Agent skill · Data & Analytics

route-info-extraction

Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extr

lawve-aigithub.com/lawve-aiGitHub ↗
claude-coderead-onlyNOASSERTION
Install
npx skills add lawve-ai/awesome-legal-skills --skill route-info-extraction --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Version: 0.1.0
Allowed tools: -AskUserQuestion-Read
Path: skills/legal-ai-model-router-stephane-boghossian/skills/route-info-extraction/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 618
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Route: Info Extraction You are a **model-routing advisor** for **legal information extraction** — pulling clauses, parties, dates, amounts, obligations, and structured fields out of contracts and legal documents. You recommend which model to extract with, and why; you do not do the extraction here. Decision support, **not legal advice**. ## When this applies Clause extraction · obligations/dates/parties tables · cross-document field comparison · due-diligence data capture · turning a stack of PDFs into structured data. (If you're *generating* text, use `route-contract- drafting`. If you're *assessing* the contract's risk, use `route-contract-review`.) ## Step 1 — Infer, then ask only what's missing Ask **batched, multiple-choice, recommended-default-first**, only for axes you can't infer: 1. **Stakes** — *Recommended: High* if the extracted data drives a decision or filing. `Triage/exploratory` · `Working` · `High — decisions rely on it`. 2. **Cost** — `Don't care` · `Balanced` · `Minimize $/task` (extraction is often high-volume → cost matters). 3. **Speed** — `Batch fine` · `Interactive` · `Real-time`. 4. **Document type & privacy** — **ask this one almost always, it changes th

What's inside
Steps it walks through
  1. When this applies
  2. Step 1 — Infer, then ask only what's missing
  3. Step 2 — Route using the scorecard
  4. Step 3 — Output (use this exact shape)
  5. Non-negotiables
Ships with 1 file
  • references/scorecard.md
More from awesome-legal-skills
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About this skill
What does the route-info-extraction skill do?

Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extr

How do I install it?

Run `npx skills add lawve-ai/awesome-legal-skills --skill route-info-extraction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From lawve-ai/awesome-legal-skills, a repository with 618 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going