skills/aws-well-architected-review/SKILL.md
Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.
npx skillsauth add williamlimasilva/.copilot aws-well-architected-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
Fetch current AWS WAF best practices:
https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.htmlScan the repository for IaC files:
**/*.tf**/*.yaml, **/*.json (CFn templates)lib/**/*.ts, bin/**/*.ts, cdk.jsonIdentify key AWS services in use (compute, data, networking, security, observability) and generate a Mermaid architecture diagram.
* actions without justification)enforceSSL: true)aws guardduty list-detectors)arm64, EC2 Graviton)arm64 architecture adopted (20% cost reduction)For each finding, classify:
🏗️ 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)
Label with "well-architected" and the pillar name (e.g., "security", "reliability").
Title: [WAF-<PILLAR>] [Brief Finding] — [Risk Level]
Body:
## 🏗️ 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:
aws s3api put-bucket-encryption --bucket <name> \
--server-side-encryption-configuration '{"Rules":[{"ApplyServerSideEncryptionByDefault":{"SSEAlgorithm":"aws:kms"}}]}'
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
development
Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.
devops
Query AWS resources using natural language. Covers EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, and more. Strictly read-only — no writes, deletes, or mutations.
devops
Analyze AWS resource health, diagnose issues from CloudWatch logs and metrics, and create a remediation plan for identified problems.
devops
Analyze AWS resources used in the app (IaC files and/or resources in a target account/region) and optimize costs - creating GitHub issues for identified optimizations.