9,260 件 ・ 関連度順
Apply seaborn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses seaborn or its strengths fit the task.
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。
Apply risk metrics calculation in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses risk metrics calculation or its strengths fit the task.
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。
Apply sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.
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。
Apply pytorch geometric in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses pytorch geometric or its strengths fit the task.
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。
Apply sql optimization patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql optimization patterns or its strengths fit the task.
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。
Apply single-cell rna qc in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses single-cell rna qc or its strengths fit the task.
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。
Apply polars in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses polars or its strengths fit the task.
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。
Apply matplotlib in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses matplotlib or its strengths fit the task.
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。
Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.
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。
Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task.
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。
Apply experimental design in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses experimental design or its strengths fit the task.
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。
Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.
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。
Apply dask in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses dask or its strengths fit the task.
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。
Apply data visualization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data visualization or its strengths fit the task.
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。
Apply data context extractor in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data context extractor or its strengths fit the task.
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。
Apply dbt transformation patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses dbt transformation patterns or its strengths fit the task.
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。
Generate free images with Agnes Image 2.1 Flash through the Agnes API, including text-to-image, image-to-image, high-density visual prompts, URL results, and local downloads. Use when the user asks for 免费图片, 免费的图片模型, free image API, Agnes image, Agnes Image 2.1 Flash, free AI image generation, image-to-image via Agnes, or wants a free image model Agent skill.
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。
codeck entry point. Scans local files for materials, shows pipeline dashboard with diagnostic intelligence, guides user to the next step. Use when the user says "codeck", "new deck", "make a presentation", "make a deck", "new slides", "build a presentation", "export", "speech", "script", or wants to start or continue a presentation project. /codeck is the user-facing entry; legacy sub-skills are internal modules.
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。
Editor role. Reads local materials, asks narrative questions, plans story arc. Outputs $DECK_DIR/deck.md as the sole content source. Use whenever the user says "outline", "plan slides", "organize materials", "structure", "table of contents", "narrative", or wants to structure content into a presentation.
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。
Find current job postings on employer career pages and job boards. Use when the user asks who's hiring, seeks remote or local jobs, or wants openings matched to their role, skills, experience, or salary preferences.
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。
Export documentation portals, help centers and API reference sites into local markdown or JSON with Firecrawl browser, including pages that render with JavaScript, hide content behind collapsible navigation or paginate. Use when the user wants a docs site, help center or support knowledge base saved as files, even without an explicit request to scrape.
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 knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.
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。
Find and qualify new prospects with Firecrawl Alexandria people and company data: people by role, seniority, company, industry, size, location or technology, target accounts that match criteria, or the right contacts at companies, returned as a ranked, sourced table or CSV. Also extracts lead lists from a database or directory the user names, and refreshes a prospect list with only new matches. Use when the user wants a lead list, prospects, target accounts or the people to reach out to at companies that fit criteria. Not for enriching people or companies the user already supplied (use firecrawl-lead-research).
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。
Enrich and research the people and companies the user names or supplies with Firecrawl Alexandria data: background on a person, a professional profile URL to look up, whether someone still holds a role, missing columns in a table or CSV of people or companies, firmographics for company domains (industry, headcount, funding, location), and pre-call or meeting briefs. Use whenever the request is about specific named people or companies, even without the word enrich, since roles, headcount and funding change faster than training data. Not for finding new prospects (use firecrawl-lead-gen) or a company's published press, sales or support contacts (use firecrawl-business-contacts).
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。