Database
SQL, schema design, migrations, and database operations
Skills List
harvard-art-museum-etl-analytics
Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL workflows, SQL analytics, and Streamlit visualization.
reason-machinesharvard-artifacts-data-engineering-pipeline
Build ETL pipelines and analytics dashboards using Harvard Art Museums API with SQL storage and Streamlit visualization
reason-machinesharvard-artifacts-data-engineering-analytics
Build end-to-end ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
reason-machinesharvard-art-museum-data-pipeline
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Streamlit, MySQL, and Python
reason-machinesharvard-artifacts-data-engineering-app
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with SQL storage and Streamlit visualization
reason-machinesharvard-artifacts-collection-analytics-pipeline
End-to-end data engineering pipeline for Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
reason-machinesharvard-artifacts-collection-etl-analytics
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit
reason-machinesharvard-artifacts-collection-data-engineering-analytics
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
reason-machinesharvard-artifacts-etl-streamlit-analytics
Build ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit
reason-machinesharvard-art-museums-data-engineering-app
End-to-end data engineering and analytics application for Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
reason-machinesharvard-artifacts-data-pipeline
Build end-to-end data engineering pipelines using Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
reason-machinesretail-etl-medallion-pipeline
End-to-end retail ETL pipeline using Medallion Architecture (Bronze/Silver/Gold) with TSQL, PySpark, and Airflow for inventory, sales, and supplier data processing
reason-machinesharvard-art-museums-etl-pipeline
Build ETL pipelines and analytics dashboards using Harvard Art Museums API with Python, SQL, and Streamlit
reason-machinesharvard-art-museums-data-engineering-pipeline
Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL, SQL analytics, and Streamlit dashboards
reason-machinesharvard-artifacts-collection-analytics-app
Build ETL pipelines and analytics dashboards for the Harvard Art Museums API using Python, SQL, and Streamlit
reason-machinesharvard-art-museum-data-engineering
ETL pipeline and analytics app for Harvard Art Museums API using Python, SQL, and Streamlit
reason-machinesharvard-art-museums-data-engineering-analytics
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
reason-machines
writing-sql
Write SQL queries for Celigo RDBMS exports and imports -- SELECT, INSERT, UPDATE, UPSERT, MERGE, delta, once, and bulk operations across Snowflake, Postgres, MySQL, SQL Server, Oracle, BigQuery, and Redshift. Use when editing rdbms.query or troubleshooting SQL errors.
celigo
datapackage
Explore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what tables or resources a dataset contains, look up column names and descriptions, surface usage warnings embedded in metadata, or understand how to load data from Parquet files, DuckDB or SQLite databases, or CSV files described by a datapackage.json. Also use when the user has a datapackage.json and wants to know what's in it, how to query it efficiently, or how to connect its metadata to actual data files. Pairs well with dataset-specific skills (like `pudl`) that layer domain knowledge on top.
catalyst-cooperative
create-issue
GitHub Issue を親子構造 (sub-issues) で作成する。`gh issue create` で親・子 Issue を生成し、`gh api .../sub_issues` で親子関係を紐付ける。タイトルは Conventional Commits 形式 (`feat:`, `fix:` 等) を推奨。「Issue 作って」「タスクを分解して Issue 化」などで使用。
fandhe-ai
clawdirect-dev
Build agent-facing web experiences with ATXP-based authentication, following the ClawDirect pattern. Use this skill when building websites that AI agents interact with via MCP tools, implementing cookie-based agent auth, or creating agent skills for web apps. Provides templates using @longrun/turtle, Express, SQLite, and ATXP.
napoleond
review-loop
Iterative worker-reviewer cycle that spawns a critic subagent to score work 1-10 and provide actionable feedback, then revises until a quality gate is met. Use when implementing features, writing specs, reviewing existing code, or completing any task where quality matters more than speed. Trigger phrases: "use review-loop", "polish this", "iterate on this", "/review-loop", "review with feedback loop".
2dmurali
alan-plan
Create an implementation plan artifact in Alan
supatest-ai