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Found 625 Skills
Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely.
Used when user requests involve dataset queries, SQL creation, and BFF development for the Lovrabet/Yuntoo platform. Trigger words: dataset, data table, custom SQL, filter, sql.execute, bff.execute, get_dataset_detail, validate_sql_content, save_or_update_custom_sql, save_or_update_bff_script, @lovrabet/sdk, MCP SQL workflow, multi-table association, lovrabet development.
Manage daily check-in records stored in local SQLite, supporting functions including adding check-ins, viewing records, statistical analysis, querying consecutive check-in days, deleting and modifying records. This skill should be actively used when users mention check-in, sign-in, recording daily habits such as exercise, reading, learning, fitness, meditation, running, cycling, etc., or want to check how many days they have stuck to a certain habit, how much time they spent exercising this week, what they checked in today. Even if the user does not explicitly say "check-in", it is applicable as long as it involves daily habit tracking and activity recording. It also works for English scenarios, such as check in, log my workout, track my reading, how many days in a row, streak, habits.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
개발 DB에 SQL을 직접 실행하여 데이터를 조회하는 스킬. "DB 확인", "데이터 조회", "테이블 조회", "SQL 실행", "데이터 검증" 키워드로 트리거. AI 에이전트가 백엔드/프론트엔드 개발 중 개발 DB 데이터를 즉시 확인할 때 사용.
Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.
REST-native FedEx CLI for small business shippers, with rate-shopping, bulk CSV labels, an address book, and a local SQLite ledger no other tool has. Trigger phrases: `ship a package via FedEx`, `rate-shop FedEx services`, `bulk-print FedEx labels from CSV`, `save a FedEx recipient`, `issue a FedEx return label`, `FedEx spend this month`, `track a FedEx shipment`, `use fedex-pp-cli`, `run fedex`.
Debug Flask applications systematically with this comprehensive troubleshooting skill. Covers routing errors (404/405), Jinja2 template issues, application context problems, SQLAlchemy session management, blueprint registration failures, and circular import resolution. Provides structured four-phase debugging methodology with Flask-specific tools including Werkzeug debugger, Flask-DebugToolbar, and Flask shell for interactive investigation.
Validates SQL schema files for compliance with internal safety and naming policies.
Python backend patterns for asyncio, FastAPI, SQLAlchemy 2.0 async, and connection pooling. Use when building async Python services, FastAPI endpoints, database sessions, or connection pool tuning.
Master data engineering, ETL/ELT, data warehousing, SQL optimization, and analytics. Use when building data pipelines, designing data systems, or working with large datasets.