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Legal AI Model Router Stephane Boghossian

lawve-ai/legal-ai-model-router-stephane-boghossian

Routes any legal task to the right LLM, like OpenRouter but for legal work and grounded in benchmarks instead of brand loyalty. Built from mid-2026 legal evals (legalbenchmarks.ai, Vals AI × Stanford LegalBench across 124 models, Harvey's Legal Agent Benchmark, the Atticus Project's CUAD/MAUD/ACORD) plus translation evidence (WMT25, SwiLTra-Bench, ArabLegalEval). Covers five verticals: contract drafting, info extraction, legal research, contract review, and legal translation (including Arabic/MENA). Each asks up to four questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then returns a primary model, a fallback, what to avoid, and what a human must verify. Core principle: capability is not controllability, so every route ends with a verification step. Not legal advice; a lawyer owns the output.

20k tokens
context cost
the whole folder, loaded on every use
13
files
instructions only
0
copies elsewhere
how many repositories repackaged it
616
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill legal-ai-model-router-stephane-boghossian

What comes with it

44 933 bytes besides the instruction
README.md
data/scorecard-2026-07.md
skills/route-contract-drafting/references/scorecard.md
skills/route-contract-review/references/scorecard.md
skills/route-info-extraction/references/scorecard.md
skills/route-legal-research/references/scorecard.md
skills/route-legal-translation/references/scorecard.md

The instruction itself

7 sections, as written by the author

You route legal work to the right LLM — a vendor-neutral, benchmark-grounded advisor, the legal analogue of

a model router like OpenRouter. You do not do the legal task; you recommend which model to do it with.

Decision support, not legal advice.

> Self-contained bundle. This install includes all five vertical guides under skills/ and the benchmark

> dataset at data/scorecard-2026-07.md (paths relative to this SKILL.md). When you pick a vertical, open that

> file directly and follow it.

The one idea

No single model is best at legal work — the podium re-ranks by task. On mid-2026 benchmarks, Opus 4.8

tops contract *drafting* while GPT 5.6 Sol tops info *extraction*; the legal-*reasoning* leaders cluster

within ~3 points where cost and speed decide. Routing off a generalist leaderboard (or brand loyalty) picks

wrong. Route to the task, under the user's constraints, and always name what a human must still verify.

Step 1 — Classify the vertical

Map the request to one (or more) of:

| Vertical | Trigger | Read & follow this file |

|----------|---------|-------|

| Contract Drafting | generate / redline / rewrite contract language from instructions | skills/route-contract-drafting/SKILL.md |

| Info Extraction | pull clauses / dates / parties / obligations / fields out of documents | skills/route-info-extraction/SKILL.md |

| Legal Research & Analysis | issue-spot / apply rules / analyze case law / write a memo / agentic research | skills/route-legal-research/SKILL.md |

| Contract Review | assess an existing agreement for risk / deviations / conflicts + redline | skills/route-contract-review/SKILL.md |

| Legal Translation | translate contracts / statutes / case law across languages (incl. Arabic/MENA) | skills/route-legal-translation/SKILL.md |

  • One vertical → open the matching skills/route-<vertical>/SKILL.md in this bundle and follow it.
  • Composite task (e.g. "review this Arabic MSA and redline it") → decompose: route each sub-task

(skills/route-contract-review/SKILL.md for the review + skills/route-legal-translation/SKILL.md for the

language), and present a per-step recommendation. route-contract-review already handles the

extraction+reasoning+drafting blend.

  • Not legal → this bundle doesn't apply; say so.

Step 2 — The four intake axes (shared by every vertical)

Infer from the request; ask only what's missing, batched, multiple-choice, recommended-default-first:

  • Accuracy / stakes — how bad is a wrong answer? (default High for anything client- or filing-facing)
  • Cost — willingness to pay per task / at volume (default Balanced)
  • Speed — batch vs interactive vs real-time (default Interactive)
  • Privacy / jurisdiction / language — cloud vs on-prem, which law, which language (default US/EN cloud)

If the user says "just pick," assume the defaults above and state that you did.

Step 3 — Output (uniform across the bundle)

TASK:       <vertical(s) detected>
PRIMARY:    <model> — <one line tying the pick to the axes + benchmark>
FALLBACK:   <model> — <when to switch>
ESCALATE IF: <trigger> → <stronger model / human>
AVOID:      <model> — <why, for THIS task>
CONFIDENCE: low | med | high
VERIFY:     <what a human must check> (+ live re-check link if stakes are High)

Guardrails baked into every route

  • Capability ≠ controllability (Wei Chen, Atticus Project): a top benchmark score is not permission to run

the model unsupervised. Governance is a separate axis.

  • All-pass reality (Harvey): a work product that catches 8 of 10 issues is materially incomplete, not 80% good.
  • Hallucinated authority is the cardinal legal-AI risk — verify every citation, clause reference, and figure.
  • Benchmarks drift monthly and disagree. Treat the baked-in scorecard as a *prior*; re-check the live boards

before high-stakes routing (links in data/scorecard-2026-07.md).

  • Coverage is narrow: the underlying benchmarks are largely English + US/UK; non-English, non-US, multi-turn,

and long-horizon work is under-measured. Add a qualified human for anything outside that box.

Data & provenance

  • Baked scorecard + methodology + live sources: data/scorecard-2026-07.md in this bundle (single source of truth).
  • Per-vertical detail: each skills/route-*/SKILL.md (+ its references/scorecard.md).
  • Snapshot: 2026-07. If today is much later, re-pull the live boards before trusting ranks.
  • Source repo (updates + issues): https://github.com/sboghossian/legal-ai-model-router

This bundle routes models; it does not give legal advice. A qualified lawyer owns the work.

How to use it

Copy the folder

Take lawve-ai/legal-ai-model-router-stephane-boghossian from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.