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Found 278 Skills
Analyze tonality — key detection, chord progression, melody contour
Construct a business cycle model using leading and coincident indicators, and interpret two business cycle phases: Expansion (Risk-On) and Contraction (Risk-Off), and generate "Iceberg" and "Sinking" event signals based on the theory.
Analyze the BTC market using a custom momentum theory with nested multi-timeframe analysis (2-day/1-day/12h/6h/4h/2h/1h/30min). Identify uptrend segments, downtrend segments, discrete regulation, unit adjustment cycles, continuous gap divergences, and DIF-DEA divergences, and generate momentum reports with detailed attribute judgments and trading signals. Automatically activates when users inquire about BTC momentum, segment status, MACD analysis, cycle judgment, or divergence detection.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Guide Claude through ingesting TCGA sample sheets, expression archives, and clinical carts into omicverse, initialising survival metadata, and exporting annotated AnnData files.
Index points into a hexagonal grid
Analyze metabolomics data including metabolite identification, quantification, pathway analysis, and metabolic flux. Processes LC-MS, GC-MS, NMR data from targeted and untargeted experiments. Performs normalization, statistical analysis, pathway enrichment, metabolite-enzyme integration, and biomarker discovery. Use when analyzing metabolomics datasets, identifying differential metabolites, studying metabolic pathways, integrating with transcriptomics/proteomics, discovering metabolic biomarkers, performing flux balance analysis, or characterizing metabolic phenotypes in disease, drug response, or physiological conditions.
Tests project primitive (SELECT fields)
Detect anomalies in data using statistical and ML methods. Z-score, IQR, Isolation Forest, and time-series anomalies.
Guide for using Nushell for structured data pipelines and scripting. Use when writing shell scripts, processing structured data, or working with cross-platform automation.
Perform technical analysis on stock K-line data, calculate indicators such as MA/MACD/RSI, and judge trends and trading signals. Trigger scenarios: (1) "Analyze the technical aspects of Moutai" (2) "Check if this stock is buyable" (3) "Technical analysis 600519" (4) Used when needing to judge stock trends and trading points. Need to use data-collect to obtain data first