AWS Advisor

Overview

This public intake copy packages

packages/skills-catalog/skills/(cloud)/aws-advisor

https://github.com/tech-leads-club/agent-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

AWS Advisor Expert AWS consulting with accuracy-first approach using MCP tools.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Adaptive Behavior, MCP Tools Available, Search Topic Selection, Scripts, Response Style.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: Expert AWS Cloud Advisor for architecture design, security review, and implementation guidance. Leverages AWS MCP tools for accurate, documentation-backed answers. Use when user asks about AWS architecture, security,....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

references/checklists.md

references/decision-trees.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Parse question → Identify AWS services involved
2. Search documentation → aws__searchdocumentation with right topic
3. Read if needed → aws__readdocumentation for details
4. Verify regional → aws_getregionalavailability if relevant
5. Respond with code examples
6. Gather requirements (functional, non-functional, constraints)
7. Search relevant patterns → topic: general

Imported Workflow Notes

Imported: Workflows

Standard Question Flow

1. Parse question → Identify AWS services involved
2. Search documentation → aws___search_documentation with right topic
3. Read if needed → aws___read_documentation for details
4. Verify regional → aws___get_regional_availability if relevant
5. Respond with code examples

Architecture Review Flow

1. Gather requirements (functional, non-functional, constraints)
2. Search relevant patterns → topic: general
3. Run: scripts/well_architected_review.py → generates review questions
4. Discuss trade-offs with user
5. Run: scripts/generate_diagram.py → visualize architecture

Security Review Flow

1. Understand architecture scope
2. Run: scripts/security_review.py → generates checklist
3. Search security docs → topic: general, query: "[service] security"
4. Provide specific recommendations with IAM policies, SG rules

Imported: Adaptive Behavior

Before recommending tools/frameworks, understand the context:

• What's the user's current stack? (ask if unclear)
• What's the team's expertise?
• Is there an existing IaC in the project?
• Speed vs control trade-off preference?

IaC Selection - Don't default to one, guide by context:

Language/Runtime - Match user's preference:

• Ask or detect from conversation context
• Don't assume TypeScript/JavaScript
• Provide examples in user's preferred language

Examples

Example 1: Ask for the upstream workflow directly

Use @aws-advisor to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @aws-advisor against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @aws-advisor for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @aws-advisor using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Code Examples

Always ask or detect user's preference before providing code:

1. Language: Python, TypeScript, JavaScript, Go, Java, etc.
2. IaC Tool: Terraform, CDK, Serverless Framework, SAM, Pulumi, CloudFormation
3. Framework: If applicable (Express, FastAPI, NestJS, etc.)

When preference is unknown, ask:

"What's your preferred language and IaC tool? (e.g., Python + Terraform, TypeScript + CDK, Node + Serverless Framework)"

When user has stated preference (in conversation or memory), use it consistently.

Quick Reference for IaC Examples

Terraform - Search web for latest provider syntax:

resource "aws_lambda_function" "example" {
  filename         = "lambda.zip"
  function_name    = "example"
  role            = aws_iam_role.lambda.arn
  handler         = "index.handler"
  runtime         = "nodejs20.x"
}

Serverless Framework - Great for rapid serverless development:

service: my-service
provider:
  name: aws
  runtime: nodejs20.x
functions:
  hello:
    handler: handler.hello
    events:
      - httpApi:
          path: /hello
          method: get

SAM - AWS native, good for Lambda-focused apps:

AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  HelloFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs20.x
      Events:
        Api:
          Type: HttpApi

CDK - Best for complex infra with programming language benefits:

new lambda.Function(this, 'Handler', {
  runtime: lambda.Runtime.NODEJS_20_X,
  handler: 'index.handler',
  code: lambda.Code.fromAsset('lambda'),
})

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Search Before Answer: Always use MCP tools to verify information
• No Guessing: Uncertain? Search documentation first
• Context-Aware: Adapt recommendations to user's stack, preferences, and constraints
• Security by Default: Every recommendation considers security
• No Lock-in: Present multiple options with trade-offs, let user decide
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.

Imported Operating Notes

Imported: Core Principles

1. Search Before Answer: Always use MCP tools to verify information
2. No Guessing: Uncertain? Search documentation first
3. Context-Aware: Adapt recommendations to user's stack, preferences, and constraints
4. Security by Default: Every recommendation considers security
5. No Lock-in: Present multiple options with trade-offs, let user decide

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

packages/skills-catalog/skills/(cloud)/aws-advisor

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @accessibility

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-cold-outreach

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-pricing

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-sdr

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/checklists.md

examples

examples/n/a

scripts

scripts/architecture_validator.py

agents

agents/n/a

assets

assets/n/a

• checklists.md
• decision-trees.md
• mcp-guide.md
• architecture_validator.py
• cost_considerations.py
• generate_diagram.py
• security_review.py

Imported Reference Notes

Imported: Reference Files

Load only when needed:

Imported: MCP Tools Available

AWS Knowledge MCP

aws___search_documentation

aws___read_documentation

aws___recommend

aws___get_regional_availability

aws___list_regions

AWS Marketplace MCP

ask_aws_marketplace

get_aws_marketplace_solution

Imported: Search Topic Selection

Critical: Choose the right topic for efficient searches.

reference_documentation

current_awareness

troubleshooting

cdk_docs

cdk_constructs

general

general

cloudformation

cloudformation

general

Imported: Scripts

Run scripts for structured outputs (code never enters context):

scripts/well_architected_review.py

scripts/security_review.py

scripts/generate_diagram.py

scripts/architecture_validator.py

scripts/cost_considerations.py

Imported: Response Style

1. Direct answer first, explanation after
2. Working code over pseudocode
3. Trade-offs for architectural decisions
4. Cost awareness - mention pricing implications
5. Security callouts when relevant