数据库

harvard-artifacts-data-engineering-app

5database

Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with SQL storage and Streamlit visualization

reason-machines

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harvard-artifacts-collection-analytics-pipeline

5database

End-to-end data engineering pipeline for Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization

reason-machines

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harvard-artifacts-collection-etl-analytics

5database

Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit

reason-machines

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harvard-artifacts-collection-data-engineering-analytics

5database

End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization

reason-machines

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harvard-artifacts-etl-streamlit-analytics

5database

Build ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit

reason-machines

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harvard-art-museums-data-engineering-app

5database

End-to-end data engineering and analytics application for Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization

reason-machines

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harvard-artifacts-data-pipeline

5database

Build end-to-end data engineering pipelines using Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization

reason-machines

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retail-etl-medallion-pipeline

5database

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-machines

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harvard-art-museums-etl-pipeline

5database

Build ETL pipelines and analytics dashboards using Harvard Art Museums API with Python, SQL, and Streamlit

reason-machines

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harvard-art-museums-data-engineering-pipeline

5database

Build end-to-end data engineering pipelines with Harvard Art Museums API, ETL, SQL analytics, and Streamlit dashboards

reason-machines

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harvard-artifacts-collection-analytics-app

5database

Build ETL pipelines and analytics dashboards for the Harvard Art Museums API using Python, SQL, and Streamlit

reason-machines

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harvard-art-museum-data-engineering

5database

ETL pipeline and analytics app for Harvard Art Museums API using Python, SQL, and Streamlit

reason-machines

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harvard-art-museums-data-engineering-analytics

5database

Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit

reason-machines

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writing-sql

4database

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

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datapackage

3database

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

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create-issue

2database

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

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clawdirect-dev

1database

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

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review-loop

1database

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

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alan-plan

0database

Create an implementation plan artifact in Alan

supatest-ai

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