データ・AIのスキル

Build MotherDuck analytics into customer-facing applications with tenant isolation, backend routing, and serving APIs.

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Roll out governed MotherDuck analytics to internal teams, choosing trusted datasets, access boundaries, and 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。

Create, consume, or manage MotherDuck data shares, including audience grants, table filters, and refresh policy.

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。

Load files, object storage, dataframes, or external databases into MotherDuck using an appropriate bulk ingestion path.

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 ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication.

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。

Create, edit, publish, share, or embed MotherDuck Dives using their React and SQL runtime.

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。

Design or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.

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 or implement migrations to MotherDuck with SQL translation, source-to-target validation, cutover, and rollback.

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。

Look up or repair DuckDB SQL syntax and verify MotherDuck-specific command and feature support.

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。

Discover MotherDuck databases, tables, columns, shares, and sample data to understand an available dataset.

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。

Write, execute, or optimize analytical DuckDB SQL against MotherDuck 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。

Read a Facebook Page and its audience from real data. Pull any Page's public stats (yours or a competitor's): followers, likes, categories, intro, websites. And pull the commenters on a public Page post, since Facebook hides the reactor and liker roster and shows counts only, so commenters are the signal. Powered by Apify, no login. Triggers on "analyze my Page", "competitor Page stats", "who is commenting", "read the comments", "audience insights". Not for writing Page posts (use fb-post-writer).

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 whenever the user wants to find, shortlist, vet, or enrich US B2B law firms — corporate, IP/patent, M&A and securities, employment, commercial litigation, regulatory/compliance, data privacy/cyber, real estate, and tax. Triggers on "find three boutique IP law firms in California", "shortlist M&A counsel for a Series-B fundraise", "patent prosecution for our hardware startup", or "pull contact info for these 10 law firm domains", even when described indirectly (outside counsel, cap-table review, GDPR/SOC2 oversight). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip personal/consumer legal services where the user is the end client (divorce, personal injury, criminal defense, family law, estate planning, wills) — the catalog is B2B-only. Also skip in-house GC hires, "is this NDA enforceable" DIY questions, non-US firms, individual freelancers.

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。

The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

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。

Query foreign LLM for chat. Use this skill if a foreign LLM like OpenAI ChatGPT, Google Gemini, DeepSeek or xAI Grok should be queried with a single chat message.

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。

将 SRT 字幕文件转换为结构化 JSON 数据。 触发场景： (1) 需要解析 SRT 字幕文件 (2) 需要将字幕转为 JSON/结构化格式 (3) 需要提取字幕时间码和文本 (4) 视频字幕数据处理和分析 (5) 生成字幕纯文本或统计信息

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。

Handle PR review feedback - understand reviewer comments, research their concerns, and help the user address them. The LLM explains what reviewers want and why; the user decides how to respond and writes the code. Use when you receive review comments on a submitted PR. Not for opening the PR in the first place, which is oss-submit-pr. If the feedback is a failing CI job, use oss-debug-ci.

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 to publish or schedule a LinkedIn post through Publora: text, a multi-image grid, a video, a PDF document, or @mentions. Not for analytics, reactions, comments or reshares (use linkedin-analytics), and not for other platforms.

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Analyze LinkedIn performance and manage engagement through Publora. Use when the user asks how a LinkedIn post or account performed (impressions, reach, reactions, comments, reshares, follower growth) or wants to react, comment, reshare or resolve an @mention. Statistics run over the REST API, engagement over MCP tools. Not for creating posts (use linkedin-post).

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。

Reverse-engineer a supplied video into a shot-by-shot generation specification, displayed evidence frames, and paired Chinese-English prompt packages. Use for requests such as 视频反推提示词, 视频转提示词, 从视频生成提示词, 视频逆向提示词, 逐镜头反推, 分镜拆解并生成提示词, 反推视频 prompt, or video-to-prompt; also use for Veo, Sora, Kling, Wan, generic prompt adaptation, and bounded generate-compare-optimize loops. Do not use for ordinary video summaries or basic transcription.

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Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.

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。

Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.

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Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.

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。

Fetches tweets from Twitter List, scores them using data metrics and AI analysis, and generates topic recommendation reports for content creators.

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。