Review AWS infrastructure code and deployed resources against the six Well-Architected pillars. Produce risk-ranked findings, remediation examples, and an architecture diagram, then create GitHub issues for tracking improvements after user confirmation.
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Perform an AWS Well-Architected Framework review of the current workload IaC and architecture, generating findings and GitHub issues for improvements.
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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**:
```bash
aws s3api put-bucket-encryption --bucket <name> \
--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]
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:
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Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
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Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
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Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.