Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add beita6969/ScienceClaw --skill information-extractiongit clone --depth 1 https://github.com/beita6969/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/beita6969/scienceclaw/information-extraction)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/information-extraction"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/information-extraction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/beita6969/scienceclaw/information-extraction"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/information-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00426 |
| Opus 5 | $0.00009 | $0.00213 |
| Sonnet 5 | $0.00003 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
information-extraction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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
- Schema definition — Define target entity types, relation types, and attributes
- Preprocessing — Sentence segmentation, tokenization, abbreviation expansion
- Entity recognition — Identify and classify named entities (NER)
- Relation extraction — Detect relationships between entity pairs (RE)
- Normalization — Map entities to standard ontologies (MeSH, ChEBI, UniProt)
- 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 37 lines · 17 tokens per session scan A c2c85ff1e1b6
information-extraction is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 426 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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