knowledge-discovery
Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data
Best use case
knowledge-discovery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data
Teams using knowledge-discovery 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/knowledge-discovery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How knowledge-discovery Compares
| Feature / Agent | knowledge-discovery | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Discover patterns, build knowledge graphs, and extract insights from linguistic and historical data
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
# 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
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