Finops Intelligence

FinOps Intelligence

Purpose

Unified cost intelligence skill that identifies savings opportunities, tracks cost trends, and generates chargeback/showback reports by team or project. Answers both "where can we save?" and "who spent what?"

When to use this skill

• User asks "why did our bill go up?"
• User asks for cost optimization or savings opportunities
• User asks "how much did team X spend this month?"
• User asks for chargeback, showback, or cost allocation report
• Monthly proactive cost review or FinOps cadence

Pre-check

Confirm with the user:

• Scope: Which subscriptions to scan (all or specific ones)
• Time range: For trend analysis (default: last 3 months)
• Allocation model (for chargeback): Which tag to use for cost splitting?
• cost-center tag (most common)
• owner or team tag
• application or project tag
• Resource group naming convention (e.g., rg-teamname-*)
• Exclusions: Dev/test subscriptions, sandbox resource groups

Analysis procedure

Step 1: Cost trend overview

Get the big picture using Azure Cost Management.

# Current month cost by service (last 30 days)
az costmanagement query --type ActualCost --timeframe MonthToDate \
  --scope "subscriptions/" \
  --dataset-aggregation '{"totalCost":{"name":"Cost","function":"Sum"}}' \
  --dataset-grouping name="ServiceName" type="Dimension" \
  -o table

# Previous month for comparison
az costmanagement query --type ActualCost --timeframe TheLastMonth \
  --scope "subscriptions/" \
  --dataset-aggregation '{"totalCost":{"name":"Cost","function":"Sum"}}' \
  --dataset-grouping name="ServiceName" type="Dimension" \
  -o table

If az costmanagement is unavailable, use the REST API:

az rest --method post \
  --url "https://management.azure.com/subscriptions//providers/Microsoft.CostManagement/query?api-version=2023-11-01" \
  --body '{"type":"ActualCost","timeframe":"MonthToDate","dataset":{"aggregation":{"totalCost":{"name":"Cost","function":"Sum"}},"grouping":[{"type":"Dimension","name":"ServiceName"}]}}'

Summarize:

• Total spend this month vs. last month (% change)
• Top 5 services by spend
• Top 5 resource groups by spend
• Any spending anomalies (day-over-day spikes > 20%)

Step 2: Orphaned resources (waste)

Find resources consuming cost with no active use.

| Check | Command | Savings signal | |-------|---------|----------------| | Unattached managed disks | az disk list --query "[?managedBy==null]" | Disk cost per month | | Unused public IPs | az network public-ip list --query "[?ipConfiguration==null]" | ~$3.65/month each | | Stopped but allocated VMs | az vm list -d --query "[?powerState=='VM deallocated']" | Disk + IP cost still billed | | Unused App Service plans | az appservice plan list --query "[?numberOfSites==0]" --resource-group | Full plan cost | | Old snapshots (>90 days) | `az snapshot list --query "[?timeCreated 30 days — candidates for Reserved Instances (up to 72% savings)

1. List databases running 24/7 — candidates for reserved capacity
2. Check if Azure Savings Plans could apply to compute spend

Step 5: Storage optimization

1. Check storage accounts for access tier usage:

``bash az storage account list --query "[].{name:name, accessTier:accessTier, kind:kind}" -o table ``

1. Identify blobs that haven't been accessed in 90+ days — candidates for Cool/Archive tier
2. Check for lifecycle management policies — suggest if missing
3. Check for redundancy over-provisioning (GRS when LRS would suffice for non-critical data)

Step 6: Cost allocation (chargeback/showback)

Group costs by the user's chosen allocation model.

By tag (preferred):

• Group all resources by the chosen tag value
• Aggregate costs per tag value
• Track untagged resources separately as "Unallocated"

By resource group (fallback):

• Parse resource group names for team/project identifiers
• Group and aggregate accordingly

Shared costs (identify and handle):

• Resources used by multiple teams (e.g., shared AKS cluster, shared networking)
• Flag these separately — suggest allocation keys (even split, usage-based, or headcount-based)

For each team/project:

• This period vs. previous period: $ change and % change
• Top cost driver (which service drove the change?)
• Flag anomalies (> 30% increase without known cause)

Step 7: Efficiency metrics

Calculate per-team efficiency indicators:

• Cost per resource: Total spend / number of resources
• Compute waste ratio: Cost of idle/underutilized resources / total compute cost
• Tag compliance: % of team's resources properly tagged

Expected output

Report header (mandatory — use this exact format)

FinOps Cost Optimization & Chargeback Report

| Field | Value | |-------|-------| | Subscription | (name + ID) | | Report Date | YYYY-MM-DD | | Total Spend (MTD) | $X,XXX | | Projected Full Month | $X,XXX | | Month-over-month Change | +/-$X (+/-X%) | | Waste Identified (recoverable) | ~$X/month | | Resource Groups Tracked | N (M with spend) | | Tag Compliance | X% | | Issues Found | X Critical, Y High, Z Medium |

Savings breakdown table

| Category | Finding | Current Cost/mo | Savings/mo | Priority | Action | |----------|---------|----------------|------------|----------|--------| | Orphaned | 3 unattached disks | $45 | $45 | High | Delete or snapshot+delete | | Rightsizing | 2 VMs at --tags team= cost-center=

### Remediation guidance
For each cost finding, include in the output:
1. The specific `az` CLI command to remediate (suggest only — do not execute)
2. Use `GetAzCliHelp` to validate the command syntax before suggesting
3. The official Microsoft Learn documentation link

### References
- Cost Management: https://learn.microsoft.com/en-us/azure/cost-management-billing/costs/overview-cost-management
- Azure Advisor Cost: https://learn.microsoft.com/en-us/azure/advisor/advisor-cost-recommendations
- Reserved Instances: https://learn.microsoft.com/en-us/azure/cost-management-billing/reservations/save-compute-costs-reservations
- Orphaned Resources: https://learn.microsoft.com/en-us/azure/advisor/advisor-reference-cost-recommendations

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ricmmartins](https://github.com/ricmmartins)
- **Source:** [ricmmartins/azure-sre-agent-skills](https://github.com/ricmmartins/azure-sre-agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.