Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 696 files from 2 113 authors, of which 68 413 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Explain MotherDuck pricing and ROI tradeoffs. Use for any pricing, cost, billing, plan-comparison, instance-sizing, chargeback, or budget question — when an economic_buyer, technical_owner, or analytics_lead asks about spend, budget guardrails, workload cost drivers, plan fit, vendor cost comparisons, or whether MotherDuck is worth adopting.
Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.
Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.
Decide when DuckLake is the right MotherDuck storage pattern versus native MotherDuck storage (the default). Use when evaluating lakehouse or open table format storage, Iceberg-style requirements, fully managed DuckLake, BYOB buckets, own-compute DuckLake access, data inlining, time travel, object-storage layout, or file-aware compaction and maintenance.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.
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.
MotherDuck REST API control-plane reference. Use when calling api.motherduck.com or MotherDuck MCP admin tools to provision service accounts, manage tokens, configure Ducklings, or mint Dive embed sessions. Not for SQL or data-plane query work.
Explain MotherDuck security, governance, and access-control patterns. Use for any question about SOC 2, GDPR, compliance, data residency, regions, SSO, service accounts, token handling, tenant isolation, sharing boundaries, snapshots and recovery, or governance posture — including when a security_compliance_owner, technical_owner, or application_builder is evaluating MotherDuck.
Design a MotherDuck-backed customer-facing analytics app. Use for embedded analytics, multi-tenant SaaS reporting, or product analytics for external users -- whenever the decision depends on per-customer isolation, backend routing, service-account boundaries, read scaling, or Hypertenancy-style patterns.
Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.
Build a live MotherDuck dashboard as a Dive. Use when composing one shareable KPI, trend, and breakdown story over existing MotherDuck data, especially when the result should stay a saved workspace artifact rather than a full application.
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
Connect to MotherDuck from any application. Use when setting up database connectivity via the Postgres endpoint (recommended), pg_duckdb, native DuckDB API, or JDBC. Covers connection strings, authentication, SSL, and environment variable configuration.
Create, edit, manage, share, or embed MotherDuck Dives — live React + SQL dashboards, charts, and data apps saved in the workspace. Use for any dashboard, chart, KPI display, or data visualization over MotherDuck data, and for Dive authoring mechanics such as get_dive_guide, useSQLQuery, local preview, version history, Dives-as-code, required resources, team sharing, or embedded Dive sessions.
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.
DuckDB SQL reference for MotherDuck. Use when you need exact DuckDB syntax or function behavior, friendly SQL features like QUALIFY, GROUP BY ALL, or list/struct types, MotherDuck-specific SQL such as shares, secrets, snapshots, or UNDROP, or to fix SQL errors and PostgreSQL-style SQL that fails on MotherDuck.
Design or redesign a MotherDuck Dive as a responsive, reusable analytics interface. Use when a Dive must be mobile-friendly from the start, support light and dark modes, reserve space for filters, use restrained Power BI-style information design, embed small charts inside metric components, or work across customers without one-off layout changes.
Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.
Decide when DuckLake is the right MotherDuck storage pattern versus native MotherDuck storage (the default). Use when evaluating lakehouse or open table format storage, Iceberg-style requirements, fully managed DuckLake, BYOB buckets, own-compute DuckLake access, data inlining, time travel, object-storage layout, or file-aware compaction and maintenance.
Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.
Load and ingest data into MotherDuck from local files, object storage (S3, GCS, Azure, R2), HTTPS, dataframes, or external databases. Use for any import or bulk-load task — CSV, Parquet, JSON, Delta, Iceberg, local DuckDB database upload — and for choosing between CTAS, INSERT...SELECT, COPY, cloud-storage secrets, and Postgres-endpoint versus native DuckDB-client paths.
Plan a migration onto MotherDuck. Use when moving from Snowflake, BigQuery, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, source-vs-target validation, rollback, and native-versus-DuckLake posture.
Deliver repeatable MotherDuck architectures across multiple clients. Use when a consultancy, agency, or multi-client product team needs to standardize isolation, provisioning, regional deployment, sharing boundaries, and client-specific exceptions across client engagements.
Explain MotherDuck pricing and ROI tradeoffs. Use for any pricing, cost, billing, plan-comparison, instance-sizing, chargeback, or budget question — when an economic_buyer, technical_owner, or analytics_lead asks about spend, budget guardrails, workload cost drivers, plan fit, vendor cost comparisons, or whether MotherDuck is worth adopting.
MotherDuck REST API control-plane reference. Use when calling api.motherduck.com or MotherDuck MCP admin tools to provision service accounts, manage tokens, configure Ducklings, or mint Dive embed sessions. Not for SQL or data-plane query work.
Design and build database schemas and data models in MotherDuck. Produces a file-based SQL project scaffold with a model manifest. Use for any schema design or data modeling task — creating tables, choosing data types, star schemas, wide denormalized tables, raw/staging/analytics layers, dbt-style transformation projects, or restructuring data for analytics workloads.
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.
Create and manage MotherDuck data shares for zero-copy, read-only data distribution. Use whenever someone wants to share a database with team members, another organization, or the public — covers CREATE SHARE, access/visibility/update modes, GRANT READ ON SHARE, attaching share URLs, UPDATE SHARE, and REFRESH DATABASE.
Explain MotherDuck security, governance, and access-control patterns. Use for any question about SOC 2, GDPR, compliance, data residency, regions, SSO, service accounts, token handling, tenant isolation, sharing boundaries, snapshots and recovery, or governance posture — including when a security_compliance_owner, technical_owner, or application_builder is evaluating MotherDuck.
Claude Skills — questions
Answers built from the skills we actually parsed.