「cat」の検索結果

API reference: UserNotifications. Query for local/remote notifications, triggers, notification content.

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。

Browse hookify rule catalog. Use when installing pre-built rules or browsing categories. Do not use when writing custom rules; use hookify:writing-rules.

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 certification acceptance criteria and equivalence mapping. Covers ISO 27701, SOC 2 Privacy, APEC CBPR, EU Code of Conduct evaluation, certification scope analysis, gap supplementation requirements, and cross-framework equivalence 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。

Identifies and classifies GDPR Art. 9 special category data including racial origin, political opinions, religious beliefs, trade union membership, genetic, biometric, health, and sexual orientation data. Covers processing conditions under Art. 9(2)(a)-(j). Keywords: special category, Art 9, sensitive data, biometric, genetic, health data, explicit consent.

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。

Implements GDPR Article 12 transparent information and communication requirements, covering concise, intelligible, and plain language obligations, response timelines, fee and refusal provisions, and layered notice design. Activate for transparent communication, Art. 12, privacy notice, plain language, response timeline queries.

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。

Implements NIST SP 800-88 Rev. 1 media sanitization procedures including Clear, Purge, and Destroy methods for all media types. Covers certificate of destruction generation, verification procedures, vendor management for third-party destruction, and chain of custody documentation. Activate for data destruction, media sanitization, secure erasure, certificate of destruction queries.

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。

Implements GDPR Article 8 parental consent verification for information society services offered to children. Covers age thresholds by EU/EEA Member State (13-16 years), EDPB Guidelines 5/2020 on consent, parental verification mechanisms, and consent record-keeping. Keywords: parental consent, Article 8, children, age threshold, EDPB, verification.

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。

Conducts Data Protection Impact Assessments for biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, special category.

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。

Develops data classification policies with tiered handling (public, internal, confidential, restricted), labeling requirements, enforcement mechanisms, and procedures per tier. Covers policy governance, exception handling, and compliance monitoring. Keywords: classification policy, data tiers, handling procedures, labeling, enforcement, data governance, information security.

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。

Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.

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 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.

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。

Assesses AI bias risks for GDPR Art. 9 special category data and AI Act Art. 10 data governance. Covers fairness metrics, bias detection methods, mitigation strategies, and documentation requirements for protected characteristics. Keywords: AI bias, special category, fairness metrics, discrimination, Art. 9, Art. 10.

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。

Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.

Authenticate.com integration. Manage data, records, and automate workflows. Use when the user wants to interact with Authenticate.com data.

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。

Amplication integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amplication data.

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。

Amazon Location Service integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Location Service data.

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。

Abstract - Email Verification API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Abstract - Email Verification API data.

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 Flutter home feed / main screen stuck on loading spinner after login when using Riverpod StreamProvider that watches GoRouter location changes. Use when: (1) Screen shows BrandedLoadingIndicator or CircularProgressIndicator permanently after successful auth redirect, (2) Widget watches a route-type-gating provider that returns AsyncValue.loading() intermittently, (3) Logs show route location oscillating between stale and current paths during post-login transition (e.g., /welcome/* after /home/0), (4) Provider chain has double-gate: widget gates on pageContext AND data provider also gates on pageContext. Distinct from riverpod-infinite-rebuild-loop (rapid rebuilds) — this causes permanent loading state, not infinite rebuilds.

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 duplicate Nostr events in ClickHouse when using ReplacingMergeTree for NIP-33 addressable events (Kind 30000+). Use when: (1) Edited videos/events appear as duplicates, (2) Same d_tag shows multiple events with different IDs, (3) FINAL keyword doesn't deduplicate properly for parameterized replaceable events. The issue is that FINAL deduplicates by ORDER BY key (typically `id`), not by (pubkey, kind, d_tag).

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。

Analyzes test suites to identify redundant and duplicate test cases using coverage analysis, semantic similarity, and execution results. Use this skill when you need to reduce test suite size, identify redundant tests, optimize test execution time, analyze test coverage overlap, find tests with identical behavior, or improve test suite maintainability. Triggers when users ask to deduplicate tests, find redundant test cases, reduce test suite size, identify duplicate tests, or optimize test coverage.

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。

Generate implementation code and tests from written specifications. Use when the user provides specifications (natural language descriptions, formal specs, requirements documents, API specs) and asks Claude to implement the described functionality. Supports data structures, algorithms, classes, functions, and includes automatic test generation to validate implementation against specification.

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。

Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.

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。

Automatically simplify and minimize TLA+ specifications by reducing redundant state variables, merging equivalent actions, and minimizing invariants while preserving specified properties. Use when working with TLA+ specifications that need optimization, simplification, or reduction. Triggers when users ask to minimize, reduce, simplify, or optimize TLA+ specs, or when they want to remove redundancy from formal specifications while maintaining semantic equivalence.

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。

Generate systematic refinement steps from high-level specifications to concrete implementations in Isabelle/HOL or Coq, preserving correctness obligations at each step. Use when working with formal verification, program refinement, proof development, or when translating abstract specifications into executable code while maintaining formal guarantees. Supports data refinement (abstract types → concrete structures), algorithmic refinement (specifications → algorithms), and stepwise refinement with proof obligations.

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。