Skills

Evaluate completed mediation-competition problems consisting of general information and confidential packets for both sides. Use when asked to validate, audit, score, quality-control, or assess competition readiness; identify factual, numerical, chronological, authority, commercial-logic, or confidentiality defects; test mediation usefulness, BATNAs, settlement corridors, information asymmetry, and opportunities for firmness, cooperation, imagination, and clarification; or produce an author-only validation report. Perform a qualified partial review when packets are missing. Diagnose by default; do not generate a new problem or edit source packets unless repairs are expressly requested. Requires access to all supplied files and a host capable of reading their formats; otherwise disclose the unreviewed material and resulting limits.

Create original, competition-ready commercial mediation problems and confidential packets with iterative validation. Use when a user provides a topic, industry, dispute sketch, or existing general-information packet and wants a public packet plus confidential packets for both sides—or one named side. Build a narrow but workable settlement corridor; verify factual and numerical consistency, information asymmetry, balance, originality, and commercial feasibility; and revise until deterministic and semantic exit criteria pass. Do not use merely to summarize an existing packet or to advise parties in a real dispute. Requires local file access; Python 3 is recommended for bundled deterministic checks, with disclosed manual fallbacks when unavailable.

Professional PDF solution. Create PDFs using HTML+Paged.js (academic papers, reports, documents). Process existing PDFs using Python (read, extract, merge, split, fill forms). Supports KaTeX math formulas, Mermaid diagrams, three-line tables, citations, and other academic elements. Also use this skill when user explicitly requests LaTeX (.tex) or native LaTeX compilation.

Builds compliance checklists for business scenarios involving GDPR, PIPL, or advertising/data laws. Outputs a structured checklist with check items, legal basis, risk levels, and actionable recommendations. Triggered by requests like \"run a compliance check,\" \"GDPR/PIPL compliance,\" \"pre-launch review,\" \"privacy impact assessment (PIA/DPIA),\" or asking if a feature is compliant.

Assess and classify legal risks using a severity-by-likelihood framework with escalation criteria. Use when evaluating contract risk, assessing deal exposure, classifying issues by severity, or determining whether a matter needs senior counsel or outside legal review.

Find and quote statutory and regulatory law with the LibreJustice MCP tools — French codes at any date, EU law, treaties and bilateral accords, foreign codes (59 countries), collective agreements, BOFiP, circulaires. Use whenever the user asks what a text says or said at a given date, which version or which text applies to a situation, needs the exact wording of a provision for a brief, or works with foreign or international law in French litigation, even if they never mention LibreJustice.

Search and cite French case law and statutes with the LibreJustice MCP tools. Use whenever the user asks about French or European court decisions (Cour de cassation, Conseil d'État, cours d'appel, TJ/TA, CNDA, Conseil constitutionnel, CNIL, CEDH, CJUE), needs authorities for a brief, requête, référé or consultation, wants to check how courts rule on an issue, needs counts of decisions on a point, or asks what a code article says at a given date, even if they never mention LibreJustice.

Assess compliance obligations under the EU Data Act (Regulation (EU) 2023/2854) for connected products, IoT devices, data sharing, cloud switching, B2B fairness, B2G data access, dispute resolution, and international data transfers. Covers scope assessment (manufacturer, data holder, data recipient roles), user data access rights, pre-sale transparency, unfair contract terms, public authority data requests, cloud portability, dispute settlement mechanisms, international transfer restrictions, access-by-design obligations, trade secret protection, and cross-regulation mapping with GDPR, AI Act, and CRA. Use when assessing Data Act obligations, designing connected products, drafting data sharing contracts, responding to B2G requests, planning cloud switching capabilities, or evaluating dispute resolution options.

Serious Incident Reporting (SIR) for high-risk AI systems under Article 73 EU AI Act (Regulation (EU) 2024/1689). Assess, qualify, draft, and submit Art. 73 SIR notifications across the four Art. 3(49) harm categories (death or serious health damage, critical infrastructure disruption, breach of fundamental rights, property or environmental damage). Covers Art. 73 immediate, 2/10/15-day reporting windows, market surveillance authority notification, deployer duties under Art. 26(5), and cross-regulation mapping with GDPR Art. 33/34 breach notification, NIS2 Art. 23 incident reporting, DORA Art. 19 ICT incident, and MDR vigilance. Built for in-house counsel and AI risk teams handling AI incidents, building AI incident response playbooks, or managing AI failure, AI bias, or automated decision incidents. DACH routing through German market surveillance (BNetzA, BaFin, BSI, BfArM, KBA). Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and GPAI Code of Practice skills.

Assess and operationalize implementation readiness for high-risk AI systems under the EU AI Act Annex III, including provider and deployer obligations, conformity assessment, post-market monitoring, and EU database registration. Use when users say things like “we classified this as high-risk, what now?”, “build an EU AI Act readiness plan”, “assess our Annex III compliance gaps”, “what do providers/deployers of high-risk AI need to implement?”, “prepare for conformity assessment”, or “create a high-risk AI implementation roadmap.”

Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689), Articles 51-56. Covers GPAI model identification (Art. 3(63)), systemic risk model designation (Art. 3(65), Art. 51, 10^25 FLOP training compute threshold), upstream provider obligations under Art. 53 (transparency, technical documentation, copyright policy, training data summary), additional systemic risk obligations under Art. 55 (model evaluation, adversarial testing, AI Office incident notification, cybersecurity), systemic-risk notification and Commission designation under Art. 52, downstream Art. 25 quasi-provider duties, and Art. 53(2) open-source exemption. Built for in-house counsel and AI governance teams reviewing LLM or foundation model deployments, drafting GPAI compliance plans, or assessing systemic risk thresholds. DACH: BaFin, BSI, BNetzA. Pairs with the EU AI Act System Classifier, High-Risk Readiness, FRIA, and Serious Incident Reporting skills.

Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific high-risk AI deployment. Covers deployer scope gating (public bodies and private entities providing public services), affected group mapping, Charter rights analysis, proportionality, safeguards evaluation, residual risk, DPIA/FRIA interaction, notification under Article 27(3), and DACH-specific considerations. Use when asked about FRIA obligations, Article 27 scope, fundamental rights and AI, or deployer assessment duties.

Classify an AI system under the EU AI Act (Regulation (EU) 2024/1689) and map the compliance obligations that follow. Walks Art. 2 scope exclusions, Art. 3(1) AI system definition (7-criteria test), Art. 5 prohibited practice screening, Annex I and Annex III high-risk assessment with the Art. 6(3) narrow procedural exception, Art. 51-56 GPAI with the 10^25 FLOP systemic risk threshold, and Art. 50 transparency triggers. Built for in-house counsel classifying real systems: medical imaging, medical devices, credit scoring, insurance pricing, HR or CV screening, biometric identification, emotion recognition, deepfake or synthetic content, content moderation, fraud detection, generative AI or foundation model deployments. DACH routing for Betriebsrat consultation, BaFin, BSI, BNetzA, BfDI. Pairs with the EU AI Act High-Risk Implementation Readiness, FRIA, GPAI Code of Practice, and Serious Incident Reporting skills for downstream workflow.

Checks the formal coherence and plausibility of Italian and EU legal citations (statutes, case law) in text produced by Claude or other legal-AI skills. Reports clause-by-clause on what is well-formed, suspicious, or unresolvable against real norms, covering Cassazione, Corte Costituzionale, Consiglio di Stato, TAR, Normattiva, EUR-Lex, Garante Privacy, AGCM, ANAC. Minimal-surface variant: no proprietary backend fetch, allowlist limited to official Italian and EU institutional registries.

Guide for end-to-end California Bar Exam prep — the five essays (13 subjects), the 90-minute Performance Test, and the 200-question MBE (7 subjects). Built by a practicing attorney who used it to compress four months of prep into a week and a half.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Guide for creating effective skills. Use when users want to create a new skill (or update an existing skill) that extends the model's capabilities with specialized knowledge, workflows, or tool integrations.

Toolkit for comprehensive presentation creation, editing, and analysis. Use to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

Toolkit for comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks

Rigorous multi-pass editor for law review articles, student notes, seminar papers and other legal scholarship. Runs six specialized passes — structure, substantive critique, Bluebook citations, grammar and clarity, fact-checking against sources, and a synthesizing memo — and delivers an editorial memo with issues prioritized as critical, substantial or minor and keyed to specific footnotes and paragraphs. Handles articles of any length, including 50,000-word pieces, by chunking on section boundaries. Reads .docx directly. Use when editing, critiquing, workshopping or pre-submission-reviewing academic legal writing. Triggers on "edit my article," "review this draft," "critique my note," "check my Bluebook citations," "is my argument sound," "read my law review piece," "workshop this paper," "pre-submission review." Chunking needs Python 3; fact-checking needs web search; both degrade gracefully without.

Takes a skill — a .skill or .zip archive, a bare SKILL.md, a folder, or just a raw idea — and produces a package ready for public distribution on lawve.ai, plus the exact entries for the submission form. Runs a gate first: skills with no plausible legal use, unsafe code, or licensing bars are declined with reasons, and general-purpose skills that could serve legal work are offered a legal adaptation instead. Then applies the standard compliance transforms — structure, metadata, attribution, dependency ladders, orphaned-file repair, limitations disclosure — validates the result, and delivers a zip. Raw ideas are gated first, built (via skill-creator where available), then adapted. Use for "make this Lawve-compliant," "prep this skill for Lawve," "package for lawve.ai," "is this suitable for Lawve," "get my skill ready to publish," or any request to ready a skill for the Lawve catalogue.

Transforms reading-oriented prose into listening-optimized text for flat, neutral vocal delivery — TTS, podcasts, audiobooks, CLE audio. Carries a legal layer: case citations, section symbols, subsection lettering, Latin terms and footnotes are unspeakable as written, so a brief, opinion, statute, contract or memo needs them expanded, restructured or stripped before it can be listened to — and quoted authority is never rewritten. Use when converting written content for audio, preparing oral argument by ear, producing CLE or client-facing audio, or making a document listenable on a commute. Triggers on "make this listenable," "convert for audio," "optimize for reading aloud," "prepare for TTS," "make an audio version," "podcast script," "read this aloud." Supports English, French, Spanish, Italian, German and Portuguese; outputs plain text, ElevenLabs audio tags, or SSML for Amazon Polly, Google or Azure.

Turn an interpretive-ambiguity audit of a legal text — contract, statute, regulation, or judicial opinion — into a polished deliverable. Produces a multi-page website (default), a single-page site, a self-contained single-file HTML page, a Word document, a PowerPoint deck, or LaTeX source. Use whenever a user has a stress-test result, ambiguity audit, or "where will this be litigated" scenarios and wants to publish, present, or share them. Triggers on "publish the audit," "make a report from this," "turn this into a website / deck / brief / Word doc," "make a Netlify site," "build slides of these ambiguities," "render the scenarios," "package this stress test," "produce a LaTeX version," and similar. Pairs with the ambiguity-stress-test skill (the underlying analysis) but works on any structured or semi-structured ambiguity analysis. Design is opinionated but colors, fonts and brand label are overridable. Most formats need Python and a writable filesystem; single-file HTML works where the host has neither.

Adversarially stress-tests a legal text — a contract, statute, regulation, or judicial opinion — for interpretive ambiguity: it finds the seams where the people governed by it will later disagree about what it means and turns each into a concrete dispute scenario with both sides' arguments, the likely outcome, and a fix. Use it whenever someone wants to pressure-test, red-team, audit, or find weak spots, loopholes, gaps, ambiguities, or drafting problems in a legal document; whenever a drafter wants to tighten a contract, statute, regulation, or opinion before it issues; whenever a litigator wants to mine an opinion or contract for arguments; or whenever someone hands over a legal text and asks where it will be fought over or for issue-spotting. Trigger it for contract review, statutory-ambiguity analysis, judicial-opinion scope analysis, and drafting QA — even if the user never says "stress-test" or "ambiguity."