Aws Well Architected Review logo

Aws Well Architected Review

OrganizationPopular
github
aws-well-architected-review

Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.

Overview

Publishergithub
Repositoryawesome-copilot
Skill nameaws-well-architected-review
Stars
39.1K
Forks
5K
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 github on GitHub. Read the source before you install it.

Installation

Install the Aws Well Architected Review 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/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p .claude/skills
cp -r /tmp/awesome-copilot/skills/aws-well-architected-review .claude/skills/aws-well-architected-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Well Architected Review 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 Well Architected Review 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 Well Architected Review 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 Well-Architected Review

This workflow performs a structured AWS Well-Architected Framework (WAF) review against your workload's IaC files and deployed infrastructure. It identifies risks across all 6 WAF pillars and creates GitHub issues to track remediation.

Prerequisites

  • AWS CLI configured and authenticated
  • IaC files present in the repository (Terraform, CloudFormation, CDK, or SAM)
  • GitHub MCP server configured and authenticated

Workflow Steps

Step 1: Load Well-Architected Framework Reference

Fetch current AWS WAF best practices:

  • https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html
  • Pillar-specific lenses relevant to the workload type (Serverless, SaaS, etc.)

Step 2: Discover IaC & Architecture

Scan the repository for IaC files:

  • Terraform: **/*.tf
  • CloudFormation/SAM: **/*.yaml, **/*.json (CFn templates)
  • CDK: lib/**/*.ts, bin/**/*.ts, cdk.json

Identify key AWS services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.

Step 3: Pillar-by-Pillar Review

Pillar 1: Operational Excellence
  • All infrastructure defined as IaC (no manual console changes)
  • Consistent tagging strategy applied across all resources
  • CloudWatch alarms defined for key metrics
  • Automated deployment pipeline present (no manual deployments)
  • CloudTrail enabled for audit logging
  • Runbooks or operational documentation present
Pillar 2: Security
  • IAM roles use least-privilege policies (no * actions without justification)
  • No hardcoded credentials in IaC or code
  • Secrets managed via Secrets Manager or SSM Parameter Store
  • S3 buckets have public access blocked and server-side encryption enabled
  • Sensitive resources placed in private subnets
  • Security groups restrict inbound to minimum required ports/CIDRs
  • KMS encryption enabled for sensitive data stores (RDS, EBS, S3, SQS, DynamoDB)
  • SSL/TLS enforced on all endpoints (enforceSSL: true)
  • GuardDuty enabled (aws guardduty list-detectors)
  • AWS WAF configured on public-facing APIs and CloudFront distributions
  • MFA delete enabled on critical S3 buckets
Pillar 3: Reliability
  • Multi-AZ deployments for production databases (RDS Multi-AZ, DynamoDB Global Tables)
  • Auto Scaling configured with appropriate policies for EC2/ECS
  • S3 versioning and lifecycle policies configured
  • RDS automated backups enabled with appropriate retention period
  • DynamoDB Point-in-Time Recovery (PITR) enabled
  • Dead Letter Queues (DLQ) configured for Lambda, SQS, SNS
  • Route 53 health checks configured for DNS failover
  • Lambda reserved concurrency set to prevent noisy-neighbor throttling
Pillar 4: Performance Efficiency
  • Right-sized instance types (Lambda memory, EC2 type, RDS class)
  • Graviton/ARM instances used where available (Lambda arm64, EC2 Graviton)
  • Caching implemented (ElastiCache, DAX, CloudFront, API Gateway caching)
  • CloudFront used for global static content delivery
  • Aurora Serverless or DynamoDB On-Demand for variable load patterns
  • Lambda Provisioned Concurrency for latency-critical synchronous paths
Pillar 5: Cost Optimization
  • EC2 Reserved Instances or Savings Plans for steady-state workloads
  • S3 lifecycle policies moving data to cheaper storage tiers
  • Lambda arm64 architecture adopted (20% cost reduction)
  • VPC Endpoints for S3/DynamoDB to avoid NAT Gateway charges
  • gp2 EBS volumes migrated to gp3 (same performance, 20% cheaper)
  • Development/test environments have auto-shutdown schedules
  • AWS Budgets and Cost Anomaly Detection configured
  • Unattached EBS volumes and idle EC2 instances identified
Pillar 6: Sustainability
  • Graviton/ARM instances selected where available
  • Serverless/managed services preferred over always-on EC2
  • S3 lifecycle policies reduce unnecessary long-term data storage
  • Auto Scaling configured to avoid over-provisioning
  • Region selection considers AWS renewable energy commitments

Step 4: Risk Classification

For each finding, classify:

  • High Risk: Security vulnerability, single point of failure, no backup/recovery
  • Medium Risk: Suboptimal reliability, cost inefficiency, performance concern
  • Low Risk: Best practice deviation, minor optimization opportunity

Step 5: User Confirmation

🏗️ AWS Well-Architected Review Summary

📊 Review Results:
• IaC Files Analyzed: X
• AWS Services Identified: Y
• Total Findings: Z
  • High Risk: A (immediate action required)
  • Medium Risk: B (should address soon)
  • Low Risk: C (nice to have)

🔴 Top High Risk Findings:
1. [Pillar]: [Finding] — [Why it matters]
2. [Pillar]: [Finding] — [Why it matters]

💡 This will create Z individual GitHub issues + 1 EPIC issue.

❓ Proceed with creating GitHub issues? (y/n)

Step 6: Create Individual Finding Issues

Label with "well-architected" and the pillar name (e.g., "security", "reliability").

Title: [WAF-<PILLAR>] [Brief Finding] — [Risk Level]

Body:

markdown
## 🏗️ Well-Architected Finding: [Brief Title]

**Pillar**: [Name] | **Risk Level**: [High/Medium/Low] | **Effort**: [Low/Medium/High]

### 📋 Description
[Clear explanation of the finding and why it matters]

### 🔧 Remediation

**IaC Fix** (preferred):
```hcl
# Terraform example
resource "aws_s3_bucket_server_side_encryption_configuration" "example" {
  bucket = aws_s3_bucket.example.id
  rule {
    apply_server_side_encryption_by_default {
      sse_algorithm = "aws:kms"
    }
  }
}
```

**AWS CLI fallback**:
```bash
aws s3api put-bucket-encryption --bucket  \
  --server-side-encryption-configuration '{"Rules":[{"ApplyServerSideEncryptionByDefault":{"SSEAlgorithm":"aws:kms"}}]}'
```

### 📚 AWS Reference
- [WAF Best Practice Link]
- [AWS Documentation Link]

### ✅ Validation
- [ ] Change implemented in IaC and deployed
- [ ] AWS Config rule passes (if applicable)
- [ ] Security Hub finding resolved (if applicable)

**Well-Architected Question**: [WAF question this maps to]

Step 7: Create EPIC Tracking Issue

Label with "well-architected" and "epic".

Title: [EPIC] AWS Well-Architected Review — X findings across 6 pillars

Body: Executive summary with pillar breakdown table (finding counts by pillar and risk level), Mermaid architecture diagram, prioritized checklist linking all individual issues (High → Medium → Low), and success criteria:

  • All High-risk findings resolved
  • Medium findings have accepted mitigation plans
  • No regression in existing CloudWatch alarms or Config rules

Error Handling

  • No IaC Files Found: Limit review to live resource discovery via AWS CLI and note the gap
  • Insufficient AWS Permissions: List required read-only permissions for the review
  • GitHub Creation Failure: Output all findings as formatted markdown to console

Success Criteria

  • ✅ All 6 WAF pillars reviewed against IaC and live infrastructure
  • ✅ All findings classified by risk level and pillar
  • ✅ Actionable remediation steps with IaC examples for each finding
  • ✅ GitHub issues created for team tracking
  • ✅ Architecture diagram generated for EPIC context
  • ✅ AWS documentation references included

Frequently asked questions

What does the Aws Well Architected Review AI skill do?

Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.

Why use Aws Well Architected Review on TypingMind?

Because you install it once and use it with any model. Aws Well Architected Review 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 Well Architected Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/aws-well-architected-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aws Well Architected Review?

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 Well Architected Review?

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

Is the Aws Well Architected Review AI skill free?

Yes. It is published on GitHub by github 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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