Loading...
Loading...
Found 41 Skills
Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.
Use this skill when a user wants to store, manage, or work with Goldsky secrets — the named credential objects used by pipeline sinks. This includes: creating a new secret from a connection string or credentials, listing or inspecting existing secrets, updating or rotating credentials after a password change, and deleting secrets that are no longer needed. Trigger for any query where the user mentions 'goldsky secret', wants to securely store database credentials for a pipeline, or is working with sink authentication for PostgreSQL, Neon, Supabase, ClickHouse, Kafka, S3, Elasticsearch, DynamoDB, SQS, OpenSearch, or webhooks.
Amazon Redshift is NOT PostgreSQL — corrects PostgreSQL-derived LLM mistakes; covers Redshift-specific SQL, DDL, COPY/UNLOAD, system views, metadata discovery, and operational patterns. Applies ONLY when the task is about Redshift itself (cluster, Serverless workgroup, or Redshift SQL). Pushes back on: CREATE INDEX, string_agg, pg_catalog, text type, SERIAL, stl_query, LATERAL, RETURNING. Triggers on: Redshift SQL, Redshift CREATE TABLE, Redshift COPY/UNLOAD, slow Redshift query, Redshift permission denied, Redshift disk full, Redshift system views, QUALIFY, PIVOT, MERGE, Redshift Data API, Redshift WLM, concurrency scaling, Redshift resize, Redshift Spectrum external tables. Does NOT apply to (defer to that service's own skill): Amazon S3 storage/bucket policies, Athena or Glue queries/catalogs, data-lake or Iceberg work outside Redshift, Aurora, RDS, or DynamoDB — but S3/Glue ARE in scope for Redshift COPY, UNLOAD, or data-lake queries (external schemas/tables on S3).
Use this skill when building MCP (Model Context Protocol) servers with FastMCP in Python. FastMCP is a framework for creating servers that expose tools, resources, and prompts to LLMs like Claude. The skill covers server creation, tool/resource definitions, storage backends (memory/disk/Redis/DynamoDB), server lifespans, middleware system (8 built-in types), server composition (import/mount), OAuth Proxy, authentication patterns, icons, OpenAPI integration, client configuration, cloud deployment (FastMCP Cloud), error handling, and production patterns. It prevents 25+ common errors including storage misconfiguration, lifespan issues, middleware order errors, circular imports, module-level server issues, async/await confusion, OAuth security vulnerabilities, and cloud deployment failures. Includes templates for basic servers, storage backends, middleware, server composition, OAuth proxy, API integrations, testing, and self-contained production architectures. Keywords: FastMCP, MCP server Python, Model Context Protocol Python, fastmcp framework, mcp tools, mcp resources, mcp prompts, fastmcp storage, fastmcp memory storage, fastmcp disk storage, fastmcp redis, fastmcp dynamodb, fastmcp lifespan, fastmcp middleware, fastmcp oauth proxy, server composition mcp, fastmcp import, fastmcp mount, fastmcp cloud, fastmcp deployment, mcp authentication, fastmcp icons, openapi mcp, claude mcp server, fastmcp testing, storage misconfiguration, lifespan issues, middleware order, circular imports, module-level server, async await mcp
AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.
Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security, service selection, migrations, troubleshooting, or learning AWS. Triggers on AWS, Lambda, S3, EC2, ECS, EKS, DynamoDB, RDS, CloudFormation, CDK, Terraform, Serverless, SAM, IAM, VPC, API Gateway, or any AWS service.
Use when the user asks about chaos engineering, fault injection, resilience testing, or HA verification for a SPECIFIC AWS service (e.g., RDS, EKS, MSK, ElastiCache, DynamoDB, S3, Lambda, OpenSearch, etc.). Triggers on "chaos testing on [service]", "fault injection for [service]", "how to test HA of [service]", "FIS scenarios/actions for [service]", "[service] failover testing", "[service] resilience testing", "[service] 混沌测试", "[service] 故障注入", "[service] 高可用验证", "对 [service] 做混沌实验", "test my [service]", "verify my [service] is resilient". Use this skill even when the user phrases it casually like "test my RDS" or "how resilient is my MSK cluster".
AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.
Use when "designing AWS architecture", "serverless AWS", "cloud infrastructure", "Lambda", "DynamoDB", or asking about "AWS cost optimization", "CloudFormation", "CDK", "API Gateway", "ECS", "EKS"
Connect Spice to data sources and query across them with federated SQL. Use when connecting to databases (Postgres, MySQL, DynamoDB), data lakes (S3, Delta Lake, Iceberg), warehouses (Snowflake, Databricks), files, APIs, or catalogs; configuring datasets; creating views; writing data; or setting up cross-source queries.
This skill should be used when users need to interact with AWS services via CLI. It covers all AWS services including EC2, ECS, EKS, Lambda, S3, RDS, DynamoDB, VPC, Route53, CloudFront, Bedrock, Support, Billing, and more. Supports querying, creating, modifying, deleting resources, monitoring, debugging, and cost analysis. Triggers on requests mentioning AWS, cloud resources, or specific AWS service names.
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.