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Identify and annotate cloud cost implications of architectural decisions. Use when reviewing infrastructure changes or designing new services.
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Original instructions from the publisher’s SKILL.md
# SKILL: FinOps Review
## Purpose
Identify and annotate the cloud cost implications of architectural decisions.
## Cost Driver Categories
### Compute
- Lambda/Functions: invocation count x duration x memory
- EC2/VMs: instance type x hours + data transfer
- Containers: CPU/memory reservation x hours
- Edge functions: request count x execution time
### Storage
- Object storage (S3/GCS/Blob): GB stored + requests + egress
- Database: instance size + storage + IOPS + backups
- Cache: node size x hours
### Networking
- Egress: GB transferred out (most expensive)
- CDN: requests + GB served
- Load balancer: hours + LCU
### Third-party APIs
- Stripe: 2.9% + $0.30 per transaction
- Supabase: row reads/writes, storage, Edge Function invocations
- Vercel: bandwidth, function invocations, seats
## Annotation Format (Terraform)
```hcl
resource "aws_db_instance" "main" {
# finops: ~$180/mo (db.t3.medium, 100GB gp3, 1 AZ)
# finops: scale-trigger: >70% CPU or >80% storage
instance_class = "db.t3.medium"
...
}
```
## Architecture Review Questions
1. What scales with user count? What scales with data volume?
2. Are there N+1 API calls that will cost $$ at scale?
3. Is egress minimized? (Cache aggressively, colocate services)
4. Are reserved instances/savings plans applicable?
5. What is the cost at 10x current load?
## Monthly Estimate Template
```markdown
## Cost Estimate: {Feature}
| Service | Config | Est. Monthly |
|---------|--------|-------------|
| RDS PostgreSQL | db.t3.medium, 100GB | $180 |
| ElastiCache Redis | cache.t3.micro | $25 |
| ECS Fargate | 2 tasks, 0.5 vCPU, 1GB | $30 |
| ALB | 1 ALB, ~1M requests | $20 |
| **Total** | | **~$255/mo** |
Scales linearly with: [traffic volume / data volume]
```