「extract」の検索結果

Extracting and structuring design context from Figma via MCP Server for downstream implementation agents. Use for Figma-to-code bridging or Code Connect management.

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。

Verifying spec compliance: extracts ACs from specs, adversarially checks conformance, generates BDD scenarios and traceability matrices. Use when impl must be proven to match a PRD/SRS/AC.

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。

Extract text from PDFs/scans (pymupdf, marker-pdf).

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。

Resolve TikTok video URLs to return no-watermark/watermarked download addresses, playback addresses, and cover image addresses for saving promotional video assets or offline analysis. Trigger when users mention TikTok video download, TikTok watermark-free download, TikTok video save, download TikTok promotional video, TikTok no watermark video, TikTok video download, download TikTok video, no watermark TikTok video, save TikTok video, TikTok video link resolution. Even if the user does not explicitly mention "Nexscope", trigger this skill whenever their need involves extracting downloadable/playable video addresses from a TikTok video link.

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 this when creating, editing, executing, or reviewing Jupyter notebooks — including cell structure, kernel management, extracting reusable logic to src/, and ensuring notebooks are restartable.

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。

Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

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。

Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.

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。

Plan how to build with You.com. Use when adding web search, content extraction, or research to an app or agent: finds the right You.com API, MCP server, SDK, or docs path (for example Vercel AI SDK, LangChain, Python SDK, or REST) through the `you-discover` tool and Docs MCP.

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 for Korean (Hangul) fan translations of retro console or PC games, including ROM or disc analysis, text-engine reverse engineering, Hangul fonts and custom encodings, script extraction and reinsertion, code hooks, reproducible product builds, and emulator verification. Apply it to new investigations and follow-up work in existing Korean-patch projects. 레트로 게임 한글화·한글패치·한글 패치·ROM 번역의 신규 조사와 기존 프로젝트 후속 작업에 사용한다.

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。

Post-epic review to extract lessons and assess success. Use when the user says "run a retrospective" or "lets retro the epic [epic]"

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 user requests diagrams, flowcharts, sequence diagrams, class diagrams, component diagrams, ER diagrams, architecture charts, or visualizations — including generating a diagram from existing source code, or rendering/extracting PlantUML embedded in a Markdown file to images (e.g. preparing docs for Confluence/Notion). Also use proactively when explaining systems with 3+ components, APIs, data flows, or class hierarchies. Generates .puml files and exports to PNG/SVG via Kroki API (no local install required).

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 this skill when a user needs to inspect, score, repair, or regression-test academic paper formatting in DOCX files, especially thesis, journal, or conference documents with a separate format specification. The skill guides Codex through local-first document handling, rule extraction, formatting repair, report review, and contribution-safe validation.

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 the user wants a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

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。

既存コード（DDDを前提としていない）を分析し、ドメインモデル（集約、ローカルエンティティ、 値オブジェクト、ドメインサービス）を擬似コード付きで提案するスキル。プロパティ・メソッド・ 不変条件・集約境界を含む設計を提案する。対象言語: 言語非依存。 トリガー：「既存コードからドメインモデルを抽出して」「このコードをDDDで再設計したい」 「ドメインモデルを提案して」「集約を見つけたい」「DDDに移行したい」 「既存コードを分析してドメインモデルを作って」「レガシーコードをDDDに」 といった既存コード分析・ドメインモデル抽出リクエストで起動。

Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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。

Professional PDF financial plan generator. Scans current working directory for FINANCE-*.md files, extracts scores and financial data, then runs the bundled Python ReportLab script to produce a polished, client-ready PDF with cover page, Financial Health Score gauge, score breakdown, cash flow, debt, retirement, portfolio, and prioritized action plan. Output: FINANCE-PLAN.pdf.

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。

Triage long email threads for an insurance context, identify the most recent actionable message, extract key context, infer intent (e.g., missing documents, follow-up/reminder, payment, status), and decide whether to reply or take further action.

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。

Processes insurance renewal documentation for brokers and underwriters. Extracts policy details, compares renewal terms against expiring policies, identifies coverage gaps, and generates renewal summaries with premium comparisons and recommendation narratives.

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。

Produces structured summaries of insurance claims correspondence including coverage letters, reservation of rights (ROR), denial letters, and claim file documents. Extracts policy details, coverage positions, claims chronology, settlement posture, and liability exposure. Use when summarizing claim files, coverage disputes, ROR letters, denial letters, or multi-document insurance correspondence threads.

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。

Extrae datos estructurados de póliza de seguros argentina — condiciones, exclusiones, franquicias, obligaciones

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。

Consulta sistemas internos de Libra para obtener el documento de póliza a partir de datos del asegurado o del vehículo

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。

Formally verify construction payment bonds by extracting fields from bond documents and cross-referencing against Treasury Circular 570, state licensing, and underwriting limits. Use whenever the user uploads a payment bond PDF, mentions bond verification, asks "is this bond valid", references AIA A312, or needs to verify a surety company's authority to write bonds. Also trigger on "verify bond", "check surety", "payment bond", "performance bond", or "Circular 570."

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。

First step of deploying a new warp route (steps 1–9). Reads a Linear ticket to extract token details and chain configuration, looks up mailbox addresses from the local registry, generates the deploy.yaml, runs the deploy, and optionally runs a send test. Follow up with /warp-deploy-update-owners.

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。

Build a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync data".

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。