Total 50,472 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Design clear, accessible data visualizations with appropriate chart selection and styling.
Convert Dune (Trino) SQL queries to Allium (Snowflake) SQL. SQL dialect conversions (Trino → Snowflake) apply to all chains. Comprehensive Solana and EVM chain mappings included.
Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
Cluster and attribute related wallets — funding chains, shared signers, CEX deposit patterns. Use when tracing wallet ownership, comparing two wallets, finding wallet relationships, governance voters, or related address clusters.
Is SM buying this token on one chain but selling on another? Detect capital rotation.
Discover trending tokens — screener, SM holdings, Nansen indicators, and flow intelligence for promising finds. Use when scanning for new tokens or screening what's hot.
Has SM been in this token for weeks, or did they just enter? Are they still buying?
Who is this wallet and what have they been doing? Identity labels, balance, PnL summary, recent transactions, perp positions, and counterparties.
Design KPI dashboards for executive monitoring. Use for performance tracking, strategic initiatives, and management reporting.
Macro liquidity monitoring and risk early-warning system. By tracking 4 core indicators (Fed Net Liquidity, SOFR Overnight Financing Rate, MOVE Treasury Volatility Index, Yen Carry Trade Signals), it provides real-time assessment of liquidity conditions in the global financial system, outputting liquidity ratings and risk response recommendations. When users mention topics such as liquidity, Fed balance sheet reduction (QT), TGA account, reverse repo ON RRP, SOFR rate, MOVE index, Treasury volatility, yen carry trade, USDJPY and interest rate differentials, impact of QT on markets, whether money is tight, liquidity inflection points, tightening financial conditions, etc., this skill should be used. Even if users ask broadly "how is liquidity right now" or "is the Fed draining or injecting liquidity," this skill should be triggered to provide a structured analytical framework.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".