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. 79 354 files from 1 739 authors, of which 61 713 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.
Competitor content gap analysis — extracts ALL comments from competitor and your own YouTube videos, identifies what audiences want but aren't getting, ranks gaps by engagement, and proposes video ideas. Use when the user says "scoop," "content gap," "analyze competitor videos," "what should my next video be about," "find content gaps," or "competitor analysis.
Formats all Telegram bot replies to be short, scannable, and properly styled with MarkdownV2. Use when replying to Telegram messages via the Telegram MCP plugin — ensures bold, emojis, bullet points, and correct special character escaping.
Home network architecture from scratch — gateway placement, switch and AP selection, IP addressing scheme design, DHCP scoping, and common setup mistakes for homelab beginners.
BGP neighbor state analysis, stuck session troubleshooting, AS path inspection, route filtering, and peering issue diagnosis on Cisco IOS/IOS-XE and multi-vendor devices.
Multi-vendor network device SSH automation using Python Netmiko — connecting, sending commands, parsing output, handling enable mode, error handling, and batch operations across device lists.
Pre-deployment validation of Cisco IOS/IOS-XE configuration — catching missing saves, mismatched ACLs, overlapping subnets, duplicate IPs, missing route-maps, and dangerous commands before they cause outages.
Cisco IOS and IOS-XE configuration syntax, show commands, privilege levels, config mode navigation, wildcard masks, and the most common operational gotchas.
Diagnosing network interface errors including CRC errors, input/output drops, runts, giants, duplex mismatches, flapping, and speed negotiation failures on Cisco IOS/IOS-XE devices.
| Extract artists from concert/festival lineups and create playlists to preview their music.
| (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays
| brainstorm, ideate, generate ideas, run a design thinking session, create How Might We statements, use SCAMPER, do Crazy 8s, mind map, storyboard, define a problem statement, create empathy maps, build prototypes, test assumptions, run design sprints, reframe problems, do "worst possible idea", use "yes and" building, explore analogous inspiration, conduct user interviews, synthesize insights, create POV statements, plan user tests, iterate on concepts, or facilitate any creative problem-solving session.
Make responses easy to start, scan, and complete - action first, numbered steps, minimal cognitive load, no repetition or filler. Use for ANY request that would otherwise get a long or multi-part answer: how-to and setup questions, multi-step tasks, comparing options, planning work, debugging walkthroughs, or explaining a system. Also use when the user invokes it directly, says they have ADHD, asks to be brief or to cut the fluff, or shows signs of executive dysfunction, overwhelm, decision paralysis, or task paralysis ("I don't know where to start", "this is too much", "I keep putting this off", "just tell me what to do").
> 🤖 Data Science & AI/ML skill suite derived from alirezarezvani/claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
> 📈 SEO & Content Marketing skill suite derived from borghei/Claude-Skills. Keyword research, content audits, SERP analysis, technical SEO and content strategy. Provides 10 specialised commands for seo, content, marketing workflows.
Autonomously research an ML task and run MANY bounded experiments to find the best config — a fixed-budget edit→train→eval→keep-or-discard loop in the spirit of karpathy/autoresearch, wrapped in the ml-intern orchestrator model and fanned out with a Claude Code dynamic workflow. Runs LONG: an iterative generational loop (mims-harvard/AutoScientists style) where parallel agent teams propose hypotheses, peer-critique them before spending any GPU, share findings on a common board, promote a champion, and keep going until budget/stagnation/convergence. Triggers when the user wants to "run many experiments", "sweep / search for the best config", "beat a benchmark", "do an ablation", "autoresearch X", "run for a long time / overnight / for days", or "find what improves metric Y on dataset Z". Deep-researches existing solutions across the internet FIRST (fan-out web search + PapersWithCode + GitHub, sources cross-checked into a cited DEEPRESEARCH.md), then ASKS where to get GPUs ("cards") and data before spending any compute, generates an experiment matrix seeded from diverse literature angles, runs it as a background workflow under an explicit budget, keeps a running leaderboard + shared findings board, verifies winners, and reports the best config. Reuses ml-intern's notify.sh + hf_push.sh for milestone alerts and HF Hub publishing.
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.
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, 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.
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.
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.
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.
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.
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.
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.
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.
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.