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A zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.

Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates legal, financial, operational, regulatory, customer, employee, patient, citizen, market, contractual, or reputational consequence.

Creates an Agent Authority Charter for enterprise or regulated AI agents before deployment. Use this Skill when a user needs to define what an AI agent is allowed to do, who delegated authority to it, what actions are permitted or prohibited, when human approval is required, what evidence must be preserved, and how the agent can be suspended, revoked, or escalated.

Analyze any OFAC or OFSI enforcement action — by URL, pasted text, or uploaded document — and produce a structured root cause analysis as a formatted Excel (.xlsx) spreadsheet. Use this skill whenever a user names, links to, pastes, or uploads an OFAC or OFSI enforcement action and asks for any of the following: root cause analysis, compliance gaps, what went wrong, lessons learned, organizational self-assessment, or remediation planning. Also trigger when a user asks "analyze this enforcement action", "what were the root causes", "turn this into a checklist", or "how do I make sure this doesn't happen to us". Outputs a single-sheet .xlsx table with six columns: Root Cause | What Went Wrong | How It Went Wrong | What Could Have Stopped It | Is my organization immune to this? (Yes/No/Partial) | Notes.

Run an attorney-facing knockout/availability trademark search across the United States and Canada. Use whenever a user (founder, client, or attorney) asks whether a brand, mark, name, logo, slogan, or product name is "available," "free to use," "clearable," or "already taken" — or asks Claude to "search the trademark register," "check USPTO," "check CIPO," "do a knockout search," "clearance search," "availability search," or "see if [mark] is trademarked." Trigger even when the user phrases it casually ("can I call my company X?", "is this name taken?"). Covers federal USPTO (TESS/TSDR), federal Canada (CIPO), common-law/web/state, and WIPO Madrid international. Output is a knockout availability memo with green/yellow/red verdict for attorney review. Includes professional-responsibility gating (AI-use disclosure, conflict check, scope confirmation) before substantive analysis. Not legal advice — produces work product for attorney sign-off.

Adversarial premortem for England & Wales civil litigation - builds the strongest version of a case, then attacks it from four angles to find where it loses before opposing counsel does. Runs an adversarial premortem on a UK litigation matter. Builds the strongest version of the case, then attacks it from four angles — procedural, substantive, evidentiary, strategic — to find where it actually loses. Returns a ranked stress-test brief: failure scenarios, evidence inconsistencies, blind spots, mitigations, and one brutal one-sentence verdict. Use before issue, before settlement negotiations, before a litigation-funder pitch, or before deciding whether to take a case.

Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening systems.

This skill helps you apply the NIST AI Risk Management Framework and can quote AI RMF subcategories and GenAI profile action IDs verbatim from NIST AI 100-1 and 600-1. The skill routes you to the correct category as needed and has three modes, picked automatically from your question: 1. Consult: “What should I do per the AI RMF for this system?” 2. Governance Plan: “What should our AI governance program include?” 3. Assessment: “Run a full AI RMF aligned impact assessment for this system.” It knows the difference between the Core and the GenAI overlay, and won’t dump profile actions on a classifier or recommender.

Claude skill that turns Claude into a Connecticut-specific divorce planner — nine operating modes from pre-flight intake to post-judgment modification, modeled on Untangle.us's feature surface and grounded in C.G.S. Title 46b, Practice Book Chapter 25, and the 2026-08-01 CCSG schedule. Covers eligibility triage (nonadversarial under § 46b-44a vs standard), financial affidavit (JD-FM-6), child support (JD-FM-220 / CCSG-1 / 1A), alimony (§ 46b-82 fourteen factors), parenting plan (with GAL/AMC escalation under JD-FM-224), settlement agreement (JD-FM-172, TCJA-aware), filing packet (marshal 12-day rule, $360 + $50 fees, JD-FM-75 waiver), and post-judgment motion practice. Hard UPL gate: refuses non-CT, domestic violence (refers CTCADV 1-888-774-2900), hidden assets, courtroom advocacy. Heppner-aware: AI prompts are not privileged. First family-law skill in the Lawvable registry.

U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special + Chapter 99 + AD/CVD + MPF/HMF), country of origin determination, FTA qualification, and end-to-end import compliance review. Triggers on product classification, tariff questions, customs rulings, trade remedy screening (Section 301/232/201), Partner Government Agency admissibility, and UFLPA forced-labor analysis. Produces attorney-reviewable draft work product with an enforced HTSUS authority hierarchy and explicit evidence ledger.

This Skill creates a research brief for an area of US employment law. The brief can address a specific question or be a multi-state survey. It'll include citations to primary sources (e.g. statutes, rule, government guidance), and reputable secondary sources like client alerts and multi-state surveys from top law firms and articles from legal publications (e.g. National Law Review).

Audits a legal document against legal design principles. Scores the document on six pillars: language patterns (bureaucratese, archaisms, passive voice, nominalization), readability (Flesch Reading Ease and language-specific equivalents), structure and navigation, hidden conditions (material obligations buried away from their headings), statutory duplication (restating the law instead of citing it), and visual hierarchy where layout is available. Assessment-only; does not rewrite the document. Works on English, French, and German documents, inferring the applicable jurisdiction from the document's language and textual signals: governing-law clause, statute citations, party addresses. Use when a user shares a legal document (contract, NDA, T&C, privacy policy, internal policy) asking how good the drafting is from a legal design perspective, whether it is readable, whether it contains buried clauses or restates the law, or how it scores on Flesch / LIX / Amstad / Kandel-Moles readability metrics.

A skill for anyone needing legal documents translated into publication-ready English. Hand it a Word file in any language; get back an English .docx with formatting preserved, delivering notably higher quality than e.g. Legora’s DeepL tool. Unique features: * Skill has built-in English general lexicons and per-language sub-lexicons (phrasebooks) covering M&A, IP, IT/SaaS, finance, tax, litigation, employment and more. Full sub-lexicon coverage for 11 languages including Spanish, German, French and Chinese; others translate really well too. * Track changes read correctly when accepted/rejected, and typos are collapsed. * Definitions reordered alphabetically. * Headers, footers, footnotes and comments translated. * Translates to US English by default, UK English on request. * Quality gates throughout. * Takes minutes rather than seconds and is larger than simpler skills, but overall quality is higher. UK English default version here: https://lawve.ai/en/skills/legal-translation-uk-english-wouter-van-den-berg

A plugin for calendaring litigation and arbitration deadlines from scheduling orders. Scans scheduling orders and outputs in a calendar file for Outlook or Google Calendar. Also permits inviting other team members via calendar invites.

Drafts a customized SaaS Customer Agreement starting from the Y Combinator standard form SaaS template. Runs a structured intake covering fee structure, data handling, ML/AI rights, implementation services, SLA, and governing law, then applies 18 always-on professional defaults plus 12 conditional decisions. Outputs a clean .docx ready for review alongside a lawyer-facing memo that itemizes every change from the YC baseline and flags each item still requiring attorney input. Aimed at startup founders preparing first-draft customer subscription agreements.

Tribunal-prep skill modeled on Neal Katyal's Nov 2025 SCOTUS tariffs argument and his AI sparring partner "Harvey." Five phases: (1) profile each decision-maker from prior opinions, questions, concurrences, and dissents — surface their doctrinal commitments and institutional concerns; (2) predict the bench — 3–7 likely questions per judge with the worry behind each, verbatim phrasing where confidence is high; (3) map escape routes — the narrowest holding each skeptical judge could sign without abandoning prior commitments; (4) spar adversarially — relentless follow-ups, calls out parroted lines, tracks which questions broke the lawyer; (5) hand the lawyer back to themselves with a < 150-word index card for the podium. The model is the sparring partner. The lawyer still wins the case. Use for oral argument, motion hearings, depositions, arbitrations, or stress-testing a brief before filing.

Pre-flight privilege and work-product check for legal AI prompts. Use when the user is about to send legal content to a third-party AI surface and wants to know whether the prompt risks blowing attorney-client privilege or work-product protection. Returns a SAFE/CAUTION/STOP band with cited factors, a discovery-impact line, and a redacted-safe rewrite.

Generate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list update test data, screening regression test data, screening QA, fuzzy match calibration, or wants to verify their screening lists are current. Trigger even if the user doesn't say 'screening' explicitly — phrases like 'test my sanctions list', 'check our SDN coverage', 'is my list up to date', or 'build me a regression set from the latest designations' should also invoke this skill.

Conduct AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk reviews of internal AI use cases, AI product features, LLM workflows, or third-party AI vendors. The skill runs a structured intake process, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a governance review with scorecards, findings, owners, remediation actions, and follow-up questions.

NIL (Name, Image, and Likeness) contract analysis for NCAA student-athletes from the athlete's perspective. Use when user says 'review this NIL contract', 'analyze this NIL deal', 'check this athlete agreement', 'review my NIL agreement', or uploads a PDF NIL contract for review. Identifies red flags, missing protections, and compliance issues. Produces a structured review memorandum with negotiation positions. Do NOT use for general contract review, employment agreements, non-NIL endorsements, or brand-side deal analysis.

Guide for reading, interpreting, and applying statutes, regulations, and rules in legal and compliance contexts. Use when the user asks about (1) how to read and interpret statutes, regulations, or rules, (2) statutory interpretation methods and canons of construction, (3) understanding legislative intent, (4) applying statutes to specific legal situations, (5) extracting requirements from legal text, (6) distinguishing between different types of legal requirements, or (7) cross-jurisdictional compliance analysis.