214 skills found · Page 2 of 8
mksglu / context-modeFree, local plugin that keeps tool output out of your coding agent's context window. Sandbox and SQLite FTS5 search, 99% fewer bytes in context. Works with Claude Code, Codex, Pi, OpenCode, Gemini CLI, Copilot CLI and 4 more agents. Zero telemetry.
nanocoai / add-iron-proxyInstall or refresh Iron Proxy and its official Iron Control web console for NanoClaw
phuryn / sql-queriesGenerate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation
phuryn / dummy-datasetGenerate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script)
anthropics / sql-queriesWrite correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.)
anthropics / validate-dataQA an analysis before sharing -- methodology, accuracy, and bias checks
anthropics / write-queryWrite optimized SQL for your dialect with best practices
google / bigquery-udfsCreates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints
google / cloud-monitoring-metric-selectionRetrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.)
google / agent-platform-rag-engine-managementManage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK
google / gke-inferenceDeploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers
google / dbt-sf-to-bq-translatorTranslates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication
google / bigquery-migration-sql-translatorTranslates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery GoogleSQL using the BigQuery Migration Service (BQMS) MCP server
google / bigquery-observabilityProvides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API
google / google-cloud-solution-hybrid-search-alloydbDiscovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud
google / google-cloud-solution-rag-enterprise-search-gke-sqldbDiscovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud
google / alloydb-basicsManages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations
google / cloud-logging-query-generationGenerates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service.
google / bigquery-bigframesGenerates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
google / bigquery-optimizationProvides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries
google / bigtable-basicsAssists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable
google / cloud-sql-basicsThis file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL.
eigent-ai / skill-security-auditorSecurity auditing for code, configs, and infrastructure
Orchestra-Research / mlflowTrack ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
Jeffallan / fastapi-expertUse when building high-performance async Python APIs with FastAPI and Pydantic V2. Invoke to create REST endpoints, define Pydantic models, implement authentication flows, set up async SQLAlchemy database operations, add JWT authentication, build WebSocket endpoints, or generate OpenAPI documentatio…
Jeffallan / secure-code-guardianUse when implementing authentication/authorization, securing user input, or preventing OWASP Top 10 vulnerabilities — including custom security implementations such as hashing passwords with bcrypt/argon2, sanitizing SQL queries with parameterized statements, configuring CORS/CSP headers, validating…
Jeffallan / spark-engineerUse when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads.
Jeffallan / database-optimizerOptimizes database queries and improves performance across PostgreSQL and MySQL systems
Jeffallan / postgres-proUse when optimizing PostgreSQL queries, configuring replication, or implementing advanced database features. Invoke for EXPLAIN analysis, JSONB operations, extension usage, VACUUM tuning, performance monitoring.
Jeffallan / code-reviewerAnalyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback
