データ分析、機械学習、LLMアプリ、プロンプト設計（8,870 件）
概要と使いどころ
Query Windsor.ai business data across marketing, sales, CRM, ecommerce, finance, and analytics connectors. Use when users need dashboards, reports, data visualization, schema exploration, or connector-backed test data from Windsor.ai.
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。
Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.
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。
Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis outputs.
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 standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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 project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.
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 these skills when you need to explore the database structure, discover schema objects like tables and graphs, and execute custom SQL queries to interact with your 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。
Use for Desktop Commander MCP capabilities — persistent shells and REPLs, long-running processes, filesystem beyond the workspace, structured files (.xlsx, .docx, .pdf, images) and large local data files such as CSVs, ripgrep search at scale, SSH, or cross-turn state.
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。
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and 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。
Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.
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 run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.
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 standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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 project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.
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。
Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically - including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.
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。
Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis outputs.
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。
Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).
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。
Compress any conversation into a portable JSON context graph for handoff to a new chat or LLM. Trigger on: "compress this chat", "save context", "new chat", "hand off", "transfer context", "too many tokens", "start fresh", "export conversation", "context graph", "continue in new session". Also trigger proactively when the user mentions starting over in a long session.
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。
Data migration and synchronization to SurrealDB from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL. Full and incremental CDC sync. Part of the surreal-skills collection.
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。
Pre-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. Use when asked to "review this PR", "code review", "pre-landing review", or "check my diff".
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。
Turn any installed skill (gstack, custom, third-party) into an ATO agent the user can summon into war-rooms. Reads a SKILL.md, extracts the persona, strips runtime boilerplate, and writes an agent file at `.claude/agents/<slug>.md` (project-scoped) or `~/.claude/agents/<slug>.md` (global). Prompts for a model roster (primary + 1-2 alts) so cross-family dispatch in war-rooms produces real disagreement. Companion to `ato-warroom` — that skill summons agents this skill creates. Use when asked "turn this skill into an agent", "register X as a war-room agent", or when scoping a new persona before a war-room runs.
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。
Schedule a prompt to run later, once or on a repeating cron expression.
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。
TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.
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。
TanStack Table best practices for building headless, type-safe data tables in React with sorting, filtering, pagination, row selection, and column management. Use when building data grids, implementing client-side or server-side table features, defining column structures, managing table state, or optimizing table rendering performance.
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。
TanStack Router best practices for type-safe routing, file-based routing, data loading, search params, and navigation in React. Use when building React SPAs with complex routing, implementing type-safe search params, setting up route loaders, integrating with TanStack Query, or configuring code splitting and preloading.
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。
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
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。