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Aws Serverless

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SamarthaKV29
aws-serverless

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Overview

PublisherSamarthaKV29
Repositoryantigravity-god-mode
Skill nameaws-serverless
Stars
69
Forks
16
Bundled files
Instructions only
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 SamarthaKV29 on GitHub. Read the source before you install it.

Installation

Install the Aws Serverless 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.
  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/SamarthaKV29/antigravity-god-mode.git /tmp/antigravity-god-mode
mkdir -p .claude/skills
cp -r /tmp/antigravity-god-mode/skills/aws-serverless .claude/skills/aws-serverless
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Serverless 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 Serverless 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 Serverless 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 Serverless

Patterns

Lambda Handler Pattern

Proper Lambda function structure with error handling

When to use: ['Any Lambda function implementation', 'API handlers, event processors, scheduled tasks']

python
```javascript
// Node.js Lambda Handler
// handler.js

// Initialize outside handler (reused across invocations)
const { DynamoDBClient } = require('@aws-sdk/client-dynamodb');
const { DynamoDBDocumentClient, GetCommand } = require('@aws-sdk/lib-dynamodb');

const client = new DynamoDBClient({});
const docClient = DynamoDBDocumentClient.from(client);

// Handler function
exports.handler = async (event, context) => {
  // Optional: Don't wait for event loop to clear (Node.js)
  context.callbackWaitsForEmptyEventLoop = false;

  try {
    // Parse input based on event source
    const body = typeof event.body === 'string'
      ? JSON.parse(event.body)
      : event.body;

    // Business logic
    const result = await processRequest(body);

    // Return API Gateway compatible response
    return {
      statusCode: 200,
      headers: {
        'Content-Type': 'application/json',
        'Access-Control-Allow-Origin': '*'
      },
      body: JSON.stringify(result)
    };
  } catch (error) {
    console.error('Error:', JSON.stringify({
      error: error.message,
      stack: error.stack,
      requestId: context.awsRequestId
    }));

    return {
      statusCode: error.statusCode || 500,
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        error: error.message || 'Internal server error'
      })
    };
  }
};

async function processRequest(data) {
  // Your business logic here
  const result = await docClient.send(new GetCommand({
    TableName: process.env.TABLE_NAME,
    Key: { id: data.id }
  }));
  return result.Item;
}
python
# Python Lambda Handler
# handler.py

import json
import os
import logging
import boto3
from botocore.exceptions import ClientError

# Initialize outside handler (reused across invocations)
logger = logging.getLogger()
logger.setLevel(logging.INFO)

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table(os.environ['TABLE_NAME'])

def handler(event, context):
    try:
        # Parse i

API Gateway Integration Pattern

REST API and HTTP API integration with Lambda

When to use: ['Building REST APIs backed by Lambda', 'Need HTTP endpoints for functions']

javascript
```yaml
# template.yaml (SAM)
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Globals:
  Function:
    Runtime: nodejs20.x
    Timeout: 30
    MemorySize: 256
    Environment:
      Variables:
        TABLE_NAME: !Ref ItemsTable

Resources:
  # HTTP API (recommended for simple use cases)
  HttpApi:
    Type: AWS::Serverless::HttpApi
    Properties:
      StageName: prod
      CorsConfiguration:
        AllowOrigins:
          - "*"
        AllowMethods:
          - GET
          - POST
          - DELETE
        AllowHeaders:
          - "*"

  # Lambda Functions
  GetItemFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: src/handlers/get.handler
      Events:
        GetItem:
          Type: HttpApi
          Properties:
            ApiId: !Ref HttpApi
            Path: /items/{id}
            Method: GET
      Policies:
        - DynamoDBReadPolicy:
            TableName: !Ref ItemsTable

  CreateItemFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: src/handlers/create.handler
      Events:
        CreateItem:
          Type: HttpApi
          Properties:
            ApiId: !Ref HttpApi
            Path: /items
            Method: POST
      Policies:
        - DynamoDBCrudPolicy:
            TableName: !Ref ItemsTable

  # DynamoDB Table
  ItemsTable:
    Type: AWS::DynamoDB::Table
    Properties:
      AttributeDefinitions:
        - AttributeName: id
          AttributeType: S
      KeySchema:
        - AttributeName: id
          KeyType: HASH
      BillingMode: PAY_PER_REQUEST

Outputs:
  ApiUrl:
    Value: !Sub "https://${HttpApi}.execute-api.${AWS::Region}.amazonaws.com/prod"
javascript
// src/handlers/get.js
const { getItem } = require('../lib/dynamodb');

exports.handler = async (event) => {
  const id = event.pathParameters?.id;

  if (!id) {
    return {
      statusCode: 400,
      body: JSON.stringify({ error: 'Missing id parameter' })
    };
  }

  const item =

Event-Driven SQS Pattern

Lambda triggered by SQS for reliable async processing

When to use: ['Decoupled, asynchronous processing', 'Need retry logic and DLQ', 'Processing messages in batches']

python
```yaml
# template.yaml
Resources:
  ProcessorFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: src/handlers/processor.handler
      Events:
        SQSEvent:
          Type: SQS
          Properties:
            Queue: !GetAtt ProcessingQueue.Arn
            BatchSize: 10
            FunctionResponseTypes:
              - ReportBatchItemFailures  # Partial batch failure handling

  ProcessingQueue:
    Type: AWS::SQS::Queue
    Properties:
      VisibilityTimeout: 180  # 6x Lambda timeout
      RedrivePolicy:
        deadLetterTargetArn: !GetAtt DeadLetterQueue.Arn
        maxReceiveCount: 3

  DeadLetterQueue:
    Type: AWS::SQS::Queue
    Properties:
      MessageRetentionPeriod: 1209600  # 14 days
javascript
// src/handlers/processor.js
exports.handler = async (event) => {
  const batchItemFailures = [];

  for (const record of event.Records) {
    try {
      const body = JSON.parse(record.body);
      await processMessage(body);
    } catch (error) {
      console.error(`Failed to process message ${record.messageId}:`, error);
      // Report this item as failed (will be retried)
      batchItemFailures.push({
        itemIdentifier: record.messageId
      });
    }
  }

  // Return failed items for retry
  return { batchItemFailures };
};

async function processMessage(message) {
  // Your processing logic
  console.log('Processing:', message);

  // Simulate work
  await saveToDatabase(message);
}
python
# Python version
import json
import logging

logger = logging.getLogger()

def handler(event, context):
    batch_item_failures = []

    for record in event['Records']:
        try:
            body = json.loads(record['body'])
            process_message(body)
        except Exception as e:
            logger.error(f"Failed to process {record['messageId']}: {e}")
            batch_item_failures.append({
                'itemIdentifier': record['messageId']
            })

    return {'batchItemFailures': batch_ite

Anti-Patterns

❌ Monolithic Lambda

Why bad: Large deployment packages cause slow cold starts. Hard to scale individual operations. Updates affect entire system.

❌ Large Dependencies

Why bad: Increases deployment package size. Slows down cold starts significantly. Most of SDK/library may be unused.

❌ Synchronous Calls in VPC

Why bad: VPC-attached Lambdas have ENI setup overhead. Blocking DNS lookups or connections worsen cold starts.

⚠️ Sharp Edges

IssueSeveritySolution
Issuehigh## Measure your INIT phase
Issuehigh## Set appropriate timeout
Issuehigh## Increase memory allocation
Issuemedium## Verify VPC configuration
Issuemedium## Tell Lambda not to wait for event loop
Issuemedium## For large file uploads
Issuehigh## Use different buckets/prefixes

Frequently asked questions

What does the Aws Serverless AI skill do?

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Why use Aws Serverless on TypingMind?

Because you install it once and use it with any model. Aws Serverless 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 Serverless in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SamarthaKV29/antigravity-god-mode/tree/main/skills/aws-serverless. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Serverless?

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 Serverless?

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

Is the Aws Serverless AI skill free?

It is published on GitHub by SamarthaKV29. Check the repository for licensing terms. 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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