Cloud FinOps Skill & MCP

Cloud FinOps by OptimNow

FinOps knowledge for AI assistants: billing mechanics, commitment decisions and waste runbooks for AWS, Azure, GCP, AI and data platforms. By OptimNow.

[Listed in the Claude directory] [Latest release] [License: CC BY-SA 4.0]

[Install from the Claude directory]

Ask your assistant how to reduce a cloud bill, size a commitment, read a term sheet or allocate shared AI costs, and it answers from how billing works, with decision rules and worked examples, instead of general advice. The same files work in Claude, ChatGPT, Gemini, Cursor and any MCP-compatible tool. Open source, written by OptimNow from enterprise FinOps delivery, and aligned with the FinOps Foundation framework.

What you can ask, by role

Each question below is answered by a file that exists in this repository today.

FinOps lead

• "Which of our Savings Plans and reservations expire in the next 90 days, and what should we decide before they do?" (aws-expiring-commitment-no-decision, azure-unused-reservation, finops-aws-commitments.md)
• "A third of our bill is unallocated. How do we reach a defensible allocation of shared costs?" (finops-allocation-showback.md)
• "Who should own commitment purchases, and which meetings do we need to run the practice?" (finops-practice-operations.md)

Finance and procurement

• "Our AWS enterprise agreement renews in 6 months. What should we check on the term sheet before signing?" (finops-cloud-contracts.md)
• "Finance wants a 12-month cloud forecast with an accuracy target. Which measures do we report, and how tight should the band be at our maturity?" (finops-forecasting.md)
• "What has to be true in Finance and in the ERP before we move from showback to chargeback?" (finops-chargeback.md)

Engineering and platform

• "Our NAT gateway bill is higher than the compute it serves. Where does it come from and what do we change?" (aws-nat-gateway-endpoint-substitution, aws-zombie-nat-gateway)
• "How do we split the cost of a shared Kubernetes cluster between teams?" (finops-kubernetes.md)
• "Our Log Analytics ingestion doubled. Which tables and which settings do we check first?" (azure-log-analytics-sprawl, finops-azure.md)

AI product owner

• "We spend $40K a month on Bedrock and do not know which features it goes to. Where do we start?" (finops-bedrock.md)
• "Should we buy provisioned throughput or stay pay-as-you-go for 500K requests a day?" (finops-genai-capacity.md, finops-azure-openai.md)
• "Our AI coding tool spend tripled in 6 months. How do we control it without banning the tools?" (finops-ai-dev-tools.md)

Neither the skill nor the connector can see your cloud account. For a question about your own resources, the answer is a detection query you run yourself, taken from the matching runbook.

What it covers

The FinOps Framework

The reference library is mapped to the 22 capabilities of the FinOps Foundation framework, grouped in its 4 domains. A filled square means at least one reference owns the capability; a grey square means the capability is covered inside another file; a dashed square is a known gap, listed with its reasoning in docs/ROADMAP.md.

[FinOps Framework capability coverage: 21 of 22 capabilities covered by the reference library]

This map is generated from the frontmatter of every reference file, and the CI pipeline fails when the committed map no longer matches the files (fcp-coverage.md).

Providers and business needs

The matrix reads the library by business need. Every cell comes from a named reference file, listed in the notes under the tables.

Hyperscalers

Other estates

Platforms

On-prem covers owned data centres and the hybrid platforms (Outposts, Azure Local, Arc). AI covers the AI platforms: Anthropic, AWS Bedrock, Azure OpenAI, GCP Vertex AI and the open-weight vendor APIs. Data covers Databricks, Snowflake and Microsoft Fabric. SaaS covers SaaS applications, AI coding tools and licences.

Contract negotiation and renewal

The reference finops-cloud-contracts.md covers negotiated spend commitments with the 3 hyperscalers: AWS Private Pricing (EDP and PPA), Azure MACC and the Azure Commitment Discount, and Google Cloud Minimum Commitment. It describes the 12 clauses of an enterprise agreement, where the discount shows in billing data, migration funding (MAP, Azure Accelerate, RaMP), Marketplace private offers as a commitment lever, the reseller channel, 9 intake questions to ask before advising, a 15-point review grid for a term sheet, the usual pitfalls and the timing of a renewal. A closing section covers Databricks and Snowflake commitments inside a cloud contract. Benchmarks come from public sources named in the file; the library carries no observed contract figure.

Also covered: SaaS renewal governance (renewal calendar, auto-renewal review) in finops-sam.md and the pre-renewal checkpoint and marketplace channel governance in finops-itam.md.

Not covered today: negotiation with AI providers (Anthropic, OpenAI and the hosted model vendors; the library covers their reservation mechanics, not their enterprise agreements), OCI Universal Credits beyond their mechanics, and colocation or hosting contracts.

Waste runbooks

A runbook is a step-by-step procedure for one specific waste pattern: the symptoms, a detection query you run in your own account, the fix in safest-first order, and the mistake to avoid. The library files them by provider and by waste category, and each one carries a confidence level that says whether a single signal is enough to act.

[Waste runbook coverage: 43 runbooks across 8 waste categories and 4 scopes]

What it does not do

• Live prices. The files carry billing mechanics, which stay true for years. Price figures go stale inside a packaged skill within weeks, so they are left out. For a current price the skill points to OptimToken, OptimNow's free comparison of LLM token rates and compute instance rates, each figure with its as-of date.
• Your cloud account. Nothing here reads a bill or an API. For "which of my resources" questions, the deliverable is the runbook's detection query.
• Legal, tax or accounting advice. The contract and chargeback references describe mechanics and the questions to ask; your counsel, tax adviser and auditors decide.
• Topics still open. Accounting for owned capacity (depreciation, lease treatment), AI provider and OCI contract negotiation, and the FinOps Education & Enablement capability. Each is listed in docs/ROADMAP.md with the trigger that would reopen it.
• Guaranteed routing. The model decides which file to read. When an answer arrives without a visible file read or tool call, ask for the library by name ("check the playbook library"). See docs/CONCEPTS.md.

Install

Full options, troubleshooting, and the model-agnostic API loader: see INSTALLATION.md. A version-tagged zip (cloud-finops-vX.Y.Z.zip) is attached to every GitHub release.

How it works

• The skill is a folder of markdown files: an entry point that routes a question to the right reference, the reference library and the runbooks. Installed in Claude, Kiro or through the installer for other tools, the guidance sits in the model's context before it answers. No code runs, no data leaves your machine.
• The MCP connector serves the same library the other way round, as 6 read-only tools the model calls when it decides to: list, search by FinOps capability or waste category, and fetch one reference or runbook. One URL to paste (https://mcp.optimnow.io/mcp), nothing to install. Tool calls go to a server OptimNow runs in Paris; see "Data handling" below.
• The plugin in the Claude directory is the skill only. The connector is a separate listing, so that the same content is not loaded twice in one session.
• Live prices come from OptimToken, queried at answer time, never copied into these files.

The 2 delivery shapes behave differently: a loaded skill grounds advisory and symptom questions without a decision to make, while the connector depends on the model choosing to call a tool. What that means in practice, and when to use both, is in docs/CONCEPTS.md. Using a non-Claude model through an API, add the response contract from INSTALLATION.md to your system prompt so answers stay structured and billing-grounded.

Usage examples

More questions the library is written to answer, beyond the role list above:

• "Our monthly bill jumped from $12K to $38K after a developer enabled Fast mode in Claude Code. How do I get this under control?"
• "Should we self-host an open-weight model on rented H100s instead of paying per token, and what hidden costs do TCO calculators miss?"
• "Our client wants separate AWS invoices per business unit. Their AWS contact suggested Cost Categories. Is that right?"
• "We are migrating from EA to MCA. What FinOps work do we need to do before the switch?"
• "We need to start reporting our cloud carbon emissions. Where do we begin?"
• "Our Snowflake bill doubled in a quarter. Is it the warehouses, storage or Cortex, and what do we check first?"
• "We run half our workloads in our own data centres. How do we give app teams a monthly bill for on-prem capacity they will accept?"

Design principles

• Visibility before optimisation. Establish what you spend and what drives it, then act. The skill does not recommend commitments to a team that cannot allocate its bill.
• Provider mechanics first, vendor claims second. Guidance is grounded in how billing works (CUR columns, Azure cost-management semantics, BigQuery export, FOCUS conformance), with primary sources cited. Vendor savings and sustainability claims are read critically.
• Maturity is contextual. Where cloud is a cost centre, Crawl plus selective Walk is often the right state; where cloud is the product, Run pays for itself in gross margin. The skill assesses maturity before recommending.
• Cost connects to business value. Every recommendation answers the CFO question: which business outcome does this protect or create.
• Mechanics live here, price figures do not. Any figure a reference quotes carries its date and source; current prices route to OptimToken.
• AI cost management is a first-class domain, with a reference per AI platform and a discipline file for agents, capacity and investment governance.

Data handling

What the skill and the hosted connector run, send and fetch, so you can decide where each is appropriate. Full policy in PRIVACY.md.

• The skill is static text. The entry point, the references and the runbooks are markdown files read into the model's context. They run no code, call no network endpoint and carry no credentials. The runbooks contain detection queries (billing export SQL, CLI commands) that you run in your own cloud account; nothing in this repository reads a cloud account.
• The hosted MCP connector, if you add it, sends tool calls to mcp.optimnow.io. When the model calls one of the 7 tools, the tool arguments (a reference or runbook name, a section phrase, facet filters, a guide slug) travel over HTTPS to that server and the matching content comes back. The server requires no account and no authentication, holds no database and no per-user state, and serves the same public files as this repository. Its application log writes one line per request (method, path, status and request type, never the arguments); when a lookup matches nothing, it also writes the tool name and the arguments that missed, which are short strings the model writes and can include a word copied from your question. The server runs on Fly.io, primary region Paris, which holds that log stream, possibly with the client IP address, under its default retention. OptimNow does not sell, share or profile from it.
• Price lookups route to a live tool, not to this repo. Whether the model opens OptimToken is its decision in the conversation.
• One tool makes an outbound request. get_live_figures sends one HTTPS GET to optimtoken.optimnow.io (an OptimNow site) for a guide's figures.json, carrying the guide slug and a cloud-finops-mcp User-Agent, nothing from the conversation. On the hosted connector the request leaves the server; with the PyPI package it leaves your machine. No other tool touches the network.
• Nothing else. No telemetry, no analytics beacon, no update check, no package launcher, no credential read from your environment.

Maintenance

The library is updated weekly. Every Saturday an automated scan checks around 35 sources: cloud and AI provider release notes, billing changelogs and pricing pages, plus FinOps community publications. Each change is confirmed on the provider's own documentation and reviewed by a practitioner before it is applied. Every merge to main becomes a new plugin version in the Claude directory, and a version-tagged release ships the same content to PyPI and the MCP Registry. Watch or star the repository to be notified.

Contributing

Practitioner experience is the highest-value contribution. Frameworks and vendor docs are already public; what is rare is "we tried X in production, this is what it billed". Corrections to billing mechanics, new or improved runbooks, real-world counter-examples and adversarial review of the recommendations are all welcome: the repository is opinionated, and it should also be falsifiable.

The full guide, including the repository structure, contribution types, process and conventions, is in CONTRIBUTING.md. If your change names another OptimNow tool (an MCP tool name, an endpoint URL, a provenance field), read DEPENDENCIES.md first.

Adapting this skill for your organisation

Fork the repository and customise the reference files for your context: your cloud stack, your internal policies, your tag taxonomy, your negotiated rates. A fork gives you a stable base you can pull upstream updates into at your own pace. Typical customisations:

• Adding organisation-specific tag requirements to finops-tagging.md
• Replacing illustrative figures with your negotiated rates
• Adding reference files for internal tools or platforms not covered here
• Adjusting the methodology file to your team's own approach

About OptimNow

OptimNow is a boutique FinOps consultancy that helps organisations connect cloud and AI spend to measurable business value. Based in France, with European reach.

• Website: optimnow.io
• LinkedIn: OptimNow
• GitHub: github.com/OptimNow

Open-source tools built by OptimNow:

Acknowledgements

This skill incorporates content derived from the following sources:

• FinOps Foundation: framework definitions, capability descriptions and maturity model structure are based on the FinOps Framework.
• Tokenomics Foundation: the token complexity classes in the agentic FinOps reference are adapted from Big-T Notation by Dan Neff (Adobe), published by the Tokenomics Foundation under CC BY 4.0. The AI cost and value references also draw on the Foundation's five-layer tokenomics stack paper, its proposed definition of the total cost of AI, and the drafts of its consumption and value working groups, each cited where it is used.

Every other source is credited inline, in the file that uses it. Billing mechanics cite the provider's own documentation with the date it was read; figures and opinions from practitioner talks (FinOps X 2026 and others) are attributed to the speaker and organisation where they appear.

All referenced content has been adapted with additional context from OptimNow's consulting delivery experience. Any errors or opinionated interpretations are our own. This skill is independently maintained and is not affiliated with or endorsed by the FinOps Foundation.

License

Licensed under CC BY-SA 4.0. See LICENSE.md for the full licence text.

You are free to use, adapt and redistribute this skill, including for commercial purposes, as long as you credit OptimNow and share any derivatives under the same licence. When redistributing the material, please include this attribution:

Cloud FinOps Skill by OptimNow - licensed under CC BY-SA 4.0.