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Bedrock Skill for Claude

by itsmostafa

AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.

6k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
1139
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/itsmostafa/aws-agent-skills --skill bedrock

The instruction itself

28 sections, as written by the author

AWS Bedrock

Amazon Bedrock provides access to foundation models (FMs) from AI companies through a unified API. Build generative AI applications with text generation, embeddings, and image generation capabilities.

Table of Contents

  • Core Concepts
  • Common Patterns
  • CLI Reference
  • Best Practices
  • Troubleshooting
  • References

Core Concepts

Foundation Models

Pre-trained models available through Bedrock:

  • Claude (Anthropic): Text generation, analysis, coding
  • Titan (Amazon): Text, embeddings, image generation
  • Llama (Meta): Open-weight text generation
  • Mistral: Efficient text generation
  • Stable Diffusion (Stability AI): Image generation

Model Access

Models must be enabled in your account before use:

  • Request access in Bedrock console
  • Some models require acceptance of EULAs
  • Access is region-specific

Inference Types

| Type | Use Case | Pricing |

|------|----------|---------|

| On-Demand | Variable workloads | Per token |

| Provisioned Throughput | Consistent high-volume | Hourly commitment |

| Batch Inference | Async large-scale | Discounted per token |

Common Patterns

Invoke Model (Text Generation)

AWS CLI:

# Invoke Claude
aws bedrock-runtime invoke-model \
  --model-id anthropic.claude-3-sonnet-20240229-v1:0 \
  --content-type application/json \
  --accept application/json \
  --body '{
    "anthropic_version": "bedrock-2023-05-31",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Explain AWS Lambda in 3 sentences."}
    ]
  }' \
  response.json

cat response.json | jq -r '.content[0].text'

boto3:

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def invoke_claude(prompt, max_tokens=1024):
    response = bedrock.invoke_model(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': max_tokens,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )

    result = json.loads(response['body'].read())
    return result['content'][0]['text']

# Usage
response = invoke_claude('What is Amazon S3?')
print(response)

Streaming Response

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def stream_claude(prompt):
    response = bedrock.invoke_model_with_response_stream(
        modelId='anthropic.claude-3-sonnet-20240229-v1:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 1024,
            'messages': [
                {'role': 'user', 'content': prompt}
            ]
        })
    )

    for event in response['body']:
        chunk = json.loads(event['chunk']['bytes'])
        if chunk['type'] == 'content_block_delta':
            yield chunk['delta'].get('text', '')

# Usage
for text in stream_claude('Write a haiku about cloud computing.'):
    print(text, end='', flush=True)

Generate Embeddings

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def get_embedding(text):
    response = bedrock.invoke_model(
        modelId='amazon.titan-embed-text-v2:0',
        contentType='application/json',
        accept='application/json',
        body=json.dumps({
            'inputText': text,
            'dimensions': 1024,
            'normalize': True
        })
    )

    result = json.loads(response['body'].read())
    return result['embedding']

# Usage
embedding = get_embedding('AWS Lambda is a serverless compute service.')
print(f'Embedding dimension: {len(embedding)}')

Conversation with History

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

class Conversation:
    def __init__(self, system_prompt=None):
        self.messages = []
        self.system = system_prompt

    def chat(self, user_message):
        self.messages.append({
            'role': 'user',
            'content': user_message
        })

        body = {
            'anthropic_version': 'bedrock-2023-05-31',
            'max_tokens': 1024,
            'messages': self.messages
        }

        if self.system:
            body['system'] = self.system

        response = bedrock.invoke_model(
            modelId='anthropic.claude-3-sonnet-20240229-v1:0',
            contentType='application/json',
            accept='application/json',
            body=json.dumps(body)
        )

        result = json.loads(response['body'].read())
        assistant_message = result['content'][0]['text']

        self.messages.append({
            'role': 'assistant',
            'content': assistant_message
        })

        return assistant_message

# Usage
conv = Conversation(system_prompt='You are an AWS solutions architect.')
print(conv.chat('What database should I use for a chat application?'))
print(conv.chat('What about for time-series data?'))

List Available Models

# List all foundation models
aws bedrock list-foundation-models \
  --query 'modelSummaries[*].[modelId,modelName,providerName]' \
  --output table

# Filter by provider
aws bedrock list-foundation-models \
  --by-provider anthropic \
  --query 'modelSummaries[*].modelId'

# Get model details
aws bedrock get-foundation-model \
  --model-identifier anthropic.claude-3-sonnet-20240229-v1:0

Request Model Access

# List model access status
aws bedrock list-foundation-model-agreement-offers \
  --model-id anthropic.claude-3-sonnet-20240229-v1:0

CLI Reference

Bedrock (Control Plane)

| Command | Description |

|---------|-------------|

| aws bedrock list-foundation-models | List available models |

| aws bedrock get-foundation-model | Get model details |

| aws bedrock list-custom-models | List fine-tuned models |

| aws bedrock create-model-customization-job | Start fine-tuning |

| aws bedrock list-provisioned-model-throughputs | List provisioned capacity |

Bedrock Runtime (Data Plane)

| Command | Description |

|---------|-------------|

| aws bedrock-runtime invoke-model | Invoke model synchronously |

| aws bedrock-runtime invoke-model-with-response-stream | Invoke with streaming |

| aws bedrock-runtime converse | Multi-turn conversation API |

| aws bedrock-runtime converse-stream | Streaming conversation |

Bedrock Agent Runtime

| Command | Description |

|---------|-------------|

| aws bedrock-agent-runtime invoke-agent | Invoke a Bedrock agent |

| aws bedrock-agent-runtime retrieve | Query knowledge base |

| aws bedrock-agent-runtime retrieve-and-generate | RAG query |

Best Practices

Cost Optimization

  • Use appropriate models: Smaller models for simple tasks
  • Set max_tokens: Limit output length when possible
  • Cache responses: For repeated identical queries
  • Batch when possible: Use batch inference for bulk processing
  • Monitor usage: Set up CloudWatch alarms for cost

Performance

  • Use streaming: For better user experience with long outputs
  • Connection pooling: Reuse boto3 clients
  • Regional deployment: Use closest region to reduce latency
  • Provisioned throughput: For consistent high-volume workloads

Security

  • Least privilege IAM: Only grant needed model access
  • VPC endpoints: Keep traffic private
  • Guardrails: Implement content filtering
  • Audit with CloudTrail: Track model invocations

IAM Permissions

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "bedrock:InvokeModelWithResponseStream"
      ],
      "Resource": [
        "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0",
        "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v2:0"
      ]
    }
  ]
}

Troubleshooting

AccessDeniedException

Causes:

  • Model access not enabled in console
  • IAM policy missing bedrock:InvokeModel
  • Wrong model ID or region

Debug:

# Check model access status
aws bedrock list-foundation-models \
  --query 'modelSummaries[?modelId==`anthropic.claude-3-sonnet-20240229-v1:0`]'

# Test IAM permissions
aws iam simulate-principal-policy \
  --policy-source-arn arn:aws:iam::123456789012:role/my-role \
  --action-names bedrock:InvokeModel \
  --resource-arns "arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-3-sonnet-20240229-v1:0"

ModelNotReadyException

Cause: Model is still being provisioned or temporarily unavailable.

Solution: Implement retry with exponential backoff:

import time
from botocore.exceptions import ClientError

def invoke_with_retry(bedrock, body, max_retries=3):
    for attempt in range(max_retries):
        try:
            return bedrock.invoke_model(
                modelId='anthropic.claude-3-sonnet-20240229-v1:0',
                body=json.dumps(body)
            )
        except ClientError as e:
            if e.response['Error']['Code'] == 'ModelNotReadyException':
                time.sleep(2 ** attempt)
            else:
                raise
    raise Exception('Max retries exceeded')

ThrottlingException

Causes:

  • Exceeded on-demand quota
  • Too many concurrent requests

Solutions:

  • Request quota increase
  • Implement exponential backoff
  • Consider provisioned throughput

ValidationException

Common issues:

  • Invalid model ID
  • Malformed request body
  • max_tokens exceeds model limit

Debug:

# Check model-specific requirements
aws bedrock get-foundation-model \
  --model-identifier anthropic.claude-3-sonnet-20240229-v1:0 \
  --query 'modelDetails.inferenceTypesSupported'

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How to use it

Copy the folder

Take itsmostafa/bedrock from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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