「retrieval」の検索結果

Build a bookmark system you'll actually use: capture, tagging, retrieval, and regular pruning. Use when bookmarks pile up unfindable or you keep losing useful links.

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Build data-centric LLM applications with indexing and retrieval frameworks — document ingestion, indices, query engines, and RAG pipelines. Use when the core problem is getting the right data in front of the model.

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Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.

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Design memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.

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Architect persistent, searchable memory for agents — episodic, semantic, and procedural stores with retrieval, consolidation, and forgetting policies. Use when designing how an agent remembers across sessions.

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Build AI chatbots end to end — conversation design, persona, retrieval grounding, escalation, and deployment. Use when creating a chatbot for support, sales, or internal assistance.

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Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.

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Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

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Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.

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Convert unstructured documents into LLM-ready structured data. Supports PDF, Word, PPT, and images; extracts paragraphs, formulas, tables, charts, and other elements in one step; generates up to 8 levels of headings; and outputs Markdown organized in reading order. Useful for field extraction, comparison and validation, knowledge retrieval, and intelligent Q&A.

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Professional LaTeX writing assistant. Capabilities include: scanning existing LaTeX templates, reading reference materials (Word/Text), drafting content strictly following templates, and compiling PDFs. Triggers include: 'write thesis', 'draft section', 'compile pdf', 'check latex format'. Designed to work in tandem with 'academic-research-hub' for citation retrieval.

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Design and deploy copilot-style assistants on Azure with secure model access, tool integration, and observability. Use this skill when users ask to build assistants, agents, or copilot workflows in Azure. Covers orchestration, retrieval, safety guardrails, and production deployment.

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案例检索 Skill — 面向法律从业者及普通用户的裁判文书智能检索工具。当用户需要检索裁判案例、查找类案裁判规则、按案由/法院/时间筛选案例、或上传文件进行案例匹配时使用。触发词包括："查一下XX的案例"、"找关于XX的判决"、"XX法院的判例"、"类案检索"、"有没有类似的案例"、"XX案由的裁判规则"等。

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案例检索与类案分析大师：帮助法律工作者掌握案例检索方法、制作类案检索报告 核心能力： 1. 类案识别与判断标准 2. 检索平台使用与检索方法 3. 关键词设计与优化 4. 类案筛选与排序 5. 类案检索报告制作 适用场景： 1. 诉讼案件类案检索 2. 法律问题研究 3. 裁判规则梳理 4. 类案检索报告制作

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类案检索报告生成 Skill — 面向律师、法官助理及法务人员的类案检索全流程自动化工具。基于待决案件信息，通过调用得理案例检索 API 完成案件拆解、检索执行、类案筛选、比对分析与规则提炼，最终输出一份可直接向法院提交或供内部策略研判使用的标准化《类案检索报告》。当用户提出以下需求时触发：帮我生成XX案由的类似案例报告、检索XX报告、做一个类案检索报告、我要向法院提交类案检索报告

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Retrieve up-to-date library, framework, and project documentation using scripts + MCP tools (Context7, Context Hub) with intelligent fallback. Use this skill whenever the user or agent needs documentation for any library, framework, API, SDK, or internal project spec. Triggers on "docs for [X]", "how does [library] work", "find documentation", "API reference for", "look up [feature] in [library]", "latest docs", "what's the API for", "find our [internal spec]", or any request that requires current, accurate documentation rather than relying on training data. Always prefer this skill over raw WebSearch for documentation retrieval — it returns structured, context-efficient results.

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code-review-graph (CRG) MCP wrapper — token-efficient context retrieval via AST knowledge graph. Exposes 4 core tools from CRG.

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Persistent, adaptive vector memory for agents on a ruOS desktop — a Rust-native vector database with local semantic embeddings, HNSW retrieval, and graph relationships. Use to remember and recall across sessions with no database server or API key.

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Openapi utility services - real-time currency exchange rates, HTML-to-PDF conversion (renders JavaScript), .it domain registration/management, and managed RAG (retrieval-augmented generation over your documents). Use for these one-off utility tasks.

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.

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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

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。