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概要と使いどころ
Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.
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。
Create a source-faithful single-product FF&E cut sheet or spec book entry from a supplied PDF or product URL. Preserve selected identity and source limits; use ffe-spec-book for a complete FF&E book and spec-writer for Project Manual 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。
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.
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Provides TypeScript patterns for DynamoDB-Toolbox v2 including schema/table/entity modeling, .build() command workflow, query/scan access patterns, batch and transaction operations, and single-table design with computed keys. Use when implementing type-safe DynamoDB access layers with DynamoDB-Toolbox v2 in TypeScript services or serverless applications.
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Generates complete CRUD modules for NestJS applications with Drizzle ORM. Use when building server-side features in NestJS that require database operations, including creating new entities with full CRUD endpoints, services with Drizzle queries, Zod-validated DTOs, and unit tests. Triggered by requests like "generate a user module", "create a product CRUD", "add a new entity with endpoints", or when setting up database-backed features in NestJS.
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。
Provides patterns to implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and `@Query`), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing.
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。
Canonical rules for writing and reviewing Rust code in Shift's crates. Use whenever you add or change `.rs` code, especially error handling (`ok_or`, `ok_or_else`, `map_err`, `?`, `CoreError`, `StoreError`, `SlugError`, `BridgeError`), entity lookups by id, checked arithmetic and integer conversions, or helpers that several call sites repeat. Load `rustdoc` as well when the change adds or edits doc comments.
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 data models for construction projects. Create entity-relationship diagrams, define schemas, and generate database structures.
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 AI-generated writing patterns in prose. Use when reviewing docs for slop, vague language, or identity leaks before publishing.
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 someone wants to build, refine, or audit their personal brand positioning across professional platforms. Triggers on "personal brand", "brand positioning", "brand voice", "how should I position myself", "professional identity", "LinkedIn presence", "build my brand".
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, inspect, and reuse Featuretools automated feature engineering workflows for EntitySets, DFS, feature matrices, primitive authoring, feature inspection, and selection.
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Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.
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。
Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction 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。
Turn 2+ user reference images into a high-fidelity animated Codex companion using PocketMen's own local stack. Prefer the open-weight Neural Local Studio (FLUX.2 klein 4B; optional Qwen-Image-Edit-2511 Identity-Max) on compatible hardware; otherwise fall back to the deterministic local renderer. Never require hatch-pet or OPENAI_API_KEY for the normal workflow.
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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.
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Guides AI agents through the complete GDPR Data Subject Access Request (DSAR) workflow under Article 15, including identity verification, 30-day deadline calculation with extensions, response formatting, exemptions, and fee provisions. Activate when handling DSAR, access request, subject access, Art. 15, or SAR queries.
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Guides appointment of GDPR Article 27 EU representative for non-EU controllers or processors. Covers criteria, responsibilities, and documentation. Activate when a non-EU entity processes EU data. Keywords: EU representative, Article 27, non-EU controller, territorial scope.
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California consumer privacy rights workflow implementation under CCPA/CPRA. Covers right to know, delete, opt-out of sale/sharing, correct, and limit sensitive PI processing. Includes 45-day response timelines, identity verification procedures, and authorized agent handling.
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Implements CCPA Section 1798.105 right to delete and CPRA amendments including service provider obligations, statutory exceptions for legal, security, and internal uses, consumer identity verification procedures, and 45-day response timeline management. Activate for CCPA deletion, CPRA right to delete, California privacy, consumer deletion queries.
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Manages California Consumer Privacy Act (CCPA) consumer rights requests under Civil Code sections 1798.100-125, covering the right to know, right to delete, right to opt-out of sale, and non-discrimination. Includes 45-day response window and identity verification requirements. Activate for CCPA, California privacy, right to know, right to delete, opt-out of sale queries.
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Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.
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Guide for building a consent record-keeping system to demonstrate valid consent per GDPR Article 7(1). Covers required fields including timestamp, version, purpose, mechanism, and identity. Implements audit-ready consent receipts per the Kantara Initiative Consent Receipt Specification and supervisory authority expectations.
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Manages deletion requests for children's personal data. Covers parental-initiated versus child-initiated requests, age of capacity assessment, identity verification, scope determination, third-party notification obligations, and regulatory timelines under GDPR Art. 17, COPPA Section 312.6, and UK AADC Standard 15. Keywords: deletion, children, right to erasure, parental request, data deletion, COPPA.
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 generating JPA entities, repositories, queries, or anything touching the persistence layer. Covers entity conventions, N+1 prevention, projections, and query 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。