「policy」の検索結果

Theory of Constraints primitives for focusing steps, drum-buffer-rope, throughput accounting, critical chain, and policy constraints. Use when sequencing by bottleneck.

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Designs approval and sandbox policy for local coding agents: permission modes, isolation, network and destructive-command guards. Use when setting approvals. Not cloud sandboxes.

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Guides dependency audits and upgrades. Use when auditing dependencies, upgrading vulnerable packages, managing lockfiles or package managers, SBOMs, or update and monorepo policy.

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Refresh HitKeep's checked-in bot/AI-agent, IP metadata, and spam-filter datasets through their canonical generators. Use for routine generated-data maintenance; do not use for hand-editing generated assets or changing detector policy.

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Auditing regulatory compliance (SOC2/PCI-DSS/HIPAA/ISO 27001): maps requirements, checks controls, designs audit trails, implements Policy as Code. Use when compliance auditing is needed.

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Reviewing legal documents for Terms of Service, Privacy Policy, and Tokushoho compliance. Detects clause gaps and flags risks. Not a substitute for legal advice — consult a lawyer.

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Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.

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Determine whether front/side/back/top reference templates can be satisfied by one rigid 3D Blender model, choose a canonical source policy, and emit constraints before rebuilding. Use when multiview bboxes, silhouettes, part counts, or depth ratios contradict each other.

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Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

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Use when reviewing, critiquing, or stress-testing an existing strategy document. Evaluates seven dimensions — diagnosis quality, guiding policy strength, action coherence, assumption exposure, falsifiability — with optional 7S, Five Forces, Balanced Scorecard, and Hoshin Kanri lenses. Triggers on: review my strategy, poke holes in this plan, what's weak here, strategy audit, red team this. Does NOT build strategy (use strategy-interview) or brainstorm project ideas (use brainstorm-beagle).

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Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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The alphabetical-sort invariants the hyperlane-registry CI / CodeRabbit enforces on warp route YAML (deploy.yaml and config.yaml) — top-level chain entries sorted by chain name, and keys within each entry in strict alphabetical order — plus the canonical per-file key orderings and a verification pass. Referenced by any warp deploy/update skill that writes or edits a registry YAML file.

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The low-level on-chain probes for identifying what an owner address is — cast code → EOA / EIP-7702-delegated EOA / contract, VERSION() → Gnosis Safe, read-only get-owner-ica.ts derive → ICA derivation, getAccountInfo → Sealevel account existence — with how to interpret each result. The primitive layer beneath validate-owners / resolve-artifacts / update-owners, which each apply their own per-chain or per-artifact policy on top.

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保险行业政策追踪工具。追踪保险监管政策、监管动态、改革措施。接入东方财富/新浪财经等开源API，支持自动更新。适用于政策研究、合规监测。

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Processes insurance renewal documentation for brokers and underwriters. Extracts policy details, compares renewal terms against expiring policies, identifies coverage gaps, and generates renewal summaries with premium comparisons and recommendation narratives.

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Produces structured, citation-backed summaries of U.S. insurance policies, endorsements, claims files, and coverage correspondence for coverage analysis and insurance litigation. Use when summarizing policies, declarations, claims files, reservation-of-rights letters, denial letters, or coverage disputes.

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Produces structured summaries of insurance claims correspondence including coverage letters, reservation of rights (ROR), denial letters, and claim file documents. Extracts policy details, coverage positions, claims chronology, settlement posture, and liability exposure. Use when summarizing claim files, coverage disputes, ROR letters, denial letters, or multi-document insurance correspondence threads.

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Load this skill when the user asks specific insurance education questions in the Indian context, such as term vs endowment vs ULIP, riders, claim settlement process, nominee concepts, or policy jargon; do not load for generic budgeting or emergency fund queries.

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Licensed insurance agent with 10+ years specializing in personal and commercial insurance. Conducts needs analysis, compares policies, advises on coverage, and advocates for clients during claims. Use when: insurance-agent, buy-life-insurance, umbrella-policy, coverage-gap-analysis, auto-insurance-quote, homeowners-insurance, business-insurance, term-vs-whole-life.

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EU Insurance Adviser specializing in IDD compliance for life, health, property, and insurance-based investment products (IBIPs). Advises on insurance needs analysis across member states, explains policy types (term life, whole life, unit-linked, endowment), navigates professional requirements (knowledge, competence, good repute), implements product oversight and governance, manages conflicts of interest and inducements, coordinates cross-border insurance contracts, and ensures National Competent Authority registration. Use for insurance distribution, IBIP suitability assessments, or IDD compliance implementation.

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Extrae datos estructurados de póliza de seguros argentina — condiciones, exclusiones, franquicias, obligaciones

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Consulta sistemas internos de Libra para obtener el documento de póliza a partir de datos del asegurado o del vehículo

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Answer employee questions about company policies, benefits, and procedures. Use when users ask about PTO, vacation, sick leave, parental leave, health insurance, 401k, remote work policy, expense reimbursement, code of conduct, professional development, or any company-specific rules and perks.

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。