Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.
An intermediate example with metadata and examples
Use this skill when users need interactive HTML reports, dashboards, charts, or visualizations for LimaCharlie data. You generate the HTML, this skill serves it on localhost.
Creates STRUCTURED summary.md in each daily folder. Analyzes chat conversations and generates actionable summaries with "Što je uradio" and "Što bi trebao da uradi" sections per person. Use for daily reports and activity tracking.
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_by for grouping, aggregation functions (count, sum, avg, percentile), and topk for top N results. Returns single summary row per group across entire time range. For time-series trends, see time-series-analysis skill.
Create dbt models following FF Analytics Kimball patterns and 2×2 stat model. This skill should be used when creating staging models, core facts/dimensions, or analytical marts. Guides through model creation with proper grain, tests, External Parquet configuration, and per-model YAML documentation using dbt 1.10+ syntax.
Generate well-formatted markdown tables from data with proper alignment and spacing. Use for report statistics, comparison tables, or summary data presentation.
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.
Answer analytical questions about carbon accounting data using internal datasets, APIs, and emission factor calculations.
This skill generates interactive Chart.js visualizations for use in iframes using any chart type supported by the library (line, bar, pie, doughnut, radar, polar area, bubble, scatter). Use this skill when users need to create data visualizations for educational content, reports, or dashboards. The skill creates complete MicroSim packages with HTML, CSS, and documentation.
This skill generates interactive Chart.js bubble chart visualizations for priority matrices and multi-dimensional data analysis. Use this skill when users need to create scatter plots with variable bubble sizes, particularly for 2x2 priority matrices (Impact vs Effort, Risk vs Value, etc.), portfolio analysis, or any visualization comparing items across two dimensions with a third dimension represented by size. The skill creates a complete MicroSim package with HTML, CSS, and documentation.
Use when working on Acuantia's BigQuery Dataform pipeline (acuantia-gcp-dataform project) - adds Acuantia-specific patterns on top of dataform-engineering-fundamentals: ODS two-arg ref() syntax, looker_ filename prefix, Looker integration (looker_prod/looker_dev), acuantia dataset conventions, coordination with callrail_data_export/dialpad_data_integration/looker projects
Distributed evolutionary memory system using Merkle-DAG branching timelines, holographic erasure coding, and stake-weighted consensus to maintain coherent collective history across thousands of agents despite forking narratives and temporal relativity.
Generate Apache Airflow ETL pipelines for government websites and document sources. Explores websites to find downloadable documents, verifies commercial use licenses, and creates complete Airflow DAG assets with daily scheduling. Use when user wants to create ETL pipelines, scrape government documents, or automate document collection workflows.
PM Airtable data model reference. Use when creating tables, querying structure, or understanding relationships between Domain, Subdomain, Capability, Entity, Requirement, and BacklogItem tables.
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
Expert documentation generation for ingestion layers. Automatically detects connector types (REST API, Database, File, Streaming), documents authentication patterns, rate limiting strategies, and incremental load patterns. Use when documenting data source ingestion workflows.
Expert documentation generation for staging transformation layers. Auto-detects SQL engine (Presto/Trino vs Hive), documents transformation rules, PII handling, deduplication strategies, and data quality rules. Use when documenting staging transformations.
Manage J-Quants ingestion, feature graph generation, and cache hygiene for the ATFT-GAT-FAN dataset pipeline.
Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.
Validate production batch execution - trigger daily runs and analyze traces for architecture completeness and result quality
Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.
