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Aws Lambda

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BagelHole
aws-lambda

Build and deploy serverless functions on AWS Lambda. Configure triggers, manage permissions, and optimize performance. Use when implementing serverless applications.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill nameaws-lambda
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by BagelHole on GitHub. Read the source before you install it.

Installation

Install the Aws Lambda AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-aws/aws-lambda
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/cloud-aws/aws-lambda .claude/skills/aws-lambda
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Lambda in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Lambda on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Lambda is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS Lambda

Build serverless applications with AWS Lambda, covering function creation, event sources, layers, SAM templates, and cold start optimization.

When to Use This Skill

  • Building event-driven applications triggered by API Gateway, S3, SQS, or EventBridge
  • Running scheduled tasks (cron) without managing servers
  • Processing data streams from Kinesis or DynamoDB
  • Building lightweight APIs with API Gateway or function URLs
  • Implementing webhooks, Slack bots, or automation scripts
  • Reducing compute costs for intermittent or bursty workloads

Prerequisites

  • AWS CLI v2 installed and configured
  • IAM permissions: lambda:*, iam:PassRole, logs:*, apigateway:*, s3:*
  • Python 3.11+, Node.js 20+, or another supported runtime installed locally
  • (Optional) AWS SAM CLI for local development and deployment

Create and Deploy a Function

bash
# Create a deployment package
cd my-function
zip -r function.zip app.py

# Create the Lambda function
aws lambda create-function \
  --function-name my-api-handler \
  --runtime python3.12 \
  --handler app.handler \
  --role arn:aws:iam::123456789012:role/LambdaExecRole \
  --zip-file fileb://function.zip \
  --memory-size 256 \
  --timeout 30 \
  --environment 'Variables={STAGE=production,LOG_LEVEL=INFO}' \
  --architectures arm64 \
  --tracing-config Mode=Active \
  --tags '{"Team":"backend","Environment":"production"}'

# Update function code
aws lambda update-function-code \
  --function-name my-api-handler \
  --zip-file fileb://function.zip

# Update function configuration
aws lambda update-function-configuration \
  --function-name my-api-handler \
  --memory-size 512 \
  --timeout 60 \
  --environment 'Variables={STAGE=production,LOG_LEVEL=WARNING}'

# Publish a version (immutable snapshot)
aws lambda publish-version \
  --function-name my-api-handler \
  --description "v1.2.0 - added rate limiting"

# Create an alias pointing to the version
aws lambda create-alias \
  --function-name my-api-handler \
  --name live \
  --function-version 3

# Weighted alias for canary deployments (90% v3, 10% v4)
aws lambda update-alias \
  --function-name my-api-handler \
  --name live \
  --function-version 4 \
  --routing-config '{"AdditionalVersionWeights":{"3":0.9}}'

Function Code Examples

python
# app.py - API Gateway handler with structured logging
import json
import logging
import os

logger = logging.getLogger()
logger.setLevel(os.environ.get("LOG_LEVEL", "INFO"))

def handler(event, context):
    """Handle API Gateway proxy event."""
    logger.info("Request: %s %s", event["httpMethod"], event["path"])

    try:
        body = json.loads(event.get("body", "{}"))
        result = process_request(body)

        return {
            "statusCode": 200,
            "headers": {
                "Content-Type": "application/json",
                "X-Request-Id": context.aws_request_id
            },
            "body": json.dumps(result)
        }
    except ValueError as e:
        logger.warning("Validation error: %s", e)
        return {"statusCode": 400, "body": json.dumps({"error": str(e)})}
    except Exception as e:
        logger.exception("Unhandled error")
        return {"statusCode": 500, "body": json.dumps({"error": "Internal server error"})}

def process_request(body):
    return {"message": "OK", "data": body}
python
# sqs_processor.py - SQS batch processor with partial failure reporting
import json
import logging

logger = logging.getLogger()
logger.setLevel("INFO")

def handler(event, context):
    """Process SQS messages with partial batch failure reporting."""
    failed_ids = []

    for record in event["Records"]:
        try:
            body = json.loads(record["body"])
            logger.info("Processing message: %s", record["messageId"])
            process_message(body)
        except Exception as e:
            logger.error("Failed message %s: %s", record["messageId"], e)
            failed_ids.append(record["messageId"])

    # Return failed items so only those get retried
    return {
        "batchItemFailures": [
            {"itemIdentifier": msg_id} for msg_id in failed_ids
        ]
    }

def process_message(body):
    pass  # your logic here

Lambda Layers

bash
# Build a layer for Python dependencies
mkdir -p layer/python
pip install requests boto3-stubs -t layer/python/
cd layer
zip -r ../my-layer.zip python/

# Publish the layer
aws lambda publish-layer-version \
  --layer-name common-deps \
  --description "Shared Python dependencies" \
  --zip-file fileb://my-layer.zip \
  --compatible-runtimes python3.11 python3.12 \
  --compatible-architectures arm64 x86_64

# Attach layer to a function
aws lambda update-function-configuration \
  --function-name my-api-handler \
  --layers "arn:aws:lambda:us-east-1:123456789012:layer:common-deps:1"

# List available layers
aws lambda list-layers --compatible-runtime python3.12

Event Source Mappings

bash
# SQS trigger with batch processing
aws lambda create-event-source-mapping \
  --function-name sqs-processor \
  --event-source-arn arn:aws:sqs:us-east-1:123456789012:my-queue \
  --batch-size 10 \
  --maximum-batching-window-in-seconds 5 \
  --function-response-types ReportBatchItemFailures

# DynamoDB Streams trigger
aws lambda create-event-source-mapping \
  --function-name stream-processor \
  --event-source-arn arn:aws:dynamodb:us-east-1:123456789012:table/my-table/stream/2026-01-01T00:00:00.000 \
  --batch-size 100 \
  --starting-position LATEST \
  --maximum-retry-attempts 3 \
  --bisect-batch-on-function-error \
  --destination-config '{"OnFailure":{"Destination":"arn:aws:sqs:us-east-1:123456789012:dlq"}}'

# S3 event notification (via Lambda permission + S3 config)
aws lambda add-permission \
  --function-name image-processor \
  --statement-id s3-trigger \
  --action lambda:InvokeFunction \
  --principal s3.amazonaws.com \
  --source-arn arn:aws:s3:::my-uploads-bucket \
  --source-account 123456789012

aws s3api put-bucket-notification-configuration \
  --bucket my-uploads-bucket \
  --notification-configuration '{
    "LambdaFunctionConfigurations": [{
      "LambdaFunctionArn": "arn:aws:lambda:us-east-1:123456789012:function:image-processor",
      "Events": ["s3:ObjectCreated:*"],
      "Filter": {"Key": {"FilterRules": [{"Name": "suffix", "Value": ".jpg"}]}}
    }]
  }'

# Schedule with EventBridge (cron)
aws events put-rule \
  --name daily-cleanup \
  --schedule-expression "cron(0 2 * * ? *)" \
  --state ENABLED

aws lambda add-permission \
  --function-name daily-cleanup \
  --statement-id eventbridge \
  --action lambda:InvokeFunction \
  --principal events.amazonaws.com \
  --source-arn arn:aws:events:us-east-1:123456789012:rule/daily-cleanup

aws events put-targets \
  --rule daily-cleanup \
  --targets '[{"Id":"1","Arn":"arn:aws:lambda:us-east-1:123456789012:function:daily-cleanup"}]'

Function URLs (No API Gateway Needed)

bash
# Create a function URL (public HTTPS endpoint)
aws lambda create-function-url-config \
  --function-name my-api-handler \
  --auth-type NONE \
  --cors '{
    "AllowOrigins": ["https://myapp.com"],
    "AllowMethods": ["GET", "POST"],
    "AllowHeaders": ["Content-Type"],
    "MaxAge": 86400
  }'

# Grant public invoke for function URL
aws lambda add-permission \
  --function-name my-api-handler \
  --statement-id function-url-public \
  --action lambda:InvokeFunctionUrl \
  --principal "*" \
  --function-url-auth-type NONE

Cold Start Optimization

bash
# Enable provisioned concurrency to eliminate cold starts
aws lambda put-provisioned-concurrency-config \
  --function-name my-api-handler \
  --qualifier live \
  --provisioned-concurrent-executions 10

# Set reserved concurrency (throttle limit)
aws lambda put-function-concurrency \
  --function-name my-api-handler \
  --reserved-concurrent-executions 100

# Enable SnapStart for Java functions (near-zero cold starts)
aws lambda update-function-configuration \
  --function-name my-java-handler \
  --snap-start '{"ApplyOn": "PublishedVersions"}'
aws lambda publish-version --function-name my-java-handler

Cold start reduction tips:

  • Use arm64 architecture (Graviton) for faster init and lower cost
  • Minimize deployment package size; use layers for large dependencies
  • Initialize SDK clients outside the handler function
  • Avoid VPC unless required (VPC cold starts are longer)
  • Use provisioned concurrency for latency-sensitive paths

SAM Template

yaml
# template.yaml - AWS SAM application
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Description: My serverless API

Globals:
  Function:
    Runtime: python3.12
    Architectures: [arm64]
    MemorySize: 256
    Timeout: 30
    Tracing: Active
    Environment:
      Variables:
        STAGE: !Ref Stage
        LOG_LEVEL: INFO

Parameters:
  Stage:
    Type: String
    Default: dev
    AllowedValues: [dev, staging, prod]

Resources:
  ApiFunction:
    Type: AWS::Serverless::Function
    Properties:
      FunctionName: !Sub "${Stage}-api-handler"
      Handler: app.handler
      CodeUri: src/
      Layers:
        - !Ref DepsLayer
      Events:
        GetItems:
          Type: Api
          Properties:
            Path: /items
            Method: get
        PostItem:
          Type: Api
          Properties:
            Path: /items
            Method: post
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref ItemsTable

  QueueProcessor:
    Type: AWS::Serverless::Function
    Properties:
      FunctionName: !Sub "${Stage}-queue-processor"
      Handler: sqs_processor.handler
      CodeUri: src/
      Events:
        SQSEvent:
          Type: SQS
          Properties:
            Queue: !GetAtt ProcessingQueue.Arn
            BatchSize: 10
            FunctionResponseTypes:
              - ReportBatchItemFailures

  DepsLayer:
    Type: AWS::Serverless::LayerVersion
    Properties:
      LayerName: common-deps
      ContentUri: layer/
      CompatibleRuntimes:
        - python3.12

  ItemsTable:
    Type: AWS::DynamoDB::Table
    Properties:
      TableName: !Sub "${Stage}-items"
      BillingMode: PAY_PER_REQUEST
      AttributeDefinitions:
        - AttributeName: id
          AttributeType: S
      KeySchema:
        - AttributeName: id
          KeyType: HASH

  ProcessingQueue:
    Type: AWS::SQS::Queue
    Properties:
      QueueName: !Sub "${Stage}-processing"
      VisibilityTimeout: 360

Outputs:
  ApiEndpoint:
    Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.amazonaws.com/Prod"
bash
# SAM CLI commands
sam build
sam local invoke ApiFunction --event events/get-items.json
sam local start-api --port 3000
sam deploy --guided
sam logs --name ApiFunction --stack-name my-stack --tail

Troubleshooting

ProblemCauseFix
Function times outTimeout too low or downstream slowIncrease timeout; check VPC/NAT config
Out of memoryMemory limit too smallIncrease --memory-size; profile with CloudWatch Insights
Permission denied on AWS APIExecution role missing policyAttach required policy to the execution role
Cold starts > 5sLarge package or VPC overheadUse layers, arm64, provisioned concurrency; remove VPC if not needed
SQS messages reprocessedVisibility timeout < function timeoutSet queue visibility timeout to 6x function timeout
Event source mapping disabledToo many consecutive errorsFix the function error; re-enable the mapping
Layer not foundWrong region or deleted versionVerify layer ARN region matches function region
Canary deployment not shiftingAlias routing config wrongVerify version numbers in routing config
Cannot invoke function URLMissing resource-based policyAdd lambda:InvokeFunctionUrl permission

Related Skills

Frequently asked questions

What does the Aws Lambda AI skill do?

Build and deploy serverless functions on AWS Lambda. Configure triggers, manage permissions, and optimize performance. Use when implementing serverless applications.

Why use Aws Lambda on TypingMind?

Because you install it once and use it with any model. Aws Lambda is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Lambda in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-aws/aws-lambda. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Lambda?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Lambda?

As many as you like. As long as a model supports skills, you can use Aws Lambda with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Lambda AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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