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概要と使いどころ
Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.
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Guides GDPR certification mechanism implementation per Articles 42-43 including accredited certification body selection, certification criteria per EDPB guidelines, certification scope, periodic audit requirements, seal and mark usage rules, and relationship to codes of conduct. Covers EDPB/ENISA certification framework and national accreditation. Keywords: GDPR certification, Article 42, Article 43, certification body, EDPB, seal, mark, accreditation.
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Compares PIA/DPIA methodologies: CNIL PIA tool, ICO DPIA template, NIST Privacy Framework, and ISO 29134. Provides methodology selection criteria based on regulatory jurisdiction, organisation maturity, processing complexity, and resource availability. Covers regulatory acceptance, tool features, and cross-methodology mapping. Keywords: PIA methodology, CNIL, ICO, NIST Privacy Framework, ISO 29134, DPIA comparison, assessment.
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Builds a comprehensive breach response team playbook defining CSIRT and privacy team structure with named roles (incident commander, legal counsel, communications, IT forensics, DPO), escalation matrices, communication templates, pre-negotiated vendor contacts, and regulatory authority contacts organized by jurisdiction. Keywords: breach response playbook, CSIRT, incident response team, escalation matrix, communication templates, vendor contacts.
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Guides privacy audit sampling methodology including statistical and non-statistical sampling, sample size determination, stratification techniques, attribute sampling for compliance testing, confidence level selection, tolerable deviation rates, and extrapolation of results to the population. Keywords: audit sampling, statistical sampling, attribute testing, sample size, confidence level, stratified sampling, privacy audit.
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Implements technical breach detection capabilities including SIEM integration, DLP alert configuration, anomaly detection rules, and insider threat monitoring. Provides a breach classification taxonomy across confidentiality, integrity, and availability dimensions. Covers detection tool selection, alert tuning, and integration with privacy incident response workflows. Keywords: breach detection, SIEM, DLP, anomaly detection, insider threat, classification.
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 application of the eight privacy design patterns per Hoepman: minimize, hide, separate, abstract, inform, control, enforce, and demonstrate. Covers pattern selection methodology per processing activity, mapping to GDPR principles, and practical implementation guidance for privacy-by-design system architecture.
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Evaluates and implements age estimation and verification technologies for online services. Covers facial age estimation, digital ID verification, self-declaration with risk assessment, AI-based age estimation, and the accuracy versus privacy tradeoff. Includes ICO guidance and euCONSENT framework. Keywords: age verification, age estimation, facial analysis, digital ID, children, online safety.
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Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.
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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.
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Use when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.
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Generate Atlas Cloud images and videos through its asynchronous media API with schema-first model selection and credential-safe polling.
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。
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
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A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
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Prompt engineering regression test matrix register: baseline outputs, semantic drift thresholds, judge evaluations, and golden dataset.
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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.
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Review AAS stack manifests, preview plans and selection evidence in Workbench without applying changes.
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Scores shortlisted accounting packages against 57 evidence-backed fields, emitted as CSV, SQL, JSON Schema or Notion on request. Use for choosing accounting software.
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Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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Schedule AI agent runs with cron, loops, or external clocks while avoiding unsafe tight autonomous timers.
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。