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knowledge-discovery

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

Skill by beita6969

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.

npx agentmarket install skill/beita6969-knowledge-discovery

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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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Permissions

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

Tools
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Secrets
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

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.

More from beita6969/ScienceClaw

Signals

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
GitHubgithub-graphql

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