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概要と使いどころ
Detect documentation drift against code changes, score staleness, validate API docs via AST parsing, and audit link integrity. Use when docs fall out of sync with code, preparing releases, running CI doc gates, or auditing doc accuracy.
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Implement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.
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Give feature/pricing cards a premium feel: a soft radial spotlight follows the cursor across the card and lifts its border. Keep the glow subtle and low-contrast. Falls back to a plain static border under prefers-reduced-motion and on touch. Do not use on text-dense reading surfaces.
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Add a celebratory particle burst to a like / favorite / reaction button. A delight micro-interaction that fires on user tap only (never on load), bursts on the "like" transition (not un-like), and ships a prefers-reduced-motion fallback. Use when a small, earned moment of positive feedback will feel good — and only one such moment per screen.
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A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.
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Letters drop into place one after another — a light, gravity-flavored headline entrance. One short playful line; not body copy. Instant under prefers-reduced-motion.
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Give one short headline quiet premium emphasis: a slow on-brand gradient drifts through the type. One phrase per view; never body copy (readability). Pure CSS; falls back to a solid brand color under prefers-reduced-motion.
日本語の概要は準備中です。原文の説明を表示しています。
Falling code glyphs — a techy backdrop. A bold, canvas-based effect from Open Design's html-ppt fx pack — for slides and launch videos. Guards prefers-reduced-motion (does not start). One per screen; on web use sparingly.
日本語の概要は準備中です。原文の説明を表示しています。
Pick the right LLM for CONTRACT DRAFTING — generating, redlining, or rewriting contract language from instructions. Vendor-neutral routing grounded in mid-2026 legal benchmarks (legalbenchmarks.ai Contract Drafting). 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 should I use to draft this clause/agreement", "best AI for drafting contracts", "route this drafting task", or is about to generate/redline contract text and hasn't fixed a model.
日本語の概要は準備中です。原文の説明を表示しています。
Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). 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 should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extraction task", or is about to pull structured fields from legal docs without a fixed model.
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Pick the right LLM for LEGAL RESEARCH & ANALYSIS — issue-spotting, rule application, case/statute analysis, memos, and multi-step agentic research. Vendor-neutral routing grounded in mid-2026 benchmarks (Vals AI LegalBench across 124 models; Harvey Legal Agent Benchmark for agentic work). 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 for legal research / case analysis / a memo", "best AI for legal reasoning", "route this research task", or is starting legal analysis without a fixed model.
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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 review this contract / NDA / MSA", "best AI to redline", "route this contract review", or is about to review an agreement without a fixed model.
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Pick the right LLM for LEGAL TRANSLATION — translating contracts, statutes, case law, and legal correspondence across languages, including Arabic/MENA. Vendor-neutral routing triangulated from mid-2026 evidence (WMT25 human eval, SwiLTra-Bench legal-MT, multilingual-reasoning proxies, ArabLegalEval). There is NO clean legal-translation leaderboard, so this vertical is directional and treats human legal-linguist review as mandatory. Asks up to 4 quick questions (language pair, cost, speed, privacy/certification), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model to translate this contract/statute", "best AI for legal translation", "translate this legal doc to/from Arabic", or is about to machine-translate legal text without a fixed model.
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Verification-first methodology for the Jurisdicción Especial para la Paz (JEP), Colombia's transitional justice court under the 2016 Acuerdo Final with FARC-EP. Citations are verified against jep.gov.co before use. Covers the 11 macrocasos (from Caso 01 secuestro through Caso 11 violencia sexual, including Caso 03 falsos positivos), the three sanction tiers (propias, alternativas, ordinarias), TOAR, and the SIVJRNR sister bodies (CEV, UBPD). Guards the key structural distinctions (Sala de Reconocimiento vs Tribunal para la Paz; comparecientes FARC-EP vs Fuerza Pública vs terceros civiles; Acto Legislativo 01/2017 vs Ley Estatutaria 1957/2019). Research aid, not legal advice.
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Systematic contract negotiation strategies for technology services agreements with German/EU law specificity. Provides three-position framework (provider-favorable, balanced, client-favorable), deal-size calibration (€100K to €10M+), five-tier objection handling with regulatory leverage (GDPR, DORA, NIS2, BGB), concession packaging, and position matrices for liability, IP, payment, SLA, data protection, and termination. Use when: (1) Developing negotiation strategies for SaaS, cloud, or managed services agreements, (2) Preparing position papers and fallback positions, (3) Responding to counterparty objections, (4) Structuring concession packages that protect core economics, (5) Calibrating tactics to deal size and leverage, or (6) Applying German contract law constraints (BGB §§ 305-310 AGB, § 309 Nr. 7 liability limits, § 288 statutory interest).
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Critically assess and improve a legal scholarly thesis, article idea, student note, seminar-paper claim, or research agenda using Eugene Volokh's novelty, nonobviousness, utility, and soundness framework, connector-aware research, claim-specific tests, and a sustainable adversarial pass. Produces a self-contained green-, yellow-, or red-light report. Use when a law professor or law student asks whether a claim is viable, novel, publishable, preempted, useful, worth pursuing, or better than competing topics; or wants a thesis stress-tested, narrowed, repaired, or compared. No connector is required: use connected legal and scholarly research when available, public sources otherwise, and disclose any no-research fallback.
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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.
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Guide to review incoming one-way (unilateral) commercial NDAs in a jurisdiction-agnostic way, from either a Recipient or Discloser perspective (user-selected), producing a clause-by-clause issue log with preferred redlines, fallbacks, rationales, owners, and deadlines.
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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.
日本語の概要は準備中です。原文の説明を表示しています。
Detects prompt injection hidden in documents from the other side (pleadings, skeletons, bundles, served evidence, opponents' emails and attachments) before an AI reads them, so the model is not turned against your client. Checks word by word that the text an extractor hands the model was visibly drawn on the page: white, near-white, covered, clipped, off-page, tiny, transparent and invisible-mode text in PDF (ink test plus OCR); Word formatting resolved as Word does (styles, shading, theme colours, scaling, off-page frames, fallbacks, unused headers); HTML/CSS, RTF, email with attachments and zips, xlsx/pptx and legacy formats. Flags Unicode smuggling, text addressed to an AI and authorities found only in hidden text. Fails closed. Optional Read-tool hook. Use BEFORE any AI summary or analysis of an opposing or third-party document. Triggers: 'scan for injection', 'is this document safe', 'check the other side's skeleton', 'injection guard'.
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Assess whether Government of India approval is required for foreign investment in an Indian company under Foreign Exchange Management Act, 1999 and the Non-Debt Instruments Rules, 2019. The skill systematically gathers transaction details, evaluates sectoral entry routes and caps, assesses land border country restrictions under applicable laws, and delivers a preliminary compliance note. Produces clear guidance on whether the investment falls within Automatic Route or requires Government approval.
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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.
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Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output.
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Expert HIPAA compliance assistant for healthcare and software contexts. Use this skill whenever the user mentions HIPAA, PHI (Protected Health Information), ePHI, covered entities, business associates, healthcare data privacy, medical records, health information security, BAA (Business Associate Agreements), or any compliance review involving patient data. Also trigger for requests to draft privacy notices, HIPAA policies, consent forms, security risk assessments, or breach notification letters. Use for developers building healthcare software who need technical safeguard guidance (encryption, access controls, audit logs), compliance officers reviewing documents or procedures, and anyone asking "is this HIPAA compliant?" or "what does HIPAA require for X?". When in doubt about whether a healthcare or data privacy question falls under this skill — use it.
日本語の概要は準備中です。原文の説明を表示しています。