/beita6969/ScienceClaw
Website: scienceclaw.science
First seen: Oct 24, 2026
Last checked: Oct 24, 2026
Source: GitHub github-graphql

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knowledge-discovery skill. Agent Mart. https://agentmart.dev/skills/beita6969-knowledge-discovery (last updated 2026-10-24)