3,562 件 ・ 関連度順
概要と使いどころ
Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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 implementing caching, session storage, rate limiting, or any Redis integration. Covers cache-aside pattern, key naming, TTL strategy, and serialization config.
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 writing tests of any kind — unit, slice, or integration. Covers test structure, naming conventions, Mockito patterns, @WebMvcTest, @DataJpaTest, and Testcontainers setup.
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 integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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。
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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 centralized audit logging and SIEM integration. Configure log retention and security monitoring. Use when implementing audit trail 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。
Compiled CLI covering all 52 endpoints of the Anytype local API — objects, properties, tags, search, chat, files — one binary, no MCP server needed.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
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 this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.
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。
Implementing the IAB Transparency and Consent Framework v2.2 for programmatic advertising consent management. Covers CMP registration, Global Vendor List integration, TC String encoding, publisher restrictions, and compliance validation.
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 the NIST Privacy Framework PROTECT function covering PR.AC access control, PR.DS data security, and PR.PO protective policies. Provides technical control implementation guidance, encryption standards, access management architectures, and security-privacy integration patterns.
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 automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
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。
Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring. Keywords: PII detection, unstructured data, NER, spaCy, Presidio, OCR, regex, email scanning, document scanning.
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 and implement Purpose-Based Access Control (PBAC) architecture including purpose ontology definition, policy engine configuration, audit logging of purpose verification at query time, and integration with existing IAM systems. Enforces GDPR Article 5(1)(b) purpose limitation technically.
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。
Establishes ongoing RoPA maintenance processes including update triggers, change management integration, version control, stakeholder review cycles, and completeness verification procedures. Activate for RoPA updates, record maintenance, change management, version history, review scheduling.
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 HIPAA workforce training requirements under 45 CFR §164.530(b) (Privacy Rule) and 45 CFR §164.308(a)(5) (Security Rule). Covers initial onboarding training, periodic refresher cadence, role-based content differentiation, documentation of training completion, and sanction policy integration. Keywords: HIPAA training, workforce training, security awareness, privacy training, §164.530(b), §164.308(a)(5).
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 comprehensive inventory of protected health information across the enterprise per HIPAA Security Rule requirements at 45 CFR §164.308(a)(1)(ii)(A) and §164.310(d). Covers identification of all ePHI repositories, data flow mapping, classification of PHI by sensitivity, and integration with risk analysis. Keywords: PHI inventory, ePHI, data mapping, information asset, data flow, HIPAA risk analysis, designated record set.
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。
Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and integration with STRIDE security threat modeling.
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 data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion 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。
Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
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。
Implementation guide for ePrivacy Directive compliant double opt-in email consent. Covers confirmation email workflow design, token expiration handling, record-keeping requirements, suppression list management, and integration with CAN-SPAM Act and CASL requirements for multi-jurisdiction compliance.
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。