「summarize」の検索結果

Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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Generates English pull request titles and bodies by comparing the current branch with main. Use when the user asks to draft PR content, summarize branch changes, or produce a PR title/body from git diff.

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Automatically fetch YouTube video transcripts, generate structured summaries, and send full transcripts to messaging platforms. Detects YouTube URLs and provides metadata, key insights, and downloadable transcripts.

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Podcast knowledge workflows powered by Podwise CLI: search podcasts and episodes by keyword, monitor followed shows for new releases, find popular episodes, ask questions and extract insights from transcript content, process Podwise episode URLs, YouTube videos, Xiaoyuzhou links, and local audio or video files to retrieve transcripts, summaries, chapters, Q&A, mind maps, highlights, and keywords — plus catch up on your backlog, refine your listening taste, generate weekly recaps, export episode notes to PKM tools, research topics across podcasts, debate episode ideas, and generate language learning cards. Use when the user wants to find, summarize, transcribe, or extract insights from any podcast or audio content, or manage their listening library.

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Summarize authored commits over a user-specified time period into a concise update

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Fetch and summarize review comments from the active pull request

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Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

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会議テキストやメモから構造化された議事録を自動生成するスキル。 「議事録をまとめて」「会議メモを整理して」「アクション項目を抽出して」等のリクエストで発動。

Summarizes recent git changes for context recovery after session breaks. Use when resuming work after a gap or handing off to another session.

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Use when a working session is ending, the context window is nearly full, work needs to pass to a teammate or a fresh session, or the user says "hand this off", "write this up before we lose it", "I'm starting a new session", or "summarize where we are". Produces a handoff document that lets the next session resume without re-reading the transcript.

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Use when a coding session is burning context fast, output is drowning in noise, the model keeps forgetting earlier decisions, or the user asks to reduce tokens, stop the verbose narration, work in a huge repo, or make a long session survive. Covers what to read, what to summarize, what to write to disk, and when to start fresh.

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Use when starting or inheriting a radiology research project and need to know what it is. Classifies the project type, summarizes its current state, lists missing inputs, recommends next steps and scaffolds lightweight project memory files.

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Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.

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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use for any "make slides", "create a deck", or "slides from this document" request.

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This skill should be used when the user asks to "validate", "reindex", "repair registries", "check links", "run the continuity, pacing, clue, voice, or name checks", "count words", "summarize a story project", "import an existing manuscript", "export a manuscript", "run a build", "generate a diagram", "record revision-pass status with `story passes`", "run the story CLI", or wants deterministic maintenance on a Story Skills markdown project. It runs the CLI and reads its output; NOT for judging a finding or revising the story to fix it (use revision-continuity for continuity errors and revision passes, plot-structure for pacing, voice-style for voices and prose, genre-craft for clues and fair play), or for sharing a review copy with readers (use feedback-triage).

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Use this skill when the user requests current information, documentation, coding examples, or web research beyond the knowledge cutoff. Queries Google's AI Search mode to retrieve comprehensive AI-generated overviews with source citations from 100+ websites. Returns markdown with inline footnoted references [1][2][3]. You will receive a detailed Markdown file with references and information summarized directly from Google's AI search. Ideal for you to get new information and clues for further research.

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Build work collaboratively in reviewable pieces, handing each piece back for review before starting the next, so the person stays in the lead and steers while the work happens instead of reviewing a finished result. Use when someone says to pair with them on something, asks to collaborate rather than direct, wants to review as it goes, or wants to guide the work piece by piece — on code, on a design decision, or on writing. For a test-first build it runs tdd, for restructuring it runs refactor, for an interface contract it runs design-an-api, and for plan work it runs iterative-plan-review or plan-implementation, each collaboratively; invoke any of those directly instead to run it straight through without pausing. Does not pace someone through code that already exists and builds nothing — use code-walkthrough. Does not explain, summarize, or research something instead of producing it — use code-overview or research.

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Analyze web page content, structure, and layout to understand what a page contains and how it is organized. Trigger when the user asks to: analyze a page, understand page structure, inspect a website, summarize page content, examine page layout, review a web page, or describe what is on a page.

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Summarize document text and identify its main claims, evidence, and unanswered questions.

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Generate a PR description following the project's pull_request_template.md. Analyzes all commits on the current branch vs main, summarizes changes, and outputs a ready-to-paste PR body. Invoke with /pr-summary.

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Generate comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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Automatically generate user-facing release notes from git commits, pull requests, changelogs, and code changes. Use when preparing software releases, creating version announcements, documenting what changed between versions, or communicating updates to users. Analyzes commit messages, PR descriptions, and code diffs to produce categorized markdown release notes organized by New Features, Improvements, Bug Fixes, and Breaking Changes. Focuses on user-visible changes while filtering out internal/technical details. Triggers when users ask to generate release notes, create changelog, write version announcement, or summarize changes for a release.

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。