information-extraction
Extract structured entities, relations, and clauses from scientific and legal documents
Best use case
information-extraction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Extract structured entities, relations, and clauses from scientific and legal documents
Teams using information-extraction 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/information-extraction/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How information-extraction Compares
| Feature / Agent | information-extraction | 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?
Extract structured entities, relations, and clauses from scientific and legal documents
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
# Information Extraction ## Purpose Extract structured information (entities, relations, events, clauses) from unstructured scientific and domain-specific text. ## Key Datasets - **ChemProt** (bigbio/chemprot): Chemical-protein interaction extraction from BioCreative VI; 10 relation types (CPR:3-CPR:9) between chemicals and proteins - **CUAD** (atticus-project/cuad): Contract Understanding Atticus Dataset; 41 clause types from 510 legal contracts (CC-BY licensed) - **JNLPBA**: Biomedical named entity recognition (protein, DNA, RNA, cell line, cell type) - **SciERC**: Scientific entity and relation extraction from AI paper abstracts ## Protocol 1. **Schema definition** — Define target entity types, relation types, and attributes 2. **Preprocessing** — Sentence segmentation, tokenization, abbreviation expansion 3. **Entity recognition** — Identify and classify named entities (NER) 4. **Relation extraction** — Detect relationships between entity pairs (RE) 5. **Normalization** — Map entities to standard ontologies (MeSH, ChEBI, UniProt) 6. **Output structuring** — Format as structured JSON, RDF triples, or knowledge graph ## Extraction Types - **Chemical-protein interactions**: Substrate, inhibitor, agonist, antagonist, activator - **Legal clause extraction**: Termination, IP rights, non-compete, indemnification, limitation of liability - **Gene-disease associations**: Causal, biomarker, therapeutic target - **Drug-drug interactions**: Synergistic, antagonistic, pharmacokinetic ## Rules - Report extraction confidence scores for each entity/relation - Provide span offsets for traceability back to source text - Normalize entities to standard identifiers (CAS, UniProt ID, etc.) - Handle nested entities and overlapping relations - Validate extracted facts against known databases when possible
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