Install
$ agentstack add skill-arbazkhan971-godmode-cost ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Cost — Cloud Cost Optimization
Activate When
- User invokes
/godmode:cost - User says "reduce cloud costs," "optimize spending," "why is our bill so high?"
- User asks about right-sizing, reserved instances, or spot pricing
- Godmode orchestrator detects infrastructure cost concerns
- After
/godmode:infraprovisions resources that need cost governance
Workflow
Step 1: Inventory Cloud Resources
Discover all provisioned resources and their current costs:
COST INVENTORY:
Provider:
Account(s):
Region(s):
Time period:
Resource categories:
Compute:
Storage:
Database:
Network:
Containers:
Serverless:
Other:
Step 2: Utilization Analysis
Measure actual usage versus provisioned capacity:
Compute Utilization
COMPUTE UTILIZATION:
| Instance | Type | Avg CPU | Avg Mem | Verdict |
|--|--|--|--|--|
| | m5.2xl | 12% | 25% | OVERSIZE |
| | t3.micro | 89% | 92% | UNDERSIZE |
| | c5.large | 45% | 60% | OK |
| | m5.xl | 3% | 8% | IDLE |
Thresholds:
IDLE: 80% CPU or > 85% memory sustained
OK: within healthy range
Storage Utilization
STORAGE UTILIZATION:
| Bucket/Volume | Size | Access | Last Hit | Verdict |
|--|--:|--|--|--|
| | 2.3 TB | Frequent | Today | OK |
| | 500 GB | None | 90d ago | ARCHIVE |
| | 1 TB | None | Never | DELETE |
| | 200 GB | N/A | 180d ago | DELETE |
Database Utilization
DATABASE UTILIZATION:
| Instance | Type | Avg CPU | Storage | Verdict |
|--|--|--:|--:|--|
| | db.r5.xl | 35% | 40% | OK |
| | db.r5.xl | 5% | 10% | OVERSIZE |
| | db.m5.lg | 2% | 5% | SCHEDULE |
Step 3: Waste Detection
Identify resources that cost money but provide no value:
WASTE DETECTION:
| Category | Count | Monthly Cost | Action |
|--|--|--|--|
| Unattached EBS vols | 12 | $340 | DELETE |
| Old snapshots (>90d) | 45 | $180 | DELETE |
| Idle load balancers | 3 | $75 | DELETE |
| Unused Elastic IPs | 8 | $29 | RELEASE |
| Orphaned ENIs | 5 | $0 | CLEANUP |
| Dev envs running 24/7 | 4 | $1,200 | SCHEDULE |
| Oversized instances | 6 | $2,400 waste | RESIZE |
| Stale DNS records | 15 | $0 | CLEANUP |
Total identifiable waste: $4,224/month ($50,688/year)
Step 4: Right-Sizing Recommendations
For each oversized or undersized resource, recommend the optimal size:
RIGHT-SIZING RECOMMENDATIONS:
| Resource | Current | Recommended | Monthly Savings |
|--|--|--|--|
| | m5.2xlarge | m5.large | $180 (65% less) |
| | db.r5.xl | db.t3.medium | $420 (78% less) |
| | r6g.xlarge | r6g.large | $95 (50% less) |
| | m5.xlarge | TERMINATE | $140 (100% saved) |
Basis: 14-day P95 utilization data.
Risk: LOW — all recommendations leave 40%+ headroom above P95.
Step 5: Pricing Optimization
Recommend pricing model changes for stable workloads:
Reserved Instances / Savings Plans
RESERVATION RECOMMENDATIONS:
| Resource | On-Demand | Reserved(1y) | Savings |
|--|--|--|--|
| Prod compute (6x) | $2,400/mo | $1,560/mo | $840/mo (35%) |
| Prod database (2x) | $1,200/mo | $780/mo | $420/mo (35%) |
| Prod cache (2x) | $380/mo | $247/mo | $133/mo (35%) |
Prerequisites: Workload must have run for 3+ months with stable utilization.
Commitment: 1-year, no upfront (lowest risk).
Spot / Preemptible Instances
SPOT CANDIDATES:
- CI/CD runners: instances, tolerant of interruption → 60-70% savings
- Batch processing: instances, can retry → 60-70% savings
- Dev environments: instances, non-critical → 60-70% savings
NOT spot-eligible: production web servers, databases, stateful services.
Step 6: Cost Allocation & Tagging
Verify all resources are tagged for cost attribution:
TAGGING AUDIT:
| Required Tag | Coverage | Missing | Action |
|--|--|--|--|
| team | 72% | 45 res | TAG |
| environment | 85% | 24 res | TAG |
| project | 60% | 64 res | TAG |
| cost-center | 45% | 88 res | TAG |
| owner | 55% | 72 res | TAG |
Recommended tagging policy:
REQUIRED: team, environment, project, cost-center
RECOMMENDED: owner, created-by, expiry-date
ENFORCED VIA: AWS Config rules / GCP Organization Policy / Azure Policy
Step 7: Budget Alerts
Set up proactive cost monitoring:
BUDGET ALERT CONFIGURATION:
| Budget | Monthly Limit | Alert at | Notify |
|--|--|--|--|
| Total account | $15,000 | 50/80/100% | #finops, PagerDuty |
| Production | $10,000 | 80/100% | #infra |
| Development | $3,000 | 80/100% | #dev-team |
| Per-service | varies | 100/120% | service owner |
Anomaly detection:
- Alert if daily spend exceeds 2x rolling 7-day average
- Alert if any single resource exceeds $500/day
- Weekly cost digest to #finops channel
Step 8: Cost Optimization Report
COST OPTIMIZATION REPORT
Current monthly spend: $
Projected after optimization: $
Total monthly savings: $ ()
Annual impact: $
Savings breakdown:
Waste elimination: $ ( actions)
Right-sizing: $ ( resources)
Pricing optimization: $ ( reservations)
Scheduling: $ ( environments)
Implementation effort:
Quick wins ( savings
Medium effort (1 week): $ savings
Long-term (1 month+): $ savings
Risk: LOW — all changes are reversible
Step 9: Commit and Transition
- Save report as
docs/cost/-cost-optimization.md - Commit:
"cost: — $/month identified ( recommendations)" - Provide actionable next steps with priority order
# Check cloud cost reports
curl -s http://localhost:8080/api/costs/summary | jq .total
grep -r "instance_type" infra/ | head -5
Key Behaviors
# Analyze cloud costs
aws ce get-cost-and-usage --time-period Start=2026-02-01,End=2026-03-01 --granularity MONTHLY --metrics BlendedCost
infracost diff --path .
- Data-driven only. Actual utilization data, not assumptions.
- Dollar impact required. "$180/mo savings" not "oversized".
- Risk assessment. LOW/MEDIUM/HIGH per recommendation.
- Reversibility matters. Right-sizing > reserved purchases.
- Environment awareness. Conservative for prod, aggressive for dev.
- Tagging is foundational. Fix tags before optimizing.
- Continuous, not one-time. Alerts + monthly review.
On failure: revert with git reset --hard HEAD~1.
Flags & Options
| Flag | Description | |--|--| | (none) | Full cost analysis and optimization report | | --provider | Target specific cloud provider | | --scope | Narrow analysis scope | | --waste | Waste detection only | | --rightsize | Right-sizing recommendations only | | --tags | Cost allocation tagging audit only | | --budget | Budget alert configuration only | | --quick | Top 10 savings opportunities, skip deep analysis | | --report | Generate report from last analysis | | --threshold | Only show savings above threshold |
HARD RULES
- NEVER STOP until all resource categories are analyzed and all savings are quantified in dollars.
- EVERY recommendation MUST include dollar impact — "oversized" is not actionable, "$180/month savings" is.
- EVERY recommendation MUST include risk level and reversibility assessment.
- NEVER recommend reserved instances for workloads with less than 3 months of stable data.
- NEVER apply dev-level aggressive optimization to production resources.
- ALWAYS fix tagging first — cost optimization without attribution is guesswork.
- git commit BEFORE verify — commit the cost report, then verify recommendations.
- TSV logging — log every cost analysis:
`` timestamp provider scope current_spend projected_savings recommendations quick_wins ``
Explicit Loop Protocol
When analyzing resources across categories:
current_iteration = 0
resource_categories = [compute, storage, database, network, containers, serverless, other]
all_recommendations = []
WHILE resource_categories is not empty:
current_iteration += 1
category = resource_categories.pop(0)
# Inventory
resources = list_resources(category)
FOR each resource in resources:
utilization = get_utilization(resource, period="14d")
IF utilization.cpu_avg /dev/null && echo "aws"
gcloud config get-value project 2>/dev/null && echo "gcp"
az account show 2>/dev/null && echo "azure"
2. Infrastructure as code:
ls terraform/ *.tf 2>/dev/null && echo "terraform"
ls pulumi/ Pulumi.yaml 2>/dev/null && echo "pulumi"
ls cdk.json 2>/dev/null && echo "cdk"
3. Resource inventory tools:
which aws-nuke cloud-nuke infracost 2>/dev/null
4. Existing cost tools:
Output Format
Print on completion: Cost: ${current_monthly}/mo → ${projected_monthly}/mo (-${savings}/mo, -{savings_pct}%). Top waste: {top_waste}. Untagged: {untagged_count} resources. Reservations: {ri_recommendation}. Verdict: {verdict}.
Keep/Discard
KEEP if: improvement verified. DISCARD if: regression or no change. Revert discards immediately.
Stop Conditions
Stop when: target reached, budget exhausted, or >5 consecutive discards.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: arbazkhan971
- Source: arbazkhan971/godmode
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.