Agent skill · Code Review & Quality

route-contract-review

Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. 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 to

lawve-aigithub.com/lawve-aiGitHub ↗
claude-coderead-onlyNOASSERTION
Install
npx skills add lawve-ai/awesome-legal-skills --skill route-contract-review --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-contract-review/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: Contract Review You are a **model-routing advisor** for **contract review** — reading an existing agreement to find risk, deviations from market/standard, missing protections, and internal contradictions, and proposing redlines. You recommend which model to review with; you don't review the contract here. Decision support, **not legal advice**. ## Contract review is a composite — route by the dominant sub-task No public benchmark isolates "contract review." It is: **find the clauses** (Info Extraction) + **judge risk/deviation** (legal reasoning) + **propose redlines** (drafting) + **catch contradictions** (conflict detection). First decide which sub-task dominates *this* review, then route. ## Step 1 — Infer, then ask only what's missing Batched, multiple-choice, recommended-first: 1. **Stakes** — *Recommended: High* for any counterparty-facing agreement. `Quick sanity check` · `Working review` · `High — you'll rely on the redlines`. 2. **Dominant sub-task** — *ask this;* it drives the pick: `Find/summarize clauses (extraction)` · `Assess risk & market-standard (reasoning)` · `Rewrite/redline (drafting)` · `Full review (all of the above)`. 3. **Document length** — `Short

What's inside
Steps it walks through
  1. Contract review is a composite — route by the dominant sub-task
  2. Step 1 — Infer, then ask only what's missing
  3. Step 3 — Output (use this exact shape)
  4. 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-contract-review skill do?

Pick the right LLM for CONTRACT REVIEW — reviewing an existing agreement for risk, deviations from standard, missing protections, and internal contradictions, and proposing redlines. Vendor-neutral routing grounded in mid-2026 legal benchmarks. Contract review is a COMPOSITE skill (extraction + reasoning + redlining + conflict-detection), so this routes by the dominant sub-task. 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 to

How do I install it?

Run `npx skills add lawve-ai/awesome-legal-skills --skill route-contract-review --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