cost-optimization-report

name	cost-optimization-report
enabled	true
description	Use when performing cost optimization report — generate a comprehensive cloud cost optimization report by analyzing spending patterns, identifying waste, and recommending savings opportunities across AWS, GCP, or Azure. Covers idle resource detection, rightsizing, reserved instance/commitment analysis, and prioritized savings recommendations.
required_connections	[{"prefix":"aws","label":"AWS"}]
config_fields	[{"key":"cloud_provider","label":"Cloud Provider","required":true,"placeholder":"e.g., aws, gcp, azure"},{"key":"time_period","label":"Analysis Period","required":false,"placeholder":"e.g., last 30 days, last quarter"},{"key":"cost_threshold","label":"Minimum Monthly Waste Threshold ($)","required":false,"placeholder":"e.g., 100"},{"key":"focus_areas","label":"Focus Areas (optional)","required":false,"placeholder":"e.g., compute, storage, network, database"}]
features	["COST"]

Cloud Cost Optimization Report Skill

Generate a structured cost optimization report for {{ cloud_provider | uppercase }} covering {{ time_period | "last 30 days" }}.

Workflow

Step 1 — Establish Cost Baseline

Gather current spending data:

1. Total monthly spend — current month vs prior 3 months
2. Cost breakdown by service — top 10 services by spend
3. Cost by team/account/project — identify top-spending business units
4. Month-over-month trend — is spend growing, stable, or declining?
5. Budget vs actuals — are budgets being exceeded?

Expected output from this step:

COST BASELINE — {{ cloud_provider | uppercase }}
Period: {{ time_period }}

Total Spend: $X,XXX/month
MoM Change: +/-X%
Top Services:
  1. [Service]: $X,XXX (XX%)
  2. [Service]: $X,XXX (XX%)
  ...

Step 2 — Idle & Unused Resource Detection

For each service category, identify resources with zero or near-zero utilization:

Compute:

• Stopped/terminated EC2/GCE/VMs still incurring storage costs
• EC2 instances with CPU < 5% over 14 days
• Auto Scaling Groups at minimum capacity 24/7 (no scaling events)

Storage:

• Unattached EBS volumes / Persistent Disks
• Snapshots older than 90 days without recent access
• S3 buckets with no GET requests in 30 days (excluding logging buckets)
• Empty storage containers / buckets

Networking:

• Elastic IPs not associated with running instances
• Idle NAT Gateways (< 100MB/day transfer)
• Unused Load Balancers (0 healthy targets or 0 requests)
• Unattached Elastic Network Interfaces

Database:

• RDS/Cloud SQL instances with CPU < 2% and zero connections for 7 days
• Read replicas with zero replica lag and zero queries

Other:

• Unused Elastic Container Registries with images > 1 year old
• CloudFormation stacks in ROLLBACK state
• Forgotten development environments running in production accounts

Threshold for waste reporting: ${{ cost_threshold | "50" }}/month minimum

Step 3 — Rightsizing Analysis

Identify over-provisioned resources:

Compute Rightsizing:

For each compute instance with metrics:
- Average CPU over 14 days < 20% → downsize
- Average memory over 14 days < 30% → downsize
- Max CPU burst < 50% → no need for burstable exclusion
- Calculate estimated savings from downsizing

Database Rightsizing:

For each database instance:
- Average CPU < 15% → eligible for smaller instance class
- FreeableMemory average > 75% of allocated → memory over-provisioned
- IOPS average < 30% of provisioned → IOPS over-provisioned (if io1/io2)

Step 4 — Commitment / Reserved Capacity Analysis

Evaluate savings from longer-term commitments:

1. On-Demand vs Reserved Instance coverage

   • What % of compute is on-demand vs reserved?
   • Which on-demand instances are running 24/7 (RI candidates)?
   • Reserved Instance utilization — are purchased RIs being used?

2. Savings Plan analysis

   • What is current Savings Plan coverage?
   • Recommended Savings Plan commitment amount

3. Commitment recommendation:

   • Stable workloads running > 80% of time → 1-year reserved/committed
   • Very stable workloads → 3-year for maximum savings
   • Variable workloads → on-demand or spot-eligible

Step 5 — Spot / Preemptible Opportunities

Identify workloads that can run on spot/preemptible instances:

• Batch processing jobs
• CI/CD build agents
• Dev/test environments
• Stateless application tier (with proper interruption handling)
• ML training workloads

Estimated savings: 60-80% vs on-demand pricing

Step 6 — Data Transfer & Network Costs

Analyze network spend:

• Cross-region data transfer costs
• NAT Gateway usage vs VPC endpoints (S3, DynamoDB)
• CloudFront vs direct S3 serving for public assets
• VPC endpoint usage opportunities (eliminate NAT for AWS service traffic)

Step 7 — Anomaly Detection

Identify unusual cost spikes:

• Services with >20% MoM increase without corresponding business growth
• New services with unexpected costs (developer experiments left running)
• Data transfer spikes (potential data exfiltration or misconfigured logging)
• Unexpected region usage

Step 8 — Prioritized Recommendations

Rank all findings by impact and implementation effort:

SAVINGS RECOMMENDATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

QUICK WINS (implement this week) — Low risk, immediate savings
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
1. [Resource]: [Description]
   Monthly savings: $X,XXX | Risk: Low | Effort: Hours
   Action: [specific command or step]

2. [Resource]: [Description]
   Monthly savings: $XXX | Risk: Low | Effort: Minutes
   Action: [specific command or step]

MEDIUM TERM (implement this month) — Some coordination needed
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
3. Rightsizing [service]: [X] instances downsize from [type] to [type]
   Monthly savings: $X,XXX | Risk: Medium | Effort: Days
   Action: [specific steps with validation approach]

4. Reserved Instances: Purchase [X] RIs for [service]
   Monthly savings: $X,XXX | Risk: Low | Effort: Hours (approval needed)
   Action: [purchase via console/API]

STRATEGIC (this quarter) — Requires architectural changes
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
5. [Architectural change]: [description]
   Monthly savings: $X,XXX | Risk: High | Effort: Weeks
   Action: [high-level steps and considerations]

Step 9 — Executive Summary

Produce a one-page summary:

COST OPTIMIZATION REPORT — {{ cloud_provider | uppercase }}
Generated: [date]
Period: {{ time_period }}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

CURRENT STATE
• Monthly Spend: $X,XXX
• MoM Trend: [+/-X%]
• Top Cost Driver: [service]

SAVINGS IDENTIFIED
• Quick Wins: $X,XXX/month (implement in <1 week)
• Medium Term: $X,XXX/month (implement in <1 month)
• Strategic: $X,XXX/month (implement in <1 quarter)
• Total Potential: $XX,XXX/month (XX% reduction)

TOP 3 RECOMMENDATIONS
1. [action]: $X,XXX/month
2. [action]: $X,XXX/month
3. [action]: $X,XXX/month

NEXT STEPS
• [Owner]: [Action] by [Date]
• [Owner]: [Action] by [Date]

Counter-Rationalizations

Shortcut	Counter	Why
"We can skip some steps for this case"	Adapt the workflow steps, don't skip them	Skipped steps are where incidents and oversights originate
"The user seems to already know what to do"	Complete all workflow phases with the user	The workflow catches blind spots that experience alone misses
"This is a minor case, full process is overkill"	Scale the process down, don't turn it off	Minor cases become major when unstructured; the process scales, not disappears
"I'll fill in the details later"	Complete each section before moving on	Deferred details are forgotten; real-time capture is more accurate
"The template output isn't necessary"	Always produce the structured output format	Structured output enables comparison, audit trails, and handoff to other teams

Output Format

Produce a structured report with:

1. Cost baseline with trends
2. Waste inventory (idle/unused resources with monthly cost)
3. Rightsizing candidates with specific recommendations
4. Commitment opportunities with ROI calculations
5. Prioritized action plan sorted by savings impact
6. Executive summary for leadership sharing