4,128 件 ・ 関連度順
概要と使いどころ
Drafts a BSA/AML Risk Assessment for U.S. financial institutions per FinCEN, FFIEC, and OCC standards. Evaluates inherent risks (customer, product, geographic, transaction, third-party), control adequacy, and residual risk. Use when preparing annual BSA compliance assessments, post-acquisition integration reviews, or when business changes trigger reassessment under 31 U.S.C. § 5318(h).
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。
类案检索报告生成 Skill — 面向律师、法官助理及法务人员的类案检索全流程自动化工具。基于待决案件信息，通过调用得理案例检索 API 完成案件拆解、检索执行、类案筛选、比对分析与规则提炼，最终输出一份可直接向法院提交或供内部策略研判使用的标准化《类案检索报告》。当用户提出以下需求时触发：帮我生成XX案由的类似案例报告、检索XX报告、做一个类案检索报告、我要向法院提交类案检索报告
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。
Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.
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。
Produces internal case viability and conflict check memos for personal injury litigation intake. Screens conflicts, assesses liability and damages, checks statute of limitations, and outputs an accept/decline recommendation. Use when evaluating new PI intake, running conflict checks, assessing case merit pre-filing, or preparing acceptance memos.
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。
案例检索 Skill — 面向法律从业者及普通用户的裁判文书智能检索工具。当用户需要检索裁判案例、查找类案裁判规则、按案由/法院/时间筛选案例、或上传文件进行案例匹配时使用。触发词包括："查一下XX的案例"、"找关于XX的判决"、"XX法院的判例"、"类案检索"、"有没有类似的案例"、"XX案由的裁判规则"等。
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。
Generates a structured attorney work product memorandum from an initial client consultation for commercial litigation. Extracts verifiable facts from uploaded documents, builds a chronological narrative, identifies parties and contracts, flags preliminary legal claims and defenses, and produces a prioritized action plan. Triggers at matter intake when a business client needs a foundational case evaluation document for a pre-filing commercial dispute.
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。
AI-powered bank compliance and regulatory review assistant - screen marketing materials, KYC documents, AML alerts, and NFRA/CBIRC compliance checks. Updated for 2025-2026 regulations including 商业银行资本管理办法 (Basel III China 2024), 消费者权益保护规定升级, FATF mutual evaluation 2025, and 反洗钱法修订. Keywords: bank compliance, KYC, AML, regulatory review, NFRA, Basel III, China banking, 合规审查, 合规审核, 监管合规, 反洗钱, KYC尽调, 客户身份识别, 受益人识别, 制裁筛查, 交易监控.
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。
当AI智能体完成一段法律推理或论证后，需要对该推理结论进行自我评估，判断论证的整体强度与置信度，识别并标注推理链条中的薄弱环节。 触发条件包括但不限于： - 完成法律分析后需要给出结论可靠性评级 - 用户明确要求评估某一论证的说服力或可靠性 - 存在多个竞争性法律解释需要比较论证强度 - 推理过程中涉及不确定因素（事实不明、法律空白、争议观点）需要标注 - 为决策者提供风险提示，说明结论的局限性 - 对己方或对方的法律论证进行攻防强度评估 - 在法律意见书、备忘录等文书中需要附加置信度说明
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。
Produces an enforcement-aware Animal Welfare Act compliance audit for USDA/APHIS-regulated facilities. Maps Form 7002 inspection citations to verified 9 C.F.R. Parts 1-3 requirements, analyzes NCI severity and recurrence patterns, evaluates core compliance programs (PVC, IACUC, housing, records, transport), and outputs a prioritized corrective action plan with proof artifacts. Use when auditing AWA compliance, reviewing USDA inspection reports, preparing for license renewal, conducting diligence, or assessing enforcement risk. Trigger: Animal Welfare Act, AWA, USDA, APHIS, Form 7002, IACUC, PVC, exhibitor, dealer, research facility.
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。
Generates structured analytical summaries of appellate documents covering procedural posture, issues on appeal, standards of review, and strategic assessment. Use when onboarding to an appeal, preparing for oral argument, evaluating appellate risk, or summarizing the appellate record.
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。
Produces litigation-grade case summaries for animal law disputes including cruelty/neglect prosecutions, ownership/custody, dangerous animal designations, veterinary malpractice, and service/assistance animal accommodations. Builds exhibit-cited factual timelines, maps evidence to statutory elements, evaluates expert evidence, and assesses remedies with jurisdiction-specific legal research. Use when summarizing animal law cases, analyzing animal cruelty charges, pet custody disputes, dangerous dog hearings, veterinary malpractice claims, or assistance animal accommodation matters. Also trigger for animal forfeiture, bite liability, wildlife possession, or animal-related court filings.
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。
AI compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.
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。
AI compliance and policy engine — evaluate scan results against OWASP, NIST, SOC 2, ISO 27001, CMMC, EU AI Act, AISVS v1.0, and related frameworks. Generate SBOMs and compliance reports. Use when: "compliance report", "NIST", "SOC 2", "ISO 27001", "OWASP", "EU AI Act", "AISVS", "generate SBOM", "policy check".
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。
Builds a candidate-controlled preparation plan for a software-engineering interview target, timeline, and practice evidence. Use for planning round coverage, story coherence, mock cadence, and debrief-driven adjustments. NOT for hiring predictions, candidate ranking, surveillance, or replacing round-specific coaching.
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。
Prepares for hiring manager rounds at Staff+ (L6+) level — scope of impact, influence without authority, ambiguity navigation, mentorship, strategic thinking. Use when practicing HM rounds or calibrating story depth for target level. Activate on "hiring manager round", "HM screen", "staff level", "scope of impact". NOT for coding interviews, system design, behavioral/values rounds, or resume writing.
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。
Design dashboards for migrations and runtimes. Use for authority drift, verifier status, burn-down, runtime health, or pain panels. NOT for vanity analytics, ad hoc charts, or duplicate 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。
Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.
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。
Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.
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。
Architect data fetching with TanStack Query v5, SWR, optimistic updates, prefetching, and cache invalidation strategies. Activate on: React Query, SWR, optimistic updates, cache invalidation, prefetching, stale-while-revalidate, infinite queries. NOT for: REST API design (use api-architect), database queries (use database skills), GraphQL schema (use graphql-expert).
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 designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.
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。
Designing HTTP cache headers that work correctly across browsers, CDNs, and shared proxies — `Cache-Control` directives per RFC 9111, `stale-while-revalidate` and `stale-if-error` per RFC 5861, the Vary header for varying responses, and surrogate keys for tag-based purging. Grounded in IETF RFCs and Cloudflare/Fastly docs.
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。
Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
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。
How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).
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。
Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system
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。