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Found 278 Skills
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Identify key themes and concerns raised by analysts during earnings calls, including specific analyst attribution and topic categorization.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
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#.
Using DuckDB with remote cloud storage via HTTPFS extension, fsspec, and Delta Lake integration. Covers S3, GCS, Azure, and S3-compatible endpoints.
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.