lq-skills

EU AI Act Quick Assessment — fast 15-25 minute triage for preliminary classification and compliance assessment. This skill should be used when the user asks to "do a quick AI Act assessment", "check if the AI Act applies to us", "run a preliminary classification", "do an AI Act triage", "quick check", "preliminary assessment", "Schnellprüfung", "Ersteinschätzung", or needs a fast initial assessment before committing to full analysis.

Use this skill when the user needs to review, draft, or redline a Data Processing Agreement (DPA / Auftragsverarbeitungsvertrag / AVV) under Art. 28 GDPR, or to prepare a Joint Controller Arrangement under Art. 26 GDPR. Triggers include "DPA", "AVV", "Auftragsverarbeitung", "Auftragsverarbeitungsvertrag", "Art. 28 contract", "data processing agreement", "processor agreement", "Art. 26 arrangement", "joint controller agreement", "JCA", "review this DPA", "draft a DPA", "redline this DPA", or any request involving controller-processor / joint-controller contracting. Supports bilingual output (DE/EN), both controller- and processor-side perspectives, and both quick (Art. 28(3)(a)–(h) coverage) and negotiation-grade (clause-by-clause risk scoring) review depths.

NIS2 Compliance Navigator — scope classification, Art. 21 gap analysis (0-4 maturity scoring), and compliance roadmap under EU Directive 2022/2555 with deep German BSIG-neu coverage and profiles for Italy, France, Netherlands, Austria, Spain. Use when: (1) User mentions "NIS2", "NIS-2", "BSIG", "BSIG-neu", "NIS2UmsuCG", "Cyberbeveiligingswet", "Loi Résilience", "NISG", "decreto legislativo 138", (2) User asks if their organization falls under NIS2 or needs a cybersecurity compliance assessment, (3) User mentions essential/important entities, Annex I/II, BSI registration, § 30 BSIG, incident reporting, management body liability, supply chain security, (4) User wants a NIS2 gap analysis, readiness assessment, or compliance roadmap, (5) User asks about NIS2 fines, enforcement, or Nachweispflicht, (6) User asks about NIS2 in any EU Member State.

Draft GDPR/DSGVO-compliant privacy notices as .docx for any EU/EEA jurisdiction and audience. Use when user asks to create a privacy policy/notice, mentions "Datenschutzerklärung", "politique de confidentialité", "privacy notice", needs Art. 13/14 disclosures, AI Act transparency, cookie policy, or notices for applicants ("Bewerber-Datenschutz"), employees ("Beschäftigten-Datenschutz"), B2B partners, or B2C customers. Covers DE (DSGVO+BDSG+TDDDG), FR (RGPD+LIL+LCEN), AT, IT, ES, NL, BE, IE, UK GDPR. Five notice types: Website/App, Applicant, Employee, Business Partner, B2C Customer.

Use whenever the user is working on document production in international arbitration: drafting requests to produce, raising or replying to objections, or preparing the schedule for the tribunal to rule on. Builds and maintains the request-to-produce table (the Redfern Schedule) for the requesting party, the producing party, or the tribunal. Applies the IBA Rules (2020) Article 3.3 admissibility checklist and the Article 9.2 objection grounds, raises a content-based political and institutional sensitivity prompt for State or state-owned parties, and writes an internal memo flagging the user's own weak requests. Trigger it even when the user does not say Redfern Schedule but mentions requests to produce, document production in an arbitration, IBA objections, Article 3.3 or 9.2, or a tribunal ruling on production. It enforces form and does not decide materiality or whether an objection will succeed.

Apply the NIST AI Risk Management Framework (NIST AI 100-1 + the NIST AI 600-1 Generative AI Profile) to a specific AI system, governance question, or impact assessment. Three modes — consult, governance plan, full assessment — all cite Subcategories (`GOVERN 1.1`) and Profile Action IDs (`GV-1.2-001`) verbatim. Use when the user mentions the AI RMF, NIST RMF, NIST AI 100-1, NIST AI 600-1, GenAI Profile, the four functions (Govern / Map / Measure / Manage), the trustworthy AI characteristics, the 12 GAI risks (confabulation, harmful bias, information integrity, CBRN, data privacy, etc.), or asks "what does NIST say about X" for an AI system.

Template analyzer for CoQuill (v2). Parses docx/HTML templates, extracts variables including conditionals and loops, merges config.yaml overrides, infers types, and generates a v2 manifest.yaml. Called by the coquill orchestrator — not triggered directly by the user.

Document renderer for CoQuill. Takes a template, variable values, and produces rendered documents (docx or html+pdf). Validates output for unfilled placeholders. Called by the coquill orchestrator — not triggered directly by the user.

Document assembly tool. Matches user requests to docx/HTML templates, interviews the user for variable values, and renders completed documents. Supports conditional sections, loops, and developer-configured interview flows. Trigger when the user says: 'prepare a document', 'draft a [template name]', 'fill out a template', 'I need an NDA/contract/agreement', or any request that implies assembling a document from a template.

Transcript generator for CoQuill. Reads an interview_log.json and manifest.yaml, then writes a human-readable transcript.md to the job folder. Called by the coquill orchestrator — not triggered directly by the user.

Use when the user provides a client alert, regulatory bulletin, law firm memo, or similar legal update and wants the time-sensitive action items, deadlines, and obligations extracted into a checklist organized by deadline. Distinguishes mandatory action items (effective dates of new requirements) from recommended action items (best practices) and informational items (context only). Produces a brief context summary plus deadline-organized checklist with citations to the source alert.

Classifies the treatment of Competition Compliance Programmes (CCPs) in competition law enforcement documents. Converts PDF input, detects language, analyzes the full document, produces a scratchpad, and populates Output.xlsx. Use when classifying how a CCP is treated as an offence, defence, remedy, or irrelevant in a policy document or case/judgment.

Use when the user has a piece of legal-jargon-heavy text and wants it rewritten in plain language for a specified non-legal audience. Common scenarios — email to a business stakeholder, legal disclaimer for a customer-facing page, memo for a non-legal audience, explanation of a contract clause for a deal team, summary of a regulatory development for an executive briefing. Produces the rewritten text plus a brief explanation of what was changed and why, including any preservation-of-meaning concerns where the rewrite required interpretation. Does not draft from scratch; the input is text that exists.

Use when the user has a contract loaded and asks a specific question about it — what a clause means, where something is addressed, whether a term is unusual, how a provision compares to standard practice, or what would happen in a specified scenario under the contract. Produces an answer calibrated to the question's shape (direct answer for fact lookups, paragraph with context for "is this unusual" questions, structured findings for multi-issue questions), with verbatim citations to specific clauses. Does not perform full reviews — for that, use the appropriate review skill (NDA Review, DPA Checklist Review, MSA Review, etc.).

Use when the user provides a Data Processing Agreement, Data Processing Addendum, or HIPAA Business Associate Agreement and asks whether it contains the terms required under the applicable data-protection regime. Produces a structured checklist scoring each required term as present, partial, missing, or unclear, with clause references and recommended language for any gaps. Supports GDPR Article 28, US state privacy laws (CCPA/CPRA, VCDPA, CPA, CTDPA, and similar), HIPAA BAAs, and general commercial DPAs without a specified regime.

Use when the user has typed a short or vague prompt and the system is configured to expand prompts before submission, or when the user explicitly invokes "Enhance Prompt" or asks the system to "improve this prompt before sending." Rewrites the user's input into a structured legal prompt with role, jurisdiction, task, constraints, and output format made explicit, and returns the expansion alongside a brief reasoning section so the user can review, edit, or skip before the expanded prompt is submitted to the model.

Structured workflow for researching foreign law questions across Chinese, English, and local-language resources. Guides users through a tiered research approach: Chinese secondary sources, English legal guides (free and paid), law firm publications, AI-assisted research, and local-language materials. Helps identify the best resources for a specific jurisdiction and legal topic, prioritizes free over paid sources, and enforces cross-validation and timeliness checks. Use this skill whenever the user asks about foreign law, cross-border legal issues, comparative law research, jurisdiction-specific legal questions, "how does [country] regulate X", overseas investment legal requirements, or any legal research involving non-domestic jurisdictions. Also trigger when the user mentions terms like "外国法", "域外法", "国别法律", "跨境法律", "海外法律研究", "doing business in [country]", or asks about legal frameworks in specific foreign countries.

Use when reviewing board-level governance documents — Delegation of Authority policies, charters, board resolutions, related party transaction policies, or committee terms of reference. Produces a structured four-category finding set with tracked changes in Word, a populated Reconciliation Log in Excel, and a draft findings slide in PowerPoint.

Use when benchmarking a board-level governance document against the LQ Governance Playbook — a Delegation of Authority policy, committee charter, related party transaction framework, or board terms of reference. Produces a classification table (Match / Partial Match / Below Fallback / Red Flag / Omitted) with specific gaps and tracked changes.

Use when reviewing one-way (unilateral) commercial NDAs, analyzing key clauses for risk, producing clause-by-clause issue logs with preferred redlines, fallbacks, and negotiation guidance.

Use when the user wants to create a new LQ.AI skill, turn a chat into a reusable skill, improve an existing skill, or asks "how do I build a skill that does X." Conducts a focused conversation to elicit what the skill should do, when it should trigger, what inputs and outputs it needs, and what edge cases matter, then produces a complete skill folder ready to save.

California property tax research workflow using BOE Property Tax Rules (especially 462.* change in ownership, including 462.180 legal entities) and BOE published Property Tax Annotations (PTLG, commonly 220.*). Use when the user needs rule/annotation identification, synthesis, and application to a factual scenario involving change in ownership, trusts, or legal entities.

Use when running structured, adversarial analysis across large case-file directories — extracts facts, claims, and legal views into XML metadata via a stateless R.A.L.P.H. loop, then synthesizes contradictions and timeline across all files. Proof of concept; not a finished product.

First-pass framework for reading, interpreting, and structuring statutory analysis of US federal, state, and local law. Produces draft analysis for attorney review — not legal advice. Use this skill whenever the user references a specific US statute, regulation, ordinance, or rule by citation, asks "what does [statute X] require," asks for compliance scoping, applicability thresholds, requirement extraction, exemption analysis, definitional analysis, federal preemption analysis, or multi-state comparison — even if they don't explicitly ask for "statutory analysis." Halts and asks for missing inputs rather than guessing. Out of scope for non-US law.

Expert-level legal document translation — understands law, not just language. Use whenever a user wants to translate any legal document or legal text between any languages. Triggers: "translate this contract/agreement/order/affidavit/MoU/lease/ notice/petition", any legal document upload with a target language, or any request to make legal content readable in another language. Also trigger for transliteration of legal names/entities, bilingual legal document creation, and legal terminology lookups. Covers all major language pairs — contracts, MoUs, affidavits, court orders, power of attorney, wills, leases, legal notices, petitions, appeals, corporate filings, immigration docs, IP agreements, and employment contracts. Use when both law and language are involved. See "Not in Scope" before proceeding.

Adversarial quality control for AI deliverables. Run structured parallel verification using one or two models before delivering reports, plans, analyses, scripts, or any substantive work. Use when: (1) reviewing a deliverable before sending to a human, (2) user asks to 'QC this', 'review this', 'check this', 'verify this', (3) user wants a quality gate on AI output, (4) validating factual claims, numbers, logic, or completeness. Configurable: single-model (two agents, same model) or cross-model (two different providers). Outputs a QC certificate with pass/fail/review items and evidence.

Use when checking statutory citations in Singapore legal documents, verifying references against Singapore statutes, or auditing Word documents for citation accuracy.

HTS classification, CROSS ruling research, CIT/CAFC case mapping for US trade law. Analyzes products, finds applicable rulings, and traces legal precedent.

Use when auditing open-source dependency licenses in Python projects, generating compliance reports, or checking license risk in your codebase.

Use when you need to generate tracked changes in Word documents from diff output, apply redlines from AI agents to DOCX files, or convert text diffs into native Word revisions.

Use when you need to batch redline multiple contracts against a negotiation playbook, apply tracked changes to Word documents programmatically, or run contract review workflows with AI assistance.

Use when you need to apply word-level tracked changes to Microsoft Word documents programmatically, preserve formatting through diffs, or integrate with Office.js for document transformation.

Use when verifying Singapore court citations in legal submissions, checking for hallucinated cases in AI-generated text, or validating citations against eLitigation.

Use when you need to apply tracked changes and comments to DOCX files programmatically, merge multi-agent edits with conflict resolution, or produce native Word revisions from AI agent output.

Use when you need to identify the likely source of a text passage, attribute text to documents in a RAG system, detect plagiarism, or match contract clauses to their origin.

Use when users say "build a chronology", "make a timeline", "what happened when", "chronology from disclosure", "source the key events", or need legal documents, correspondence, pleadings, witness evidence, or disclosure materials turned into a sourced event chronology.

Use when users say "collate comments", "combine reviewer markups", "compile tracked changes", "make a resolution checklist", or have multiple DOCX drafts, Word comments, redlines, partner/client markups, or external feedback to review without auto-merging.

Use when users say "Companies House search", "investigate this UK company", "check officers/PSCs/charges", "registry snapshot", "group structure", "filing history", or need UK company profile, filings, risk leads, and registry evidence reviewed without unsupported allegations.

Use when users say "model claim economics", "litigation funding waterfall", "portfolio economics", "funder MOIC", "DBA/CFA economics", "Monte Carlo", "ATE/adverse costs", or need legal claim recoveries, fee structures, recourse, and settlement distributions modelled.

Use when users say "is this legal AI app local-first", "what leaves the machine", "BYOK privacy", "audit network calls", "where are documents stored", or need a legal AI workspace reviewed for local storage, credentials, model-provider calls, conversion, and privacy boundaries.