Boost efficiency with task automation and organizers.
Build complete document knowledge bases with PDF text extraction, OCR for scanned documents, vector embeddings, and semantic search. Use this for creating searchable document libraries from folders of PDFs, technical standards, or any document collection.
Generates architecture, database, and system diagrams using Mermaid syntax. Creates visual representations of system architecture, database schemas, component relationships, and data flows.
Coordinates academic research workflow - delegates analysis, correlation, writing, and review tasks to specialist agents
Expert guidance for enterprise resource planning systems, business logic, domain modeling, and ERP integration patterns. Use when building ERP systems, implementing business workflows, or integrating with ERP platforms.
Use PROACTIVELY when users need guaranteed schema compliance or validated tool inputs from Anthropic's structured outputs feature. Expert advisor for choosing between JSON outputs (data extraction/formatting) and strict tool use (agentic workflows). Analyzes requirements, explains trade-offs, and delegates to specialized implementation skills. Not for simple text responses or unstructured outputs.
Query internal data services to answer well-scoped questions, producing results and artifacts that are safe to share and easy to re-run. Use when stakeholders need metrics, trends, or data slices from Supabase, analytics tables, or reporting APIs.
Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla updates", "what's new with TimeGPT", or "find time-series papers".
Configure and manage data quality tests, profiling, alerts, and incidents in OpenMetadata. Use when setting up quality tests, configuring profiler workflows, creating observability alerts, or triaging data quality incidents.
Data warehouse (大数据/数仓) ops skill for designing and operating analytical data platforms. Use for tasks like defining source-of-truth, building ETL/ELT pipelines, dimensional modeling (star schema), data quality checks, partitioning strategies, cost/performance tuning, governance, lineage, and SLA monitoring.
Detect recurring patterns in issues and events. Identifies temporal, resource-based, cluster, and cascading patterns. Suggests prevention strategies. Keywords: recurrence, pattern, detection, trending, recurring, prevention, issue, analysis.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create visualizations, or build predictive models with reproducible, code-based workflows.
The core skill for working within the bigquery-etl repository. Use this skill when understanding project structure, conventions, and common patterns. Works with model-requirements, query-writer, metadata-manager, sql-test-generator, and bigconfig-generator skills.
Generic health check and data freshness monitoring patterns for marketing intelligence systems. Use when validating connectivity to data sources, checking data staleness, or ensuring system reliability. Framework for project-specific implementations.
Fast Python framework for building interactive web apps, dashboards, and data visualizations without HTML/CSS/JavaScript. Use when user wants to create data apps, ML demos, dashboards, data exploration tools, or interactive visualizations. Transforms Python scripts into web apps in minutes with automatic UI updates.
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigate between datasets using temporal relationships. Covers temporal join mechanics (lookup, join, follow), automatic field matching, and when to use Resources vs Reference Tables.
Create factual working journal entries in Notes/WorkingJournal/ after completing analysis work. Use when user asks to "summarize work", "document results", or "create working journal entry". Ensures code is committed, copies figures to attachments, and creates objective summaries with citations.
Perform comprehensive data analysis, statistical modeling, and data visualization by writing and executing self-contained Python scripts. Use when you need to analyze datasets, perform statistical tests, create visualizations, or build predictive models with reproducible, code-based workflows.
Work with Reference Tables (static CSV lookup data) using OPAL to enrich datasets with descriptive information. Use when you need to map IDs to human-readable names, add static metadata from CSV uploads, or perform lookups without temporal considerations. Covers both explicit and implicit lookup patterns, column name matching, and when to choose Reference Tables vs Resources vs Correlation Tags.
Use when working with data transformations, scales, color schemes, formatting, or CSV/TSV parsing. Invoke for data processing pipelines, scale creation, color interpolation, number/date formatting, or data loading/parsing operations.
Data engineering agent for ETL pipelines, data warehousing, and analytics
Expert assistant for Kumo's Query Module (src/features/query). Use when working with SQL Editor, Monaco editor integration, query execution, result grids, tabs management, AI features, or any query-related components. Provides architecture patterns, component APIs, hooks, utilities, and examples.
Generates architecture, database, and system diagrams using Mermaid syntax. Creates visual representations of system architecture, database schemas, component relationships, and data flows.
Analytical thinking patterns for comprehensive evaluation, code audits, security analysis, and performance reviews. Provides structured templates for thorough investigation with extended thinking support.
