motherduck-query
Execute DuckDB SQL queries against MotherDuck databases. Use when running analytics, aggregations, transformations, or any SQL operation. Covers query best practices, CTEs, window functions, QUALIFY, and performance optimization.
Works with
--- name: motherduck-query description: Execute DuckDB SQL queries against MotherDuck databases. Use when running analytics, aggregations, transformations, or any SQL operation. Covers query best practices, CTEs, window functions, QUALIFY, and performance optimization. license: MIT --- # Query MotherDuck Use this skill when executing SQL queries for analytics, aggregations, transformations, or data exploration against MotherDuck databases. ## Prerequisites - An established MotherDuck connection (or an active MotherDuck MCP server) - Target database and tables identified ## Default Posture - Write DuckDB SQL, not PostgreSQL SQL, even when using the PG endpoint. - Always use fully qualified `"database"."schema"."table"` names. - Preserve the intended grain of every result set; state the grain before optimizing or materializing a query. - Filter early, aggregate early, and prefer serving tables or summaries for repeated reads. - Keep SQL obvious, multi-line, and explicit about grain, filters, and output shape. - Treat DDL, DML, `ATTACH`, `DETACH`, recovery commands such as `CREATE SNAPSHOT`, `ALTER DATABASE ... SET SNAPSHOT`, `UNDROP DATABASE`, and lifecycle commands such as `SHUTDOWN` as writes. Use the MotherDuck MCP `query_rw` tool when the user's change request authorizes the write. Ask for confirmation only when the action is destructive, externally visible, or outside the stated scope. - Tag long-lived integrations with `custom_user_agent` when the connection path supports it. ## Workflow 1. Confirm the actual tables, columns, and grain before writing SQL. 2. Write the query in SQL first, then wrap it in Python or TypeScript only if needed. 3. Use CTEs and DuckDB-native patterns such as `GROUP BY ALL`, `QUALIFY`, and `arg_max`. 4. Check the plan, row count, and shape for pushdown, unnecessary sorts, or repeated raw rescans. 5. Materialize expensive repeated queries into serving tables or light views when warranted. ## Open Next - Read `references/QUERY_PLAYBOOK.md` for DuckDB query patterns, exploration SQL, performance rules, common analytical shapes, and common mistakes ## Related Skills - `motherduck-connect` for session setup - `motherduck-duckdb-sql` for syntax and function reference - `motherduck-explore` for understanding the source schema before writing queries
More Performance skills
seo-audit
coreyhaines31/marketingskills
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
competitor-profiling
coreyhaines31/marketingskills
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
vercel-optimize
vercel-labs/agent-skills
Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.
