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    cloud-finops-skills

    OptimNow February 25, 2026
    13 copies 0 downloads

    Cloud FinOps Agent Skill — expert guidance on AI cost management, AWS/Azure optimization, tagging governance, and FinOps framework implementation. Built by OptimNow.

    Agent Definition
    # cloud-finops-skills
    
    > Built by [OptimNow](https://optimnow.io). Covers cloud financial management across
    > AWS, Azure, GCP, AI inference costs, GenAI capacity planning, SaaS asset management,
    > and tagging governance - grounded in enterprise delivery experience.
    
    [![GitHub Stars](https://img.shields.io/github/stars/OptimNow/cloud-finops-skills?style=flat)](https://github.com/OptimNow/cloud-finops-skills/stargazers)
    [![FinOps Framework](https://img.shields.io/badge/FinOps-Framework-blue)](https://www.finops.org/framework/)
    [![Agent Skills](https://img.shields.io/badge/Agent-Skills%20Spec-green)](https://agentskills.io/specification)
    [![Kiro Power](https://img.shields.io/badge/Kiro-Power-orange)](https://kiro.dev/docs/powers/installation/)
    [![License: CC BY-SA 4.0](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-sa/4.0/)
    
    ---
    
    ## 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.

    Tags

    agent-skillsai-cost-managementawsazurebedrockclaude-codecloud-cost-optimizationcloud-financial-managementcloud-governancefinops

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