@ricmmartins

Creator · ricmmartins

Last updated · Aug 25, 2026

finops-intelligence

REVIEW · 58Registry indexed

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.

OpenAgentSkill Trust Score
58/100

Do not auto-install

Quality65/100
Audit75/100
Stars70
Verified installs0

Install targets

Codex install prompt

Install the "finops-intelligence" agent skill from https://github.com/ricmmartins/azure-sre-agent-skills/tree/main/skills/04-finops-intelligence. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 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. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"ricmmartins-finops-intelligence","task":"Install finops-intelligence","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.

Supply asset profile

Research and knowledge work

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Browse track

Scenario

Research agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence

Maintenance

fresh

Pushed today

Risk

Needs review

Dependency or permission surface needs review

GitHub quality

70

65/100 Quality · 66/100 Trust

Coverage tags

ResearchResearch agentsagent-skill

Review notes

Dependency or permission surface needs review · Permission surface may require sandboxing

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
65

Useful candidate, but compare it with alternatives before adopting.

Trust

Do not auto-install
58

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

Audit

Needs review
75

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Choose a stronger alternative or inspect the source manually before any install attempt.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

70 GitHub stars

Repo activity

70 stars, 14 forks

Maintenance

Pushed today

License

MIT

Install

npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence

Install safety

standard package or runtime install path

Permission surface

shell or command execution, network or browser access

Agent outcomes

No agent outcome data yet

Docs

Strong README/SKILL.md context

Risk summary

Review before production

  • The skill does not explicitly state that all commands are read-only, though they are.
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access
  • GitHub adoption: 70 GitHub stars

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is declared
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

View technical data+

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Policy
review
Human review
yes

Trust and risk

Trust
58/100
Audit
75/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • The skill does not explicitly state that all commands are read-only, though they are.
  • No OpenAgentSkill engagement data yet
  • High-risk permission hints: Shell or command execution

Agent safety v2

47/100 · Avoid automatic install

Experimentalreview

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Resolve via API

high

Shell or command execution

Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Database access

Skill may inspect schemas, query databases, or work with persistent stores.

  • High-risk permission hints: Shell or command execution
  • Dependency or permission surface needs review

Agent resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

Task: Use finops-intelligence in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20finops-intelligence%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/ricmmartins-finops-intelligence/install
Install command: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

Use finops-intelligence for this task. Review https://www.openagentskill.com/api/skills/ricmmartins-finops-intelligence/install, then install with: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence

Registry metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

64/100

Research agents

Platforms

Claude Code

Audit report

Needs review · 75/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Research agents

Prototype with this skill first; keep a fallback candidate ready.

64
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Research agents

Trust label

Prototype first

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 65/100 quality profile

review first

  • The skill does not explicitly state that all commands are read-only, though they are.
  • No OpenAgentSkill engagement data yet

Implementation path

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Trust profile

Do not auto-install

Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.

58
OpenAgentSkill Trust Score

GitHub adoption

CHECK

70 GitHub stars

Stars/forks activity

CHECK

70 stars, 14 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

Pushed today

License clarity

PASS

MIT

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • The skill does not explicitly state that all commands are read-only, though they are.
  • Quality score needs review
  • Permission surface needs review: shell or command execution, network or browser access
  • GitHub adoption: 70 GitHub stars
  • Stars/forks activity: 70 stars, 14 forks; issue activity unavailable in current metadata
  • Dependency/runtime risk: command execution surface, network or browser surface
  • Permission surface: shell or command execution, network or browser access
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Choose a stronger alternative or inspect the source manually before any install attempt.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

65
GitHub stars
70
Freshness
Today
Install ready
Yes
License
MIT
Review before install: The skill does not explicitly state that all commands are read-only, though they are.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- name: finops-intelligence description: 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. tools: - RunAzCliReadCommands - execute_kusto_query ---

# 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.

| 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]"` | ~$3.65/month each | | Stopped but allocated VMs | `az vm list -d --query "[?powerState=='VM deallocated']"` | Disk + IP cost still billed | | Unused App Service plans | `az appservice plan list --query "[?numberOfSites==0]" --resource-group <rg>` | Full plan cost | | Old snapshots (>90 days) | `az snapshot list --query "[?timeCreated<'$(date -u -d '90 days ago' +%Y-%m-%dT%H:%M:%SZ)']"` | Storage cost | | Unused NAT Gateways | `az network nat gateway list` cross-ref with subnets | ~$32/month each | | Empty resource groups | `az group list` then check member count | Organizational 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:

| Check | Reason | |-------|--------| | 2.1 Unattached managed disks | Kept for disaster recovery snapshots, reviewed monthly | | 3.2 Over-provisioned App Service plans | Pre-scaled for upcoming product launch next week | | 4.1 Reserved instances | Short-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

| 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 < 5% CPU | $380 | $190 | High | Resize D4s_v5 → B2ms | | Reservations | 5 VMs running 24/7 | $1,200 | $864 | Medium | 3yr RI | | Storage | No lifecycle policies | $200 | $80 | Medium | Add cool tier policy | | **TOTAL RECOVERABLE** | — | — | **~$X,XXX/mo** | — | — |

### Cost allocation table

| Team / Project | This Period | Last Period | Change | % Change | Top Service | % of Total | |----------------|------------|-------------|--------|----------|-------------|------------| | Platform | $5,200 | $4,800 | +$400 | +8.3% | Compute | 32% | | Product API | $3,800 | $3,100 | +$700 | +22.6% ⚠️ | Databases | 24% | | Unallocated | $1,300 | $800 | +$500 | +62.5% 🔴 | Mixed | 8% | | **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 - 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

## Sample output

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

## FinOps Cost Optimization & Chargeback Report

| Field | Value | |-------|-------| | Subscription | contoso-prod-001 (a1b2c3d4-e5f6-7890-abcd-ef1234567890) | | Report Date | 2026-07-15 | | 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 Tracked | 14 (12 with spend) | | Tag Compliance | 78% | | Issues Found | 2 Critical, 3 High, 4 Medium |

### Savings breakdown table

| Category | Finding | Current Cost/mo | Savings/mo | Priority | Action | |----------|---------|----------------|------------|----------|--------| | Orphaned | 3 unattached disks in `rg-app-prod` | $45 | $45 | High | Delete or snapshot+delete | | Orphaned | 2 unused public IPs | $7 | $7 | Medium | Delete | | Rightsizing | `vm-batch-01` at 3% CPU avg | $380 | $190 | High | Resize D4s_v5 → B2ms | | Rightsizing | `sql-staging` at 8% DTU | $250 | $125 | Medium | Downgrade S3 → S1 | | Reservations | 5 VMs running 24/7 for 90+ days | $1,200 | $864 | Medium | 3yr RI | | **TOTAL RECOVERABLE** | — | — | **~$1,179/mo** | — | — |

### Cost allocation table

| Team / Project | This Period | Last Period | Change | % Change | Top Service | % of Total | |----------------|------------|-------------|--------|----------|-------------|------------| | Platform | $5,200 | $4,800 | +$400 | +8.3% | Compute | 32% | | Product API | $3,800 | $3,100 | +$700 | +22.6% ⚠️ | Databases | 24% | | Data Team | $2,700 | $2,600 | +$100 | +3.8% | Storage | 17% | | Unallocated | $1,300 | $800 | +$500 | +62.5% 🔴 | Mixed | 8% | | **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 ```

Technical details

Version
1.0.0
License
MIT
Last updated
Aug 25, 2026
Published
Aug 25, 2026

Decision snapshot

Fallback candidate

64
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

75
Needs review
Security
74/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for finops-intelligence, ready for a manual X post.

Curator note
finops-intelligence: Comprehensive FinOps analysis combining cost optimization, waste identification, and chargeba...

70 stars

https://www.openagentskill.com/skills/ricmmartins-finops-intelligence?ref=x
Open X draft
Optional reply with install command
Listing + install path for finops-intelligence:
https://www.openagentskill.com/skills/ricmmartins-finops-intelligence?ref=x

Install: npx skills add ricmmartins/azure-sre-agent-skills --skill finops-intelligence

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Indexed by
OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Registry indexed listing is attributed to ricmmartins but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/ricmmartins-finops-intelligence?metric=listed&label=Listed)](https://www.openagentskill.com/skills/ricmmartins-finops-intelligence)
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Author

R

ricmmartins

@ricmmartins

Platform fit

Health signals

GitHub stars
70
Quality score
36/100
Last GitHub push
Aug 24, 2026
Framework hints
Unknown
OpenAgentSkill views
0
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Do not auto-install

58
  • GitHub adoption70 GitHub starsCHECK
  • Stars/forks activity70 stars, 14 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenancePushed todayPASS
  • License clarityMITPASS
  • README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
  • Dependency/runtime riskcommand execution surface, network or browser surfaceCHECK