「summarize」の検索結果

Produces comprehensive summaries and insights about legacy codebases to help understand unfamiliar code. Use when onboarding to a new project, planning refactoring efforts, assessing code for acquisition/migration, or generating documentation for undocumented systems. Analyzes architecture, dependencies, code quality issues, and test coverage. Creates high-level overviews with architecture diagrams, key components, entry points, and actionable insights for understanding and improving legacy code.

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Generate comprehensive API documentation from repository sources including OpenAPI specs, code comments, docstrings, and existing documentation. Use when documenting APIs, creating API reference guides, or summarizing API functionality from codebases. Extracts endpoint details, request/response schemas, authentication methods, and generates code examples. Triggers when users ask to document APIs, generate API docs, create API reference, or summarize API endpoints from a repository.

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Summarize sprint status and surface risks. Use when the user says "check sprint status" or "show sprint status"

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Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

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Combines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.

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Read a GitHub issue, summarize it, and create a branch for it

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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.

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Summarize text using extractive, abstractive, hierarchical, and multi-document techniques, producing concise outputs at configurable detail levels. Use when the user requests summarization or provides relevant inputs for this workflow.

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Summarize the architecture of code generated by a single retort run. Produces module-level structure, interfaces, and control flow in a form suitable for cross-run comparison — not a full codebase-summary.

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.

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Use when the user wants to validate, lint, or audit agent skill files (SKILL.md). Use when they say "validate these skills," "check this repo's skills," "lint SKILL.md files," or "audit skills in [repo URL]." Run skill-check locally or against a cloned GitHub repo and summarize findings.

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Review PR comments from GitHub. Fetches inline review comments and issue comments, classifies by blocking vs non-blocking, and summarizes actionable items.

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Summarize each day's Git activity into a concise daily work log, for a single date or a range. Uses the current repository, optional configured work projects, or paths named in conversation; configuration is never required.

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Summarize a contributor's Git history, a provided work log, or both into a concise, review-friendly self-evaluation for quarterly, semi-annual, or promotion cycles.

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生成一份对外可分享、脱敏的 AI-Native 开发者 README。 量化展示我对 Claude Code + Codex CLI + Kiro (AWS) + Trae (ByteDance) + Gemini Antigravity (Google) + Cursor 的使用深度、AI 协作风格、 项目与领域分布、兴趣主题，以及与 GitHub 提交的产出关联。 Trigger when the user says: "生成我的 AI 档案" / "做一份 AI-native README" / "分析我的 Claude / Codex / Kiro / Trae / Antigravity / Cursor 使用情况" / "总结我的 AI 使用" / "生成 AI 月度报告" / "按月份分析我的 AI 编码" / "分析 2026-05 的 AI 使用" / "build my AI usage profile" / "build my monthly AI coding report" / "analyze my AI usage for May 2026" / "summarize my Claude / Codex / Kiro / Trae / Antigravity / Cursor history" / "生成开发者画像". 全程本地、只读、默认匿名、不上传任何数据。

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Analyze spreadsheet data, generate insights, and create visualizations. Use when a user asks to analyze data, explore a dataset, find trends, generate statistics, create charts from CSV or Excel data, summarize data, or answer questions about tabular data.

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Generate release notes and changelogs from git commits, feature lists, or project updates. Use when a user asks to generate a changelog, create release notes, summarize recent changes, draft a CHANGELOG entry, or prepare release documentation from git history.

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AssemblyAI is a hosted speech-to-text API that transcribes audio and video files or live streams and adds speaker labels, sentiment, entity detection, PII redaction and LLM analysis of the transcript. Use when a user asks to transcribe a recording, label who said what, analyze call sentiment, redact personal data, stream live transcription, or summarize and question a transcript with LLM Gateway (the replacement for LeMUR).

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Writes the App Store "What's New in This Version" text for an app update from the repository's commit history: finds the range since the last shipped version, separates changes users can notice from internal work, drafts plain-text notes within Apple's 4,000-character limit and review guidelines, and returns a trace from every sentence back to its commits. Use when someone asks to "write release notes for the App Store", "generate What's New from git", "summarize changes since the last tag for the store listing", or needs the same notes cut to Google Play's 500 characters.

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Read and summarize text-based file types only. Prefer read_file for text formats; use execute_shell_command for type detection when needed. PDF/Office/images/archives are handled by other skills.

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Look up the latest news for the user from specified news sites. Provides authoritative URLs for politics, finance, society, world, tech, sports, and entertainment. Use browser_use to open each URL and snapshot to get content, then summarize for the user.

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Read and extract content from PDF files — text, tables, metadata, and images. Use when asked to read a PDF, extract text from a PDF, summarize a PDF, analyze a PDF document, get tables from a PDF, or check PDF metadata. Also triggers on "open this PDF", "what does this PDF say", "parse PDF", "PDF to text", or when a .pdf file path or URL is provided.

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。