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Found 278 Skills
Extract and analyze YouTube video content (transcripts + metadata). Use when the user explicitly requests to analyze, summarize, extract wisdom from, or get context from a YouTube video. Supports wisdom extraction, summary, Q&A prep, key quotes, and custom analysis. Does NOT auto-trigger on YouTube URLs - only when analysis is explicitly requested.
Apache Spark distributed computing. Use for big data processing.
Inject knowledge into JSON data context.
Use when lINQ query and method syntax, deferred execution, and performance optimization. Use when querying collections in C#.
Consulta riesgo pais de Argentina con serie historica desde Anduin API. Usar cuando el usuario pida "riesgo pais argentina", "ultimo riesgo pais", "serie historica de riesgo pais", "riesgo pais por fecha o periodo", o "evolucion del riesgo pais".
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Parse, search, analyze, and ingest LinkedIn GDPR data exports. This skill should be used when working with LinkedIn data — searching messages, analyzing connections, exporting to Markdown, or ingesting into RLAMA for semantic search. Requires a LinkedIn GDPR data export ZIP file.
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.
Combining IoT sensor data using algorithms like Kalman filters for improved accuracy and reliability
This Skill supports screening qualified stocks based on stock selection criteria (such as market indicators, financial indicators, etc.); it allows querying stocks, listed companies within specified industries/sectors, as well as component stocks of sector indices; it also supports related tasks such as stock, listed company, and sector/index recommendations, avoiding the use of outdated information by large models during stock selection.