「cti」の検索結果

Establishes boundaries for monitoring remote and hybrid workers including screen capture, productivity tracking, camera and microphone activation, attendance verification, and activity logging. Applies proportionality principles, transparency requirements, and evaluates less intrusive alternatives per EDPB and national DPA guidance. Keywords: remote work, monitoring, screen capture, productivity tracking, webcam, home office, hybrid work, proportionality, surveillance.

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。

Technical implementation of GDPR Article 25(2) data protection by default. Covers strictest privacy settings as default configuration, minimum data collection, limited storage duration, restricted accessibility, and opt-in rather than opt-out patterns. Includes implementation checklist and system design requirements.

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。

Guides compliance with India's Digital Personal Data Protection Act 2023. Covers consent manager registration, data fiduciary obligations under Sections 4-7, significant data fiduciary requirements under Section 10, data principal rights, and Board enforcement framework. Keywords: DPDP Act, India data protection, consent manager, data fiduciary, significant data fiduciary, data principal rights.

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。

Nigeria Data Protection Regulation (NDPR) and Nigeria Data Protection Act (NDPA) 2023 compliance. Covers lawful basis for processing, data subject rights, cross-border transfer mechanisms, Data Protection Compliance Organisation (DPCO) registration, mandatory DPIA filing, and breach notification. Keywords: NDPR, NDPA, Nigeria, NITDA, DPCO, Africa data protection, cross-border transfer.

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。

Guides a comprehensive organisational data protection audit against key GDPR requirements including Articles 5, 24, 25, 28, 30, 32, 35, and 37. Includes 50+ control points covering principles, accountability, security, and governance. Activate when performing compliance audits, preparing for supervisory authority inspections, or assessing organisational GDPR maturity. Keywords: data protection audit, compliance audit, GDPR audit, control points, accountability.

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。

Guides the audit of Records of Processing Activities (RoPA) against GDPR Article 30 requirements for both controllers and processors. Activate when verifying RoPA completeness, validating mandatory fields, or preparing for supervisory authority inspections. Keywords: RoPA, Article 30, records audit, processing activities, controller records, processor records.

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。

Connecticut Data Privacy Act (CTDPA) compliance. Covers consumer rights, controller obligations, dark pattern prohibition, loyalty program exemption, universal opt-out requirement effective January 2025, sensitive data consent, and AG enforcement. Effective July 1, 2023.

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。

Guides the end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.

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 and organize AI coding-agent instructions, including AGENTS.md, CLAUDE.md, skills, and agent definitions. Use when the user asks to diagnose conflicting instructions, excessive approval pauses, broad skill triggers, duplicate guidance, or broken instruction references, or to clean up that instruction set. Ordinary code review, Markdown editing, or application debugging alone does not trigger this workflow. Inspection stays read-only; apply changes when authorized.

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。

Fix Node.js pg-pool not reconnecting after CloudSQL connection reset (ECONNRESET). Use when: (1) Retries fail with same ECONNRESET error despite retry logic, (2) "Connection terminated" or "connection already closed" errors persist across retries, (3) "remaining connection slots are reserved" errors in CloudSQL, (4) High-concurrency Node.js app with pg-pool and CloudSQL/managed PostgreSQL. The pool caches dead connections - you must recreate the pool, not just retry.

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。

Convert image references into spatial hypotheses, camera constraints, construction routes, material evidence, and measurable Blender reconstruction gates. Use whenever a user asks to replicate, recreate, match, clone, or reconstruct a scene, environment, object, material, lighting setup, or shot from one or more images, especially when room connectivity, hidden space, perspective, visible-frame fidelity, material identity, aging, wetness, or photographic treatment matters.

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。

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection 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。

Think and work like an expert Galactic Astronomer. Use when a task calls for Galactic Astronomer judgment. Reasons from distance ladders, dust extinction, and survey selection functions through Gaia DR3 cross-matches, isochrone and Bayesian SFH fitting (PARSEC/MIST, Starfish), and orbit integration in named potentials (McMillan17, MWPotential2015) via galpy, Agama, and Gala, while treating parallax-S/N and RUWE failures, unresolved binaries and fiber collisions, dust-or-crowding overdensities, and spiral-arm-mimicking-streams as first-class failure modes.

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。

Select restriction enzymes by criteria using Biopython Bio.Restriction. Find enzymes that cut once, don't cut, produce specific overhangs, are commercially available, or have compatible ends for cloning. Use when selecting restriction enzymes for cloning or analysis.

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。

Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.

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。

Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.

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 Grok 4.5 as the required external design advisor when a task needs UI critique, UX critique, component-choice review, interaction-flow review, design imagery markdown, art direction, visual hierarchy judgment, design-system fit, color/type/layout suggestions, HTML mockups, SVG icons, handwritten SVG wordmarks, or file-based feedback on existing UI. Trigger on requests like "design a page", "generate design imagery", "give design advice", "optimize this UI", "improve UX", "optimize user flow", "review interaction", "check usability", "reduce friction", "choose the right component", "review this design", "create an icon", "suggest colors", "UI mockup", "visual direction", "lettering", "wordmark", "handwritten logo", or when another agent needs a second opinion on visual or interaction quality.

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。

Validate system resilience through controlled fault injection. Covers hypothesis-driven chaos experiments, failure injection types (network, service, infrastructure, dependency), LitmusChaos/Chaos Mesh/AWS FIS/Gremlin/toxiproxy tooling, automated abort gating, game day planning, and progressive chaos adoption. Use when: "chaos engineering," "fault injection," "resilience test," "game day," "failure recovery," "system reliability," "blast radius." Not for: safe rollout flags/canary/dark launch during a release — use testing-in-production; designing new tests from production telemetry — use observability-driven-testing. Related: testing-in-production, observability-driven-testing, performance-testing, release-readiness, test-environments.

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。

Symfony UX LiveComponent for reactive server-rendered UI -- components that re-render via AJAX on user interaction, zero JavaScript required. Use when building live search, real-time filtering, dynamic forms, inline validation, dependent selects, auto-save, polling, deferred/lazy rendering, or any UI that updates itself based on user input. Code triggers: AsLiveComponent, LiveProp, LiveAction, data-model, data-loading, data-live-action-url, ComponentWithFormTrait, LiveListener, emit, defer, lazy, polling. Also trigger when the user asks "how to build a search that filters as I type", "how to validate a form in real-time", "how to make a reactive component in PHP", "how to build dependent selects", "how to defer component rendering", "how to communicate between components via emit", "how to bind a form to a LiveComponent". Do NOT trigger for static reusable UI without reactivity (use twig-component), pure client-side JS behavior (use stimulus), or page-level navigation (use turbo).

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。

Assists with building real-time reactive backends using Convex. Use when creating databases with automatic client sync, reactive queries, file storage, scheduled functions, or full-text and vector search. Trigger words: convex, reactive backend, real-time database, useQuery, useMutation, convex functions, convex schema.

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。

Build serverless applications with Azure Functions. Create HTTP and event-driven functions with input/output bindings, configure triggers for queues, timers, and blob storage. Use Durable Functions for stateful orchestration workflows.

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。

Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.

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。

World-class creative strategy expertise combining the conceptual brilliance of agencies like Wieden+Kennedy, the data-informed creativity of modern brands, and the artistic vision that makes campaigns unforgettable. Creative strategy is the bridge between business objectives and creative execution. Great creative isn't just pretty pictures - it's strategic thinking made visible. The best creative strategists understand that breakthrough ideas come from deep insight, that constraints breed creativity, and that the most memorable campaigns are the ones that tell true stories in unexpected ways. Use when "creative strategy, campaign concept, creative direction, art direction, video production, photo shoot, brand campaign, advertising, ad creative, visual identity, campaign idea, creative brief, storyboard, motion design, commercial, brand video, creative, campaigns, art-direction, video, advertising, visual, storytelling, production" mentioned.

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。

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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。