Agent skill

AWS Cloudformation Bedrock

by giuseppe-trisciuoglio in giuseppe-trisciuoglio/developer-kit

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles.

MITAuto-check: notesAI & LLM Engineering

Install AWS Cloudformation Bedrock

skills CLI
$ npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-bedrock -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install giuseppe-trisciuoglio/developer-kit aws-cloudformation-bedrock --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-bedrock .claude/skills/aws-cloudformation-bedrock && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aws-cloudformation-bedrock
GitHub stars
357
Token cost
~3.2k tokens
SKILL.md length
561 words
Files
4 (incl. references)
Skills in repo
115
Repo updated
First seen
Licence
MIT

At a glance

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles.

  • Works in 9 steps: Define Parameters → Create Agent Role → Create Agent → …
  • Creating Bedrock agents with action groups
  • SKILL.md covers Overview, When to Use, Instructions and Examples, plus 4 more sections
  • Calls aws

What it does

AWS Cloudformation Bedrock is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/constraints.md`, `references/examples.md` and `references/reference.md`).

It sits in AI & LLM Engineering, covering LLM guardrails, Knowledge bases and Retrieval-augmented generation. It works with AWS CloudFormation and Amazon Bedrock. The repository describes itself as: Modular plugin marketplace for Claude Code and agentic CLIs, with validated, spec-driven skills, agents, commands, and workflows for Java, TypeScript, Python, PHP, AWS, and AI. The licence is MIT.

When your agent uses it

  • Creating Bedrock agents with action groups
  • Implementing RAG with knowledge bases
  • Configuring vector stores
  • Setting up content moderation guardrails

Example prompts

  • “Use the aws-cloudformation-bedrock skill to provide AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data…”
  • “/aws-cloudformation-bedrock”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Bash

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Define Parameters
  2. Create Agent Role
  3. Create Agent
  4. Create Knowledge Base
  5. Create Data Source
  6. Add Guardrail
  7. Create Action Group
  8. Validate Before Deploy
  9. Verify After Deploy

What it can do on your machine

Read from SKILL.md and the folder at commit fe73fb3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • aws

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.aws.amazon.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AWS Cloudformation Bedrock loads about 3.2k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 561 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~117
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from giuseppe-trisciuoglio/developer-kit at commit fe73fb3, republished under its MIT licence (© giuseppe-trisciuoglio). 561 words, ~3,248 tokens.

Download SKILL.mdSave it as .claude/skills/aws-cloudformation-bedrock/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
aws-cloudformation-bedrock
description
Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.
allowed-tools
Read, Write, Bash

AWS CloudFormation Amazon Bedrock

Overview

Creates production-ready AI infrastructure using AWS CloudFormation templates for Amazon Bedrock. Covers Bedrock agents, knowledge bases for RAG implementations, data source connectors, guardrails for content moderation, prompt management, workflow orchestration with flows, and inference profiles for optimized model access.

When to Use

  • Creating Bedrock agents with action groups
  • Implementing RAG with knowledge bases
  • Configuring S3 or web crawl data sources
  • Setting up content moderation guardrails
  • Managing prompt templates
  • Orchestrating AI workflows with Bedrock Flows
  • Configuring inference profiles for multi-model access
  • Organizing templates with Parameters and cross-stack references

Instructions

1. Define Parameters
yaml
Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0
    AllowedValues:
      - anthropic.claude-3-sonnet-20240229-v1:0
      - anthropic.claude-3-haiku-20240307-v1:0
      - amazon.titan-text-express-v1
    Description: Foundation model for agent
2. Create Agent Role
yaml
Resources:
  AgentRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: BedrockPermissions
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - bedrock:InvokeModel
                Resource: !Sub "arn:aws:bedrock:${AWS::Region}:${AWS::AccountId}:foundation-model/${FoundationModel}"
3. Create Agent
yaml
  BedrockAgent:
    Type: AWS::Bedrock::Agent
    Properties:
      AgentName: !Sub "${AWS::StackName}-agent"
      AgentResourceRoleArn: !GetAtt AgentRole.Arn
      FoundationModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::foundation-model/${FoundationModel}"
      AutoPrepare: true
      Instruction: |
        You are a helpful assistant. Use the knowledge base to answer questions.
4. Create Knowledge Base
yaml
  KnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole

  KnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: !Sub "${AWS::StackName}-kb"
      RoleArn: !GetAtt KnowledgeBaseRole.Arn
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::embedding-model/amazon.titan-embed-text-v1"
5. Create Data Source
yaml
  DataBucket:
    Type: AWS::S3::Bucket

  S3DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref KnowledgeBase
      Name: s3-data-source
      Type: S3
      DataSourceConfiguration:
        S3Configuration:
          BucketArn: !GetAtt DataBucket.Arn
          InclusionPrefixes:
            - documents/
6. Add Guardrail
yaml
  Guardrail:
    Type: AWS::Bedrock::Guardrail
    Properties:
      Name: !Sub "${AWS::StackName}-guardrail"
      BlockedInputMessaging: "I cannot help with that request."
      ContentPolicyConfig:
        filtersConfig:
          - type: PROFANITY
          - type: MISCONDUCT
7. Create Action Group
yaml
  ActionLambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Runtime: python3.12
      Handler: index.handler
      Role: !GetAtt ActionLambdaRole.Arn
      Code:
        ZipFile: |
          def handler(event, context):
              return {"statusCode": 200, "body": "{\"result\": \"success\"}"}

  ActionGroup:
    Type: AWS::Bedrock::AgentActionGroup
    Properties:
      ActionGroupName: api-operations
      ActionGroupState: ENABLED
      AgentId: !GetAtt BedrockAgent.AgentId
      ActionGroupExecutor:
        Lambda: !Ref ActionLambdaFunction
      FunctionSchema:
        functionConfigurations:
          - function: |
              { "name": "get_inventory", "description": "Get current inventory status", "parameters": { "type": "object", "properties": { "sku": { "type": "string" } }, "required": [] } }
8. Validate Before Deploy

Always validate the template before deployment:

bash
aws cloudformation validate-template --template-body file://bedrock-template.yaml
9. Verify After Deploy
bash
# Check agent status
aws bedrock-agent get-agent --agent-id $(aws cloudformation describe-stacks --stack-name STACK_NAME --query 'Stacks[0].Outputs[?OutputKey==`AgentId`].OutputValue' --output text)

# Check knowledge base sync status
aws bedrock-agent list-knowledge-bases --agent-id AGENT_ID

# Test guardrail
aws bedrock-runtime apply_guardrail --guardrail-identifier GUARDRAIL_ID --source SOURCE

Examples

Minimal RAG Agent Template

Complete working template for a RAG-enabled agent:

yaml
AWSTemplateFormatVersion: "2010-09-09"
Description: "Bedrock RAG Agent with Knowledge Base"

Parameters:
  FoundationModel:
    Type: String
    Default: anthropic.claude-3-sonnet-20240229-v1:0

Resources:
  # IAM Role for Agent
  AgentRole:
    Type: AWS::IAM::Role
    Properties:
      RoleName: !Sub "${AWS::StackName}-agent-role"
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: InvokeModel
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action: bedrock:InvokeModel
                Resource: "*"

  # IAM Role for Knowledge Base
  KnowledgeBaseRole:
    Type: AWS::IAM::Role
    Properties:
      RoleName: !Sub "${AWS::StackName}-kb-role"
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: bedrock.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: S3Access
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action: s3:GetObject
                Resource: !Sub "${DataBucket.Arn}/*"

  # S3 Bucket for Documents
  DataBucket:
    Type: AWS::S3::Bucket

  # Knowledge Base
  KnowledgeBase:
    Type: AWS::Bedrock::KnowledgeBase
    Properties:
      Name: !Sub "${AWS::StackName}-kb"
      RoleArn: !GetAtt KnowledgeBaseRole.Arn
      KnowledgeBaseConfiguration:
        Type: VECTOR
        VectorKnowledgeBaseConfiguration:
          EmbeddingModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::embedding-model/amazon.titan-embed-text-v1"

  # Data Source
  DataSource:
    Type: AWS::Bedrock::DataSource
    Properties:
      KnowledgeBaseId: !Ref KnowledgeBase
      Name: !Sub "${AWS::StackName}-ds"
      Type: S3
      DataSourceConfiguration:
        S3Configuration:
          BucketArn: !GetAtt DataBucket.Arn

  # Bedrock Agent
  BedrockAgent:
    Type: AWS::Bedrock::Agent
    Properties:
      AgentName: !Sub "${AWS::StackName}-agent"
      AgentResourceRoleArn: !GetAtt AgentRole.Arn
      FoundationModelArn: !Sub "arn:aws:bedrock:${AWS::Region}::foundation-model/${FoundationModel}"
      AutoPrepare: true
      Instruction: |
        You are a helpful assistant. Use the knowledge base to answer user questions accurately.

Outputs:
  AgentId:
    Description: Bedrock Agent ID
    Value: !GetAtt BedrockAgent.AgentId
  KnowledgeBaseId:
    Description: Knowledge Base ID
    Value: !Ref KnowledgeBase
Guardrail with Content Filtering
yaml
Resources:
  Guardrail:
    Type: AWS::Bedrock::Guardrail
    Properties:
      Name: !Sub "${AWS::StackName}-guardrail"
      blockedInputMessaging: "Content blocked by safety filters."
      blockedOutputMessaging: "Response filtered for safety."
      contentPolicyConfig:
        filtersConfig:
          - type: PROFANITY
            inputStrength: HIGH
            outputStrength: HIGH
          - type: MISCONDUCT
            inputStrength: HIGH
            outputStrength: HIGH
      sensitiveInformationPolicyConfig:
        piiEntitiesConfig:
          - type: EMAIL
            action: ANONYMIZE
          - type: SSN
            action: BLOCK

Best Practices

Security
  • Use least privilege IAM policies for agent and knowledge base roles
  • Restrict web crawl data sources to trusted internal domains
  • Encrypt sensitive data in knowledge bases
  • Parameterize all TemplateURL values for nested stacks
Cost Optimization
  • Select appropriate model size for task complexity
  • Configure retrieval filtering to reduce token usage
  • Set chunk size limits to control storage costs
  • Monitor usage with CloudWatch dashboards
Performance
  • Optimize chunk size for embedding quality
  • Use provisioned throughput for high-traffic vector stores
  • Configure appropriate knowledge base sync intervals
  • Implement caching for frequently accessed content
Validation
  • Always run aws cloudformation validate-template before deploy
  • Verify agent status after stack creation completes
  • Test guardrails with sample inputs
  • Monitor knowledge base sync status in CloudWatch

Constraints and Warnings

For detailed limits, see constraints.md:

  • Regional limits: Not all models available in all regions
  • Agent initialization: AutoPrepare may take several minutes
  • Knowledge base sync: S3 sync is near-instant; web crawl takes longer
  • Web crawl security: Always restrict to trusted domains to prevent prompt injection
  • Token limits: Configure MaxTokens parameter for your use case
  • Quota management: Request quota increases via AWS Support if needed
Show full SKILL.md (229 more words)Show less
Security
  • Restrict web crawl data sources to trusted internal domains only
  • Validate content before ingesting into knowledge bases
  • Use parameterized TemplateURL values for nested stacks
  • Implement guardrails for content moderation
  • Apply least privilege IAM policies to agent roles
  • Encrypt sensitive data in knowledge bases
  • Monitor for prompt injection in web-crawled content
Cost Optimization
  • Use appropriate model selection for task complexity
  • Implement knowledge base retrieval filtering
  • Set chunk size limits to control token usage
  • Monitor token consumption with CloudWatch
  • Use auto-prepare agents strategically
  • Implement batch processing for non-real-time workloads
  • Use knowledge base filtering to reduce costs
Performance
  • Optimize chunk size for embedding quality vs. cost
  • Use vector store optimization (OpenSearch, Pinecone)
  • Implement caching for frequently accessed knowledge base content
  • Configure appropriate knowledge base sync intervals
  • Use provisioned throughput for vector databases
  • Monitor agent initialization and cold start times
  • Implement graceful degradation for rate limiting
Data Management
  • Use appropriate inclusion/exclusion filters for data sources
  • Implement document validation before indexing
  • Use versioning for knowledge base updates
  • Configure appropriate sync intervals for data sources
  • Implement content deduplication in knowledge bases
  • Use metadata filtering for improved retrieval accuracy
  • Monitor knowledge base size and document limits

References

  • constraints.md - Resource limits, regional constraints, operational limits, and cost considerations
  • reference.md - API reference and resource properties
  • examples.md - Additional usage examples

© giuseppe-trisciuoglio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-bedrock of giuseppe-trisciuoglio/developer-kit.

  • SKILL.md
  • references/constraints.md
  • references/examples.md
  • references/reference.md

Open the folder on GitHubat commit fe73fb3

Compare with similar skills

AWS Cloudformation Bedrock next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AWS Cloudformation Bedrock compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AWS Cloudformation Bedrock this skillgiuseppe-trisciuoglio/developer-kit357—~3.2kAutomated safety check: NotesMIT
Amazon Bedrockaws/agent-toolkit-for-aws2.8k—~8.6kAutomated safety check: PassApache-2.0
Sap AI Coresecondsky/sap-skills462—~3.3kAutomated safety check: PassGPL-3.0
Context Retrievalseb1n/awesome-ai-agent-skills206—~2.1kAutomated safety check: PassMIT
AI Engineerkid-sid/claude-spellbook190—~3.7kAutomated safety check: PassMIT
RAG Pipelinebrightdata/skills264—~1.9kAutomated safety check: PassMIT

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Questions about AWS Cloudformation Bedrock

What does AWS Cloudformation Bedrock do?

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. AWS Cloudformation Bedrock is an agent skill from giuseppe-trisciuoglio/developer-kit. Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles.

When should I use AWS Cloudformation Bedrock?

AWS Cloudformation Bedrock fits situations like: creating Bedrock agents with action groups; implementing RAG with knowledge bases; configuring vector stores; setting up content moderation guardrails.

How do I install AWS Cloudformation Bedrock in Claude Code?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-bedrock -a claude-code`. Or copy the skill folder (plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-bedrock in giuseppe-trisciuoglio/developer-kit) into .claude/skills/aws-cloudformation-bedrock in your project. Claude Code loads it when a task matches its description.

How do I install AWS Cloudformation Bedrock in Codex?

Run `npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-bedrock -a codex`. Or copy the skill folder (plugins/developer-kit-aws/skills/aws-cloudformation/aws-cloudformation-bedrock in giuseppe-trisciuoglio/developer-kit) into .agents/skills/aws-cloudformation-bedrock in your project. Codex loads it when a task matches its description.

Can I use AWS Cloudformation Bedrock in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-bedrock -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-cloudformation-bedrock, .gemini/skills/aws-cloudformation-bedrock, .github/skills/aws-cloudformation-bedrock and .opencode/skills/aws-cloudformation-bedrock in your project.

What does AWS Cloudformation Bedrock need to run?

Going by SKILL.md and its folder, AWS Cloudformation Bedrock needs the command-line tools its instructions call (aws). Its frontmatter pre-approves these tools: Read, Write, Bash.

Does AWS Cloudformation Bedrock access the network?

SKILL.md names 1 domain. As links in the text: docs.aws.amazon.com. This is read from the text; nothing was executed.

Is AWS Cloudformation Bedrock safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does AWS Cloudformation Bedrock use?

AWS Cloudformation Bedrock is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AWS Cloudformation Bedrock use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 21k tokens, read only when the agent opens those files.

What are the alternatives to AWS Cloudformation Bedrock?

Skills that share tags, products or a category with AWS Cloudformation Bedrock: Amazon Bedrock (aws/agent-toolkit-for-aws, 2.8k stars), Sap AI Core (secondsky/sap-skills, 462 stars), Context Retrieval (seb1n/awesome-ai-agent-skills, 206 stars) and AI Engineer (kid-sid/claude-spellbook, 190 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AWS Cloudformation Bedrock?

giuseppe-trisciuoglio (a GitHub user) maintains it in giuseppe-trisciuoglio/developer-kit, which has 357 GitHub stars. The repository holds 115 skills in this directory. The repository was last updated on September 10, 2026.

Source: giuseppe-trisciuoglio/developer-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.