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概要と使いどころ
Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.
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KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.
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Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.
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Your long-term knowledge pages. Read them at session start. Create new pages when you learn something worth remembering across sessions. Pages auto-update from your conversations via Hindsight.
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Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).
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。
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
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 Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.
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。
Analyze a target's TLS configuration — negotiated protocol version, cipher suite, certificate chain, expiry, and downgrade vectors. Use when: SOC2 auditor flagged your endpoint for "weak TLS" but you don't know which control failed (TSC CC6.7 transmission integrity vs CC6.6 encryption) or which cipher is the problem. Threshold: any negotiated TLSv1.0 or TLSv1.1, OR a cipher with RC4 / 3DES / null / EXPORT, OR a cert with under 30 days to expiry, OR a chain that fails hostname verification. Trigger with: "audit tls", "check ssl config", "weak tls", "analyze tls".
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。
Author SVG figures for Grida docs — diff-able, version-controlled vector diagrams embedded in doc pages instead of screenshots. Provides reusable primitives (selection chrome, size badges, anchor pins, resize cursors, click ripples), color/typography tokens, a starter template, and finished examples to crib from. Canvas user docs (docs/editor/) are the first consumer; the kit is meant to generalize to any product's docs. Use when drawing diagrams that explain UI behaviour — gestures, alignment, before/after states — that a screenshot alone can't capture. Trigger phrases: "svg diagram", "draw a figure", "visual for docs", "explain this gesture visually", "before/after diagram".
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 a raster logo (PNG/JPG/WebP/screenshot) into a clean minimal SVG with edge smoothness as the primary hard gate and IoU optimized as high as reasonably possible without a fixed global threshold, then into a choreographed logo animation delivered as standalone JS-rendered HTML, applying Disney's 12 animation principles. Use when asked to animate a logo, build a logo reveal / splash screen / brand intro, convert a logo image into animated SVG or HTML, add motion to a vectorized mark, or create loading/idle/hover motion for a brand mark. v2: also handles self-crossing draw-on choreography (split-fill, exact easing subdivision, tip glint), closed variable-width ribbon fitting, and quantitative motion QA (easing probe, ink-delta continuity sweep).
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。
Auto-ingest a geospatial file (GeoJSON, Shapefile, GeoPackage, KML/KMZ, FlatGeobuf, CSV-with-geometry) into a PortalJS portal on the user's own machine, with no server. Normalizes CRS to EPSG:4326, derives a PMTiles render tier and a GeoParquet query tier, pushes all three artifacts to Cloudflare R2 via Git LFS, and appends one dual-tier datasets.json entry the showcase auto-renders. Use when the source is a vector geo format that needs a map or spatial-query view.
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A Python library for reading, writing, and analyzing geospatial vector data; use it when you need spatial operations (buffer/overlay/join), CRS reprojection, or map visualization on formats like Shapefile/GeoJSON/GeoPackage or PostGIS.
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 the user wants to provision infrastructure or third-party services using Stripe Projects. Triggers: "I need a database", "set up auth", "add caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.
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。
Milvus distributed vector database configuration for large-scale RAG applications
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。
Weaviate vector database setup with GraphQL queries and hybrid search
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。
Qdrant vector database with filtering, payloads, and quantization support
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。
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
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。
Pinecone vector database setup, configuration, and operations for RAG applications
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。
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
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。
Chroma local vector database setup and operations for development and production
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。
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity 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。
Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.
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 efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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 Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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。