lambda_skill

Overview

This skill provides practical guidance and command examples for building, configuring, and operating AWS Lambda serverless functions. It focuses on common tasks like creating Python functions, wiring triggers, optimizing performance and costs, and troubleshooting invocation problems. The content is action-oriented for developers and operators working with event-driven compute.

How this skill works

The skill explains core Lambda concepts (functions, invocation types, execution environments, and layers) and walks through concrete CLI and boto3 examples to create, update, and invoke functions. It covers configuring event source mappings (SQS, S3), permissions, environment variables, layers, and Provisioned Concurrency. Troubleshooting sections map symptoms to quick diagnostic commands and fixes.

When to use it

• Creating or deploying Python-based Lambda functions and layers
• Configuring triggers and event source mappings (S3, SQS, EventBridge)
• Debugging timeouts, memory errors, permission issues, or VPC connectivity
• Optimizing cold starts, memory/CPU trade-offs, and cost
• Setting up retries, DLQs, and idempotent handlers for reliability

Best practices

• Right-size memory to improve CPU and execution speed; benchmark before committing
• Initialize SDK clients and heavy resources outside the handler to reuse environments
• Keep deployment packages small and use layers for shared dependencies
• Grant least-privilege IAM roles and store secrets in Secrets Manager with KMS
• Use Provisioned Concurrency or Graviton2 for predictable latency and cost savings

Example use cases

• API backend: synchronous Lambda behind API Gateway for request/response flows
• Event processing: S3 or EventBridge-triggered functions for async workflows
• Queue workers: SQS-triggered handlers with batching and dead-letter queues
• Data ingestion: Kinesis/DynamoDB stream consumers with checkpointing
• Dependency management: publish a layer for shared Python libraries