Essential command-line tools and system utilities.
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier detection (Z-score, IQR), threshold-based alerts, rate-of-change detection with window functions, and moving average baselines. Choose pattern based on data distribution and anomaly type.
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.
DevRel metrics, analytics dashboards, and program reporting
Unified markdown and OpenCode component specialist providing document quality enforcement (structure, style), content optimization for AI assistants, complete component creation workflows (skills, agents, commands with scaffolding, validation, packaging), ASCII flowchart creation for visualizing complex workflows, and install guide creation for MCP servers, plugins, and tools.
Use when user asks about N+1 queries, performance optimization, query optimization, reduce API calls, improve render performance, fix slow code, optimize database, or reduce bundle size. Provides guidance on identifying and fixing performance anti-patterns across database, backend, frontend, and API layers.
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.
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pattern, summary vs time-series output, and common aggregation functions. For percentile metrics (tdigest), see analyzing-tdigest-metrics skill.
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.
Conduct systematic research with confidence scoring, source validation, and structured reporting for technology decisions and codebase analysis. Use for complex research tasks, technology selection, or best practice discovery.
Data engineering agent for ETL pipelines, data warehousing, and analytics
Use PROACTIVELY when creating research reports, experiment writeups, technical whitepapers, or empirical study documentation. Orchestrates the complete benchmark report pipeline with structure, diagrams, hi-res PNG capture, and PDF export. Provides working scripts, CSS templates, and complete command sequences for publication-quality AI/ML benchmark reports. Not for slides, blog posts, or simple README files.
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.
Detection rules and grep patterns for database performance anti-patterns. Use when scanning codebase for N+1 queries, sequential queries, or connection pool issues.
Analyzes how user-facing use cases map to data access patterns and architecture. Auto-triggers when: implementing new features that need existing pattern understanding, explaining how a feature works in the data layer, planning changes affecting data access, asking "how does X work in our codebase?", or tracing a user action through the system architecture.
