「party」の検索結果

DORA (EU 2022/2554) digital operational resilience compliance for financial entities, covering all 5 pillars. Use for DORA readiness assessments, ICT risk management, incident classification, and third-party ICT oversight.

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。

Dispute analysis and mediation preparation framework. Use when preparing for mediation, analyzing disputes, calculating settlement ranges, mapping party interests, or developing negotiation strategy.

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。

Vendor lifecycle — weighted selection scorecards, risk tiering, renewal and notice-deadline tracking, spend concentration, and SLA credits. Use when selecting a vendor, preparing a renewal, or reviewing a vendor portfolio.

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。

Detect whether an API endpoint is backed by genuine Claude (not a wrapper, proxy, or impersonator) using 9 weighted rule-based checks that mirror the claude-verify project. Also extracts injected system prompts from providers that override Claude's identity. Fully self-contained — copy the code below and run, no extra packages beyond httpx. Use when the user wants to verify a Claude API key or endpoint, check if a third-party Claude service is authentic, audit API providers for Claude authenticity, test multiple models in parallel, or discover what system prompt a provider has injected.

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。

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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Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.

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。

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.

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Audits legal AI skills, prompts, workflows, MCP/tool instructions, and agent packages for prompt injection, malicious instructions, unsafe scripts, suspicious metadata, credential exposure, exfiltration paths, persistence mechanisms, and supply-chain risk. Use before installing, importing, publishing, promoting, modifying, or trusting third-party or generated AI skills and legal workflows. Do not use as a generic code review unless skill trust, workflow safety, prompt injection, or supply-chain hygiene is in scope.

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。

Audit an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan Source / homoglyphs) and emits a PASS / WARN / FAIL verdict.

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。

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 (ChatGPT, Claude.ai, Copilot, Gemini, etc.) 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.

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'.

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。

Expert New Zealand Information Security Manual (NZISM) advisor for NZ government agencies and their supply chains. Use for NZISM control guidance, gap analysis, agency security obligations, classification framework (Unclassified through Top Secret), security risk management, system certification, and GCSB/NCSC NZ compliance. Triggers on: NZISM controls, NZ government security, GCSB compliance, agency cybersecurity obligations, NZ classification markings, Restricted/Confidential/Secret system scoping, agency security policies, third-party supplier security, Certification and Accreditation (C&A), and any question about NZ government information security requirements or the NZISM framework.

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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.

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。

Use when a user needs a legal or legal-adjacent Mermaid diagram from a document, pasted text, matter description, process, timeline, party map, obligation map, corporate structure, funds flow, or compliance workflow. Trigger on: "diagram this contract", "visualise this deal/matter", "map the parties", "create a timeline of events", "make an org chart", "obligation checklist", "export as HTML diagram". Not for general-purpose non-legal diagrams, pure graphic design, image generation, or legal advice.

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。

Practitioner skill for advising on EU Regulation 2023/2854 (Data Act). Covers Chapters II-VII (IoT data access, mandatory B2B sharing, unfair contract terms, public-sector exceptional need, cloud switching, third-country governmental access) and Chapter VIII (interoperability and smart contracts, gate-only). Use when the user asks about Data Act rights or obligations, drafts a Data Act notice or letter, reviews a data-sharing or cloud-switching contract under the Data Act, runs a Data Act gap analysis, or asks how the Data Act interacts with GDPR, the DMA, the Trade Secrets Directive, or sectoral law. Triggers include "Data Act", "Datengesetz", "Regulation (EU) 2023/2854", "Art. 4(1) request", "Art. 5(1) third-party request", "trade-secret handbrake", "cloud switching obligations", "Chapter VI", "Ch V exceptional need", and references to specific Data Act articles or recitals.

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An end-to-end method for drafting and finalising a complex, multi-pillar regulated fintech agreement — from intake to signature. Authored from a senior fintech lawyer's manual: a licensed payment-services provider engaging a counterparty across agent cash-in/cash-out, QR payments, wallet e-payments, and a marketplace, each with its own regulatory profile. Runs five phases and fourteen steps: regulatory mapping (activity-to-licence matrix, grey-zone classification gates), architecture (framework-plus-sub-agreement structure, ring-fenced marketplace), the regulatory–commercial balance (what flexes vs what cannot), core drafting (authority, float mechanics, hard-coded regulator caps, liability — all tracking control), execution-blocker triage, and a pre-signature check closing open blockers as conditions precedent. It refuses to invent licence-specific values or draft a representation as true without executed evidence. Use it to structure, draft, negotiate, or review any regulated payments contract.

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。

Expert CSRD (Corporate Sustainability Reporting Directive, EU 2022/2464) compliance advisor. Use this skill whenever a user asks about CSRD, European Sustainability Reporting Standards (ESRS), double materiality assessment, sustainability reporting obligations, ESG disclosure, CSRD scope and thresholds, value chain reporting, XBRL digital tagging, third-party assurance, CSRD gap assessments, CSRD implementation timelines, ESRS E1–E5 environmental standards, ESRS S1–S4 social standards, ESRS G1 governance, CSRD vs GRI/TCFD/SASB alignment, or any EU corporate sustainability reporting question. Trigger even if the user only mentions "ESG reporting Europe", "sustainability disclosure EU", or "non-financial reporting".

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。

California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) compliance advisor — business threshold analysis, consumer rights fulfillment (access, delete, correct, opt-out of sale/sharing, limit SPI, ADMT opt-out), privacy notice drafting, service provider vs. contractor vs. third-party classification, sensitive personal information (SPI) handling, data minimization, opt-out mechanisms (including GPC), cybersecurity audits and risk assessments (live since Jan 1, 2026), ADMT obligations (effective 2026, deadline Jan 1, 2027), CPPA enforcement (Disney $2.75M, PlayOn $1.1M, Ford $375K), penalty exposure, GDPR comparison, and gap assessments for businesses operating in or targeting California residents.

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Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI product feature, an LLM workflow, or a third-party AI vendor. The skill asks intake and clarifying questions first when facts or evidence are missing, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a preliminary or final governance review with scorecards, findings, owners, remediation actions, and follow-up questions.

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Works out which documents you have to hand over to the other side in a civil case in England & Wales, and builds the formal list. The part it gets right that trips people up is which disclosure regime applies — Practice Direction 57AD (the Disclosure Pilot, now permanent in the Business and Property Courts, with its Models A–E) or standard disclosure under CPR Part 31 everywhere else. It picks the regime, chooses a Model per issue, structures the Disclosure Review Document, and drafts the disclosure certificate for the party to sign personally. Built for litigation juniors, in-house counsel, and small teams without a precedent bank. Use when the user says 'disclosure list', 'PD 57AD', 'Model C', 'extended disclosure', 'List of Documents', 'N265', or needs to work out what must be disclosed.

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。

Privacy manifest (PrivacyInfo.xcprivacy) implementation including required reason APIs, tracking domains, third-party SDK declarations, and App Tracking Transparency. Use when preparing apps for App Store privacy requirements.

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Generate privacy policies, terms of service, and EULAs for Apple platform apps. Detects data collection patterns, third-party SDKs, and generates region-specific legal documents with Apple Privacy Nutrition Label mapping. Use when user needs legal documents or data collection disclosure for App Store submission.

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。

Expert knowledge for Azure Service Connector development including troubleshooting, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring Azure compute to Redis, databases, storage, messaging, AI services, or third‑party endpoints, and other Azure Service Connector related development tasks. Not for Azure API Management (use azure-api-management), Azure Connector Namespace (use azure-connector-namespace), Azure Logic Apps (use azure-logic-apps), Azure Service Bus (use azure-service-bus).

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Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats. Triggers on: "create original data", "proprietary data", "survey design", "benchmark study", "original research", "data-driven content", "create a survey", "industry benchmark", "aggregated data", "unique data", "first-party data", "data moat", "generate research data", "create a study", "original statistics", "data nobody else has", "competitive data advantage".

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。