データ分析、機械学習、LLMアプリ、プロンプト設計（8,876 件）
Pull GA4 reports, traffic data, and insights from the Google Analytics Data API. Use when asked about website traffic, user behavior, acquisition channels, conversions, or audience segments. Trigger phrases: "google analytics", "GA4", "traffic report", "analytics data", "user acquisition", "engagement metrics", "conversion tracking", "audience segments", "page views", "sessions".
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 demand generation strategy and multi-channel campaigns. Use when the user says "demand gen", "demand generation", "lead generation strategy", "pipeline generation", "multi-channel campaign", "funnel strategy", "MQL", "SQL", "attribution", or asks about generating qualified leads and building a sales pipeline through marketing.
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。
Provides multi-school trading Q&A, chart/OHLCV analysis, annotation, and fresh-market workflows covering ICT/SMC, ChanLun, Wyckoff, Price Action, Order Flow, VSA, and Elliott Wave. Use for trading concepts; capability-discovery questions about available analysis lenses, Schools, models, modes, or data sources; attached charts; pasted OHLCV; chart annotation; or asset-plus-timeframe requests across crypto, stocks, or forex. Defaults unselected market analysis to strict ICT/SMC, honors explicit School/source selectors, and fails closed when no native analyzer exists. Phase 1 comparison is Q&A-only; fetch fresh Mobius Quant API data only after the capability gate.
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-run setup. Detects configured API keys, points to the local setup-keys script for secure key entry, and shows sample prompts scaled to available tools.
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。
Active credit card transfer bonuses from Amex, Chase, Capital One, Citi, Bilt, and Rove. Weekly-refreshed data with confidence markers. Use when pricing an award booking that involves a points transfer or deciding whether to wait for a better bonus.
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 files and get detailed metadata including size, line counts, modification times, and content statistics. Use when users request file information, statistics, or analysis without modifying files.
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。
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
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 cost-safe geospatial SQL for BigQuery, DuckDB, Snowflake, Wherobots, and Postgres and/or render results on an interactive Dekart map.
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。
Guides state management implementation using MobX + tsyringe. Triggered when handling data models, state logic, or Store architecture.
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 known webpage or execute a discovered workflow or data-provider capability. Use for page content or structured results once the URL or tool is selected.
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 web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.
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 a direct path to structured data through ready-made workflows, data APIs, and indexes. Follow the search skill to discover and inspect tools, then the scrape skill to execute them.
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。
Autonomously navigate websites and extract structured data across pages. Use when the task requires navigation or no suitable ready-made workflow or data provider covers it.
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 and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
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 logging analytics events, setting user properties, configuring default event parameters, building funnels, or adding screen-view tracking.
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 setting up firebase_ai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI 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。
Use when scaffolding a project, refactoring into layers, creating view models/repositories, configuring dependency injection, or implementing unidirectional data flow (MVVM).
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 writing switch statements, refactoring if-else chains, creating data classes, choosing records vs classes, destructuring values, or modernizing pre-Dart-3 code.
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 setting up Data Connect, writing GraphQL queries/mutations, configuring generated SDKs, handling offline, or applying security rules.
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 preloading fonts, asset/network images, Lottie/Rive animations, local JSON/config, warming initial API data, or optimizing Flutter Web startup.
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。
Daily cross-platform rankings of AI agent skills (skills.sh, ClawHub, Tencent SkillHub, GitHub, X/HN/Bluesky). Use when the user asks which agent skill to install, what is trending, whether a skill is popular or trusted, or wants to compare skills. Read-only public CSV data; it recommends and links, it does not install anything.
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 and transform CSV data using bash tools
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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。
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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。