Cloud FinOps Skill & MCP

Open-source FinOps knowledge skill and MCP server for AI agents - Claude, ChatGPT, Gemini, Cursor, and any MCP-compatible client. Cloud cost optimisation across AWS, Azure, GCP and OCI, AI inference economics, Kubernetes, data platforms, allocation, chargeback, anomaly management, and named-pattern waste detection playbooks. Built by OptimNow, grounded in enterprise delivery experience.

Install in 5 seconds

Tool	One-step install
	At the Claude Code prompt: /plugin marketplace add https://github.com/OptimNow/cloud-finops-skills.git then /plugin install cloud-finops@optimnow
	Download the latest release zip, then Settings -> Skills -> Upload zip
	Self-host: ./install.sh --tool chatgpt --grouped (a public Cloud FinOps GPT is on the Roadmap)
	Self-host: ./install.sh --tool gemini (a public Cloud FinOps Gem is on the Roadmap)
	One-liner: curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash -s -- --tool <name>
	curl -sL https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/install.sh | bash
	Nothing to install: claude mcp add --transport http cloud-finops https://cloud-finops-skills-590a051d.alpic.live/. For Claude.ai / Desktop, Settings -> Connectors -> Add custom connector with exactly that URL (trailing slash included - the widget sandbox domain is derived from it)
	pip install cloud-finops-mcp then add to your MCP client config (Claude Code / Cursor / Codex / Windsurf / Cline). Snippets: ./install.sh --tool mcp. Six tools - faceted retrieval over the reference library and named-pattern playbooks.

Full options, troubleshooting, and the model-agnostic API loader: see INSTALLATION.md.

Live prices come from OptimToken, not from this repo

This skill carries billing mechanics, which stay true for years. It deliberately does not carry current price figures, which go stale inside a packaged skill within weeks.

Those live in OptimToken - LLM token rates for 250+ models and compute instance rates across seven clouds, each figure carrying its own as-of date. The website works on its own with no setup. Adding its MCP connector lets the model fetch a rate mid-answer instead of telling you to go and look it up:

	optimtoken.optimnow.io - compare model and instance prices in the browser
	Hosted, nothing to install. Point your client at https://ai-pricing-hub-mcp-9604f763.alpic.live/ - config snippets in INSTALLATION.md

Pair it with the skill and a pricing question gets answered with a dated figure and its source, rather than from a number the model remembers.

What is a Skill, and why does it matter

A Skill is a structured knowledge file that you attach to an AI agent or a large language model. It gives the model accurate, domain-specific context that it would not otherwise have access to.

Without it, general-purpose LLMs make confident but incorrect statements on FinOps topics. They miscalculate PTU break-even rates. They confuse Azure and AWS reservation mechanics. They give generic advice that ignores how billing actually works on Bedrock or Azure OpenAI. The answers sound plausible. Most of the time, they are wrong on the details that matter.

This skill corrects that by injecting verified, curated FinOps knowledge directly into the model's context - covering billing models, cost allocation patterns, optimisation frameworks, and governance practices across the major cloud providers and AI platforms.

The closest analogy is RAG (Retrieval-Augmented Generation). Like RAG, it extends a model's knowledge beyond its training data. Unlike RAG, it requires no vector database, no embedding pipeline, and no retrieval infrastructure. You copy a folder into your agent setup and the model gains structured expertise on cloud financial management.

This makes it portable: the same skill works with Claude, GPT, Gemini, or any MCP-compatible agent - with no changes to the files.

To keep responses consistent across models, add a response contract to your system prompt (see INSTALLATION.md, "API integration / Recommended response contract"). This ensures structured, billing-grounded answers even when model defaults differ.

Who this is for

• FinOps practitioners building or evaluating AI-assisted cost analysis tools
• Cloud engineers and architects who want a cost-aware assistant integrated into their workflow
• Developers building internal FinOps agents, chatbots, or automation pipelines
• Finance and IT managers evaluating the AI tooling their teams are deploying

No AI infrastructure experience is required to use this skill. If you can copy a folder and follow the installation steps, you can add FinOps expertise to any compatible agent.

Design principles

Read the full README on GitHub →