「summarization」の検索結果

The official Brave Search MCP server integrates the Brave Search API with AI assistants, providing comprehensive web search, local business search, image search, video search, news search, and AI-powered summarization capabilities through the Model Context Protocol.

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Transcribes audio and generates auto-chapters with summaries using AssemblyAI's /v2/transcript endpoint with auto_chapters=true. Extracts key topics, sentiment analysis, and content safety labels via AssemblyAI SDK.

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Use ManagedCode.MarkItDown when a .NET application needs deterministic document-to-Markdown conversion for ingestion, indexing, summarization, or content-processing workflows.

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Transform Markdown or text specifications into human-readable HTML documents with summaries, reordered structure, visual explanations, diagrams, charts, and traceability to the source. Use when asked to convert a spec, requirements document, API spec, product spec, design doc, or technical document into readable HTML, especially when the user wants summarization, restructuring, diagrams, graphs, images, or visual aids rather than a literal Markdown-to-HTML conversion.

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Apply WIT (Writing Is Thinking) as a human–LLM collaborative scientific reasoning skill for scientific question formulation, finding-driven research planning, next-experiment selection, Results or Discussion review, claim–evidence and reviewer stress tests, manuscript logic audits, deadline closure, and researcher growth. Use when the user invokes WIT or asks for these question-driven scientific reasoning workflows; do not use for generic copyediting, summarization, or literature search alone.

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Use this skill when tool outputs exceed 2000 tokens, tasks span multiple conversation turns, sub-agents need to share state, context window is bloating, plans need to persist across summarization, terminal/log output needs selective querying, or when user mentions "offload context", "dynamic context discovery", "filesystem memory", "scratch pad", "reduce context bloat", or "just-in-time context loading".

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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Use when needing global or relational understanding of codebases or knowledge corpora. Keywords: GraphRAG, knowledge graph, entity extraction, community summarization, graph traversal, Microsoft GraphRAG.

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Manage LLM context budgets—prioritization, summarization, compaction, and what to load vs reference. Use for long sessions, large repos, or multi-doc tasks.

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Condense long reports, documents, or data exports into structured summaries with key findings, implications, and recommended actions. Use when the work is distilling dense content for busy readers. Do not use for original analysis, strategy formulation, or when the source material needs expert interpretation beyond summarization.

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Write one-page executive briefs and decision memos that turn messy inputs into a concise recommendation, options, risks, decision ask, and next steps. Use when leaders need a fast read before approving budget, policy, vendor, headcount, or strategy changes. Do not use for long-form reports, slide design, or generic summarization without a decision.

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Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints

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Manage LLM context windows deliberately — budgeting, prioritization, summarization, and retrieval instead of dumping everything in. Use when prompts grow long, costs rise, or the model loses track of key details.

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Keep long conversations coherent — summarization checkpoints, entity tracking, topic stacks, and graceful context handoffs. Use when dialogues run long or span sessions and the agent keeps losing the thread.

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Shortest English alias for content-summary / content-search-summarization. Use this to search public content platforms, rank top results, and summarize them with links.

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Short alias for content-search-summarization. Use this to search public content platforms, rank the top relevant items, and summarize them with links.

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Use this skill when asked to search public content platforms such as Bilibili or YouTube, pick the top relevant results for a topic, and summarize each item with links and brief overviews. Uses Runtime-aware routing — opencli first, fetch second, browser last.

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Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

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Native video analysis using Google Gemini API. Upload and analyze video files — describe scenes, extract text/UI, answer questions about content, transcribe speech, identify objects and actions. Use when: (1) User sends a video file and wants it analyzed, (2) Video summarization or description needed, (3) Extracting text, UI elements, or information from screen recordings, (4) Answering questions about video content, (5) Comparing multiple videos, (6) Analyzing tutorials, demos, or walkthroughs.

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Connect AI agents to the 2020117 decentralized network. Register, post to timeline, trade compute via NIP-90 DVM jobs (text generation, translation, summarization, image/video/speech), pay with Lightning, build reputation through Nostr zaps and Web of Trust. Use when building or operating AI agents that need to communicate, exchange capabilities, or transact on an open protocol.

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Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot

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Synthesizes multiple case summaries into one unified Case Summary Report. Triggers when the user has two or more case summaries, document analyses, or memoranda and needs them consolidated into a single report with patterns, contradictions, and gaps identified.

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Generates structured bail hearing summaries from transcripts and case documents, extracting charges, arguments, conditions, and rulings. Use when summarizing bail hearings, pretrial detention decisions, bond hearings, or release condition orders.

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Produces privilege-safe U.S. corporate legal billing summaries from time and expense data. Converts entries into client-friendly narratives with categorized hours, rates, expenses, and milestones while enforcing engagement-letter rules and ethical billing safeguards. Use when asked to create a billing summary, invoice narrative, timekeeper breakdown, expense summary, or fee narrative.

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。