501 件 ・ 関連度順
概要と使いどころ
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
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。
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
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。
Search and progressively read open-access academic papers through DeepXiv. Use when the user wants layered paper access, section-level reading, trending papers, or DeepXiv-backed literature retrieval.
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。
Communications-domain literature review with Claude-style knowledge-base-first retrieval. Use when the task is about communications, wireless, networking, satellite/NTN, Wi-Fi, cellular, transport protocols, congestion control, routing, scheduling, MAC/PHY, rate adaptation, channel estimation, beamforming, or communication-system research and the user wants papers, related work, a survey, or a landscape summary.
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 deploying KubeEye for cluster inspection, creating InspectRule/InspectPlan resources, or retrieving inspection results. Covers InstallPlan-based deployment, OPA/PromQL/FileChange/Sysctl/Systemd/NodeInfo/FileFilter/ServiceConnect/CustomCommand rule types, and report retrieval. Always consult this skill when the user mentions KubeEye, cluster inspection, InspectRule, InspectPlan, or inspection reports.
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 configuring Jenkins in KubeSphere DevOps, including agent customization, LDAP/OIDC integration, build artifact retrieval, or troubleshooting Jenkins issues
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。
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
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。
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot 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。
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
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。
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance 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。
Required when terminal_* tools are available. Explains foreground execution, outer collect_result handles, promoted job IDs and their retrieval/cancellation, deadlines, output retention, platform shell selection, and structured editing when enabled.
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。
Route retrieval of Chinese medical guidelines, consensus statements, regulatory documents, drug information, safety notices, standards, and public-health materials through authoritative originals and verified fallback copies. Use when a canonical medical source is blocked, slow, unavailable, moved, paywalled, or difficult to parse, or when designing and auditing domestic medical-source fallback rules. Do not treat secondary summaries or reposts as independent clinical evidence.
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。
Manage design systems in Stitch using MCP tools. Includes retrieval of assets, creating/updating design systems in Stitch, and applying them to screens.
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。
Web information retrieval expert. ALWAYS invoke this skill when the user needs to search the web, fetch a URL, or access real-time information beyond training data. Do not guess or use stale knowledge — use this skill first.
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 professional documents using write_file when primary data sources fail, leveraging embedded domain knowledge instead of external retrieval
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 documents with write_file when retrieval tools fail, with explicit guardrails against task context drift
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。
Delegate file generation to shell_agent when direct tools fail, with systematic file location and retrieval
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。
Handle cascading data retrieval tool failures by falling back to embedded knowledge generation
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。
Submit compact RNAcentral API requests for RNA entry browsing, single-entry lookup, and cross-reference retrieval. Use when a user wants concise RNAcentral summaries
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。
Submit compact eQTL Catalogue API requests for association retrieval and documented metadata endpoints. Use when a user wants concise public eQTL Catalogue summaries
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。
Submit compact BioStudies and ArrayExpress API requests for free-text search and accession-based study retrieval. Use when a user wants concise BioStudies summaries
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。
Submit compact CIViC GraphQL requests for cancer variant interpretation schema inspection and targeted evidence retrieval. Use when a user wants concise CIViC summaries
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。
Submit compact ENCODE REST API requests for object lookups, portal-style search, and metadata retrieval. Use when a user wants concise ENCODE summaries
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 and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。