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概要と使いどころ
Provides comprehensive Drizzle ORM patterns for schema definition, CRUD operations, relations, queries, transactions, and migrations. Proactively use for any Drizzle ORM development including defining database schemas, writing type-safe queries, implementing relations, managing transactions, and setting up migrations with Drizzle Kit. Supports PostgreSQL, MySQL, SQLite, MSSQL, and CockroachDB.
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 chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
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 Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
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。
When a founder needs to write cold emails or LinkedIn messages to prospects, partners, or investors. Activate when the user mentions cold email, outbound, prospecting, LinkedIn outreach, or needs help getting replies from people who don't know them.
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。
Interview the user one question at a time to resolve remaining ambiguity before implementation. Use when planning or brainstorming is done but unknowns remain, or when the user asks to be interviewed about a task or spec.
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。
JSON/data schema validation for construction data exchange: API payloads, file imports, BIM exports. Ensure data structure compliance before processing.
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。
Email generation for construction workflows: RFI responses, submittal transmittals, meeting notices, change order notifications. Professional templates with context-aware 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。
Semantic search in the DDC CWICR construction cost database using vector embeddings (BGE-M3, 1024-dim, per-language Qdrant collections). Find similar work items and resources for cost estimation across 8 national bases and 30 markets in 26 languages.
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。
Map construction data to standard ontologies. Create semantic mappings between different data schemas
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。
Integrate and analyze telematics data from heavy construction equipment. Track location, utilization, fuel consumption, maintenance needs, and operator behavior.
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。
Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say "put these together" or "clean up these lists into one".
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。
Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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。
Searches academic literature via arXiv, Semantic Scholar, and open-access PDFs. Use when building literature reviews or finding formal research on a topic.
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 sem semantic-diff detection, install-on-first-use, and fallback patterns. Use when building skills that consume git diff output.
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 geemap for Google Earth Engine interactive mapping, geospatial conversion, visualization, timelapse, and ML helper 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。
Cinematic camera work in Blender including framing, lenses, composition, camera animation, and shot language beyond basic rendering setup via MCP.
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。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
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。
Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.
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。
Guides DPIA for workplace monitoring including email surveillance, internet usage monitoring, CCTV, GPS tracking, and keystroke logging. Covers GDPR Art. 88 employment context provisions, WP29 Opinion 2/2017 on data processing at work, and proportionality balancing for employee monitoring. Keywords: employee surveillance, workplace monitoring, DPIA, Art. 88, WP29 Opinion 2/2017, CCTV, email monitoring, GPS tracking.
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。
Colorado Privacy Act (CPA) compliance implementation. Covers universal opt-out mechanism required since July 2024, profiling opt-out rights, sensitive data consent requirements, AG rulemaking under 4 CCR 904-3, and consumer rights framework. 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。
Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven 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。
Design and verify data contracts, schema changes, migrations, rollbacks, backfills, compatibility windows, tenant isolation, and API-to-database field mapping. Use when changing database schema, event payloads, persisted documents, analytics tables, multi-tenant data boundaries, or any user-visible data contract where silent fallback or undeclared fields would be dangerous.
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。
Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.
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。