Skill: 1kalin/afrexai-cloud-cost-audit (version 1.0.0)

# Cloud Cost Optimization Audit Analyze cloud infrastructure spend across AWS, Azure, and GCP. Identify waste, rightsizing opportunities, and reserved instance savings. ## What This Skill Does When given cloud spend data (billing exports, cost explorer screenshots, or manual input), this skill: 1. **Categorizes spend** across 8 cost domains (compute, storage, networking, databases, AI/ML, observability, security, licensing) 2. **Identifies waste patterns** using 12 common anti-patterns 3. **Calculates savings** with specific dollar amounts per optimization 4. **Prioritizes actions** by effort vs. impact (quick wins → strategic moves) 5. **Generates executive summary** with 90-day roadmap ## Cost Domains & Benchmarks (2026) ### 1. Compute (typically 40-55% of total) - **Idle instances**: >30% idle = waste. Benchmark: <10% idle capacity - **Rightsizing**: 60% of instances are oversized by 1+ size category - **Spot/preemptible**: Batch workloads not on spot = 60-80% overpay - **Reserved/savings plans**: On-demand for steady-state = 30-50% overpay - **Container density**: <40% CPU utilization on nodes = poor bin-packing ### 2. Storage (typically 10-20%) - **Tiering**: Data not accessed in 90 days still on hot storage = 60-80% overpay - **Snapshot sprawl**: Orphaned snapshots older than 30 days - **Duplicate data**: Cross-region replication without business justification - **Object lifecycle**: No lifecycle policies = guaranteed bloat ### 3. Networking (typically 8-15%) - **Cross-AZ traffic**: Unnecessary data transfer between zones ($0.01-0.02/GB) - **NAT gateway abuse**: High-throughput through NAT vs. VPC endpoints - **CDN miss rate**: >20% miss rate = CDN config issue - **Egress optimization**: No committed use discounts on egress ### 4. Databases (typically 10-20%) - **Over-provisioned RDS/Cloud SQL**: Multi-AZ for dev/staging environments - **Read replica sprawl**: Replicas with <5% query load - **DynamoDB/Cosmos over-provisioning**: Provisioned capacity 3x+ actual usage - **License waste**: Commercial DB when open-source works ### 5. AI/ML Infrastructure (growing — 5-25%) - **GPU idle time**: Training instances running 24/7 for 4hr/day workloads - **Inference over-provisioning**: GPU instances for CPU-viable inference - **Model storage**: Old model versions consuming storage - **API costs**: Frontier model API calls without caching layer ### 6. Observability (typically 3-8%) - **Log ingestion bloat**: Debug logs in production, duplicate log streams - **Metric cardinality**: High-cardinality custom metrics ($$$) - **Trace sampling**: 100% trace sampling when 10% suffices - **Retention overkill**: 13-month retention for non-compliance data ### 7. Security (typically 2-5%) - **WAF rule bloat**: Managed rule groups not actively tuned - **Key management**: KMS keys for non-sensitive data - **Compliance scanning**: Overlapping tools doing same checks ### 8. Licensing (typically 5-15%) - **Shelfware**: Paid seats not logged in 60+ days - **Duplicate tools**: Multiple tools solving same problem - **Enterprise tiers**: Enterprise features unused, paying enterprise price ## 12 Waste Anti-Patterns | # | Pattern | Typical Waste | Fix Effort | |---|---------|--------------|------------| | 1 | Zombie resources (stopped but attached) | 5-15% of bill | Low | | 2 | Over-provisioned instances | 15-30% compute | Medium | | 3 | No reserved capacity strategy | 25-40% compute | Medium | | 4 | Hot storage hoarding | 40-70% storage | Low | | 5 | Cross-AZ data transfer abuse | 10-30% network | Medium | | 6 | Dev/staging mirrors production | 20-40% of envs | Low | | 7 | Orphaned snapshots/AMIs | 3-8% storage | Low | | 8 | Log ingestion without sampling | 30-60% observability | Low | | 9 | GPU instances for CPU workloads | 70-85% compute | Medium | | 10 | No spot/preemptible for batch | 60-80% batch | Medium | | 11 | Shelfware licenses | 20-40% licensing | Low | | 12 | No tagging = no accountability | Unmeasurable | High | ## Savings Estimation Framework For each finding, calculate: ``` Annual Savings = (Current Cost - Optimized Cost) × 12 Implementation Cost = Engineering Hours × Loaded Rate ROI = (Annual Savings - Implementation Cost) / Implementation Cost Payback Period = Implementation Cost / (Annual Savings / 12) ``` ### Typical Savings by Company Size | Company Size | Monthly Cloud Spend | Typical Waste % | Annual Savings | |-------------|-------------------|----------------|---------------| | Startup (5-15) | $2K-$15K | 35-50% | $8K-$90K | | Growth (15-50) | $15K-$80K | 25-40% | $45K-$384K | | Mid-market (50-200) | $80K-$500K | 20-35% | $192K-$2.1M | | Enterprise (200+) | $500K-$5M+ | 15-25% | $900K-$15M+ | ## Output Format Generate a report with: 1. **Executive Summary**: Total spend, waste identified, savings potential, top 3 quick wins 2. **Domain Breakdown**: Spend per domain vs. benchmarks 3. **Findings Table**: Each finding with current cost, optimized cost, savings, effort, priority 4. **90-Day Roadmap**: Week 1-2 quick wins, Week 3-6 medium effort, Week 7-12 strategic 5. **Governance Recommendations**: Tagging strategy, budget alerts, review cadence ## Usage Provide your cloud billing data in any format: - AWS Cost Explorer export / Azure Cost Management / GCP Billing - Monthly bill summary - Architecture description with approximate sizing - Or just describe your stack and team size for estimates The agent will analyze and produce the full optimization report. --- ## Want Industry-Specific Cloud Optimization? 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Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)