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Found 773 Skills
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Use when reviewing or writing new SQL/SQLAlchemy queries, especially in `backend/app/services/`, `backend/app/models/`, or migration files. Catches missing tenant filters, N+1 queries, async-session misuse, and missing indexes before they reach production. Trigger when the user says "review this query", "check for N+1", "is this query safe", or modifies repo-layer code.
Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.
Use SqlClient for raw SQL against mapped dataset aliases and parse columnar SQL responses safely.
Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket changes.
Audit and improve SQL quality systematically — catch performance smells in raw SQL, ORM-generated queries, schema/modeling decisions, migrations, or PR diffs, explain the database-level impact, give a corrected version, and make the tradeoff explicit. Generic by design: no project, domain, ORM, or language config — any context it needs (is this table transactional? is the scan intentional? what volume is expected?) is raised during analysis, never assumed. Reach for it whenever someone writes, reviews, or optimizes a query or data-access code — "review this query", "why is this slow", "check my migration", "is this index right", or when you see N+1, SELECT *, a cartesian/row explosion, three-plus joins, a missing date filter on a growing table, OFFSET pagination, LIKE '%term%', NOT IN with nullable columns, an unindexed ORDER BY, an unbounded list, or a long transaction — even when they never say the word "SQL". Use it both to validate new code and designs and to audit existing ones.
Write and query high-cardinality event data at scale with SQL. Load when tracking user events, billing metrics, per-tenant analytics, A/B testing, API usage, or custom telemetry. Use writeDataPoint for non-blocking writes and SQL API for aggregations.
Analyzes PHP code for SQL injection vulnerabilities. Detects query concatenation, ORM misuse, raw queries, dynamic identifiers, prepared statement bypasses.
Use this skill when you need to execute SQL against the MoviePilot database. This skill guides you through connecting to the database and executing SQL statements. The database type (SQLite or PostgreSQL) and connection details are provided in the system prompt <system_info>. Applicable scenarios include: 1) The user asks about data statistics, counts, or aggregations that existing tools don't cover; 2) The user wants to inspect, modify, or fix raw database records; 3) The user asks to clean up data, update records, or perform database maintenance; 4) The user asks questions like "how many downloads", "show me site stats", "delete old records", etc.
Systematically diagnoses and resolves common GKE issues including pod failures, networking problems, database connection errors, and Pub/Sub issues. Use when pods are stuck in Pending, CrashLoopBackOff, ImagePullBackOff, experiencing DNS failures, Cloud SQL connection timeouts, or Pub/Sub message processing problems. Provides systematic debugging workflows and solution patterns for Spring Boot applications.
Comprehensive expertise in blockchain data analysis using Dune Analytics, custom indexers, and on-chain data querying. Covers SQL for blockchain, dashboard creation, protocol metrics, and alpha discovery. Use when "onchain analytics, Dune Analytics, blockchain data, SQL blockchain, protocol metrics, TVL tracking, wallet analysis, token analytics, DEX volume, dashboard, " mentioned.
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.