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Finops Intelligence

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ricmmartins
finops-intelligence

Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeback reporting. Use when asked about reducing Azure spend, finding unused resources, cost per team, chargeback, showback, cost anomalies, rightsizing, or monthly cost review.

Overview

Publisherricmmartins
Repositoryazure-sre-agent-skills
Skill namefinops-intelligence
Stars
70
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ricmmartins on GitHub. Read the source before you install it.

Installation

Install the Finops Intelligence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ricmmartins/azure-sre-agent-skills/tree/main/skills/04-finops-intelligence
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ricmmartins/azure-sre-agent-skills.git /tmp/azure-sre-agent-skills
mkdir -p .claude/skills
cp -r /tmp/azure-sre-agent-skills/skills/04-finops-intelligence .claude/skills/finops-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finops Intelligence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Finops Intelligence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Finops Intelligence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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

If az costmanagement is unavailable, use the REST API:

bash
az rest --method post \
  --url "https://management.azure.com/subscriptions/<sub-id>/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.

CheckCommandSavings signal
Unattached managed disksaz disk list --query "[?managedBy==null]"Disk cost per month
Unused public IPsaz network public-ip list --query "[?ipConfiguration==null]"~$3.65/month each
Stopped but allocated VMsaz vm list -d --query "[?powerState=='VM deallocated']"Disk + IP cost still billed
Unused App Service plansaz appservice plan list --query "[?numberOfSites==0]" --resource-group <rg>Full plan cost
Old snapshots (>90 days)az snapshot list --query "[?timeCreated<'$(date -u -d '90 days ago' +%Y-%m-%dT%H:%M:%SZ)']"Storage cost
Unused NAT Gatewaysaz network nat gateway list cross-ref with subnets~$32/month each
Empty resource groupsaz group list then check member countOrganizational waste

Note on date handling: Use shell variable substitution for date comparisons:

  • Linux/macOS: $(date -u -d '90 days ago' +%Y-%m-%dT%H:%M:%SZ)
  • The SRE Agent sandbox runs Linux, so the above syntax is valid.

Step 3: Rightsizing opportunities

Identify over-provisioned resources.

  1. VMs with low CPU (< 5% avg over 14 days): Query Azure Monitor metrics for Percentage CPU across all VMs
  2. VMs with low memory (< 10% avg): Query Log Analytics for memory counters if available
  3. Over-provisioned App Service plans: Check CPU and memory % across the plan — if consistently < 20%, suggest downgrade
  4. Over-provisioned databases: Check DTU/vCore utilization — if < 20%, suggest lower tier

For each, calculate:

  • Current SKU and monthly cost
  • Recommended SKU and monthly cost
  • Estimated monthly savings

Step 4: Reservation & savings plan opportunities

  1. List VMs running 24/7 for > 30 days — candidates for Reserved Instances (up to 72% savings)
  2. List databases running 24/7 — candidates for reserved capacity
  3. 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
  2. Identify blobs that haven't been accessed in 90+ days — candidates for Cool/Archive tier
  3. Check for lifecycle management policies — suggest if missing
  4. 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

Accepted exceptions (optional)

If the user provides a list of accepted exceptions, do not flag those items. Instead, note them in the report as Accepted Exception with the reason provided.

Example format the user may provide:

CheckReason
2.1 Unattached managed disksKept for disaster recovery snapshots, reviewed monthly
3.2 Over-provisioned App Service plansPre-scaled for upcoming product launch next week
4.1 Reserved instancesShort-term project, reservations not cost-effective

When exceptions are provided:

  • Skip the flagged checks in scoring
  • List them in a separate "Accepted Exceptions" section at the end of the report
  • Recalculate the overall score excluding excepted checks

Expected output

Report header (mandatory — use this exact format)

FinOps Cost Optimization & Chargeback Report

FieldValue
Subscription(name + ID)
Report DateYYYY-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 TrackedN (M with spend)
Tag ComplianceX%
Issues FoundX Critical, Y High, Z Medium

Savings breakdown table

CategoryFindingCurrent Cost/moSavings/moPriorityAction
Orphaned3 unattached disks$45$45HighDelete or snapshot+delete
Rightsizing2 VMs at < 5% CPU$380$190HighResize D4s_v5 → B2ms
Reservations5 VMs running 24/7$1,200$864Medium3yr RI
StorageNo lifecycle policies$200$80MediumAdd cool tier policy
TOTAL RECOVERABLE——~$X,XXX/mo——

Cost allocation table

Team / ProjectThis PeriodLast PeriodChange% ChangeTop Service% of Total
Platform$5,200$4,800+$400+8.3%Compute32%
Product API$3,800$3,100+$700+22.6% ⚠️Databases24%
Unallocated$1,300$800+$500+62.5% 🔴Mixed8%
Total$16,000$14,300+$1,700+11.9%100%

Quick wins (implement today)

Top 3 actions that save the most with the least effort.

Requires planning

Actions that need architecture review or stakeholder approval.

Unallocated cost remediation

List untagged resources with suggested owner and tagging command:

bash
az resource tag --ids <resource-id> --tags team=<team> cost-center=<cc>

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

Sample output

The following is a redacted example of what the report looks like when run against a subscription.

FinOps Cost Optimization & Chargeback Report

FieldValue
Subscriptioncontoso-prod-001 (a1b2c3d4-e5f6-7890-abcd-ef1234567890)
Report Date2026-07-15
Total Spend (MTD)$12,340
Projected Full Month$16,450
Month-over-month Change+$1,700 (+11.5%)
Waste Identified (recoverable)~$1,179/month
Resource Groups Tracked14 (12 with spend)
Tag Compliance78%
Issues Found2 Critical, 3 High, 4 Medium

Savings breakdown table

CategoryFindingCurrent Cost/moSavings/moPriorityAction
Orphaned3 unattached disks in rg-app-prod$45$45HighDelete or snapshot+delete
Orphaned2 unused public IPs$7$7MediumDelete
Rightsizingvm-batch-01 at 3% CPU avg$380$190HighResize D4s_v5 → B2ms
Rightsizingsql-staging at 8% DTU$250$125MediumDowngrade S3 → S1
Reservations5 VMs running 24/7 for 90+ days$1,200$864Medium3yr RI
TOTAL RECOVERABLE——~$1,179/mo——

Cost allocation table

Team / ProjectThis PeriodLast PeriodChange% ChangeTop Service% of Total
Platform$5,200$4,800+$400+8.3%Compute32%
Product API$3,800$3,100+$700+22.6% ⚠️Databases24%
Data Team$2,700$2,600+$100+3.8%Storage17%
Unallocated$1,300$800+$500+62.5% 🔴Mixed8%
Total$16,450$14,750+$1,700+11.5%100%

Remediation guidance (sample)

bash
# Delete unattached managed disks
az disk delete --name disk-old-backup-01 --resource-group rg-app-prod --yes

# Resize over-provisioned VM
az vm resize --name vm-batch-01 --resource-group rg-batch-prod --size Standard_B2ms

# Tag unallocated resources
az resource tag --ids /subscriptions/.../resourceGroups/rg-shared/providers/Microsoft.Storage/storageAccounts/stcontososhared --tags team=platform cost-center=CC-1234

Frequently asked questions

What does the Finops Intelligence AI skill do?

Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeback reporting. Use when asked about reducing Azure spend, finding unused resources, cost per team, chargeback, showback, cost anomalies, rightsizing, or monthly cost review.

Why use Finops Intelligence on TypingMind?

Because you install it once and use it with any model. Finops Intelligence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Finops Intelligence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ricmmartins/azure-sre-agent-skills/tree/main/skills/04-finops-intelligence. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finops Intelligence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Finops Intelligence?

As many as you like. As long as a model supports skills, you can use Finops Intelligence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Finops Intelligence AI skill free?

Yes. It is published on GitHub by ricmmartins under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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