Total 30,649 skills, Data Processing has 1469 skills
Showing 12 of 1469 skills
Reconcile accounts by comparing GL balances to subledgers, bank statements, or third-party data. Use when performing bank reconciliations, GL-to-subledger recs, intercompany reconciliations, or identifying and categorizing reconciling items.
Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.
Expert-level data science, analytics, visualization, and statistical modeling
Google Sheets API via curl. Use this skill to read, write, and manage spreadsheet data programmatically.
Diagnose ClickHouse merge performance, part backlog, and 'too many parts' errors. Use for merge issues and part management problems.
Analyze ClickHouse table structure, partitioning, ORDER BY keys, materialized views, and identify schema design anti-patterns. Use for table design issues and optimization.
Diagnose ClickHouse replication health, Keeper connectivity, replica lag, and queue issues. Use for replication lag and read-only replica problems.
Track and diagnose ClickHouse ALTER UPDATE, ALTER DELETE, and other mutation operations. Use for stuck mutations and mutation performance issues.
Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python
Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Creates comprehensive compound profiles with identifiers, properties, bioactivity, and drug information. Use when users need chemical data, drug information, or mention PubChem CID, ChEMBL ID, SMILES, InChI, or compound names.
Knowledge about pyecharts chart creation, HTML report generation, and visualization best practices
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.