cloud-finops

仓库创建 2026年2月26日最近提交 13 天前SkillHot 收录 20 天前

▸ 精选理由

结合实战交付经验，能把成本诊断与价值挂钩，给出可执行的省钱策略。

▸ 风险提示

可能需要访问云计费/监控数据与凭据，存在凭证与敏感账单数据暴露风险。

这个 Skill 做什么

提供覆盖云、AI 与 SaaS 的专业 FinOps 成本优化与容量规划建议。

帮你把云、AI 和 SaaS 的花费降下来并做容量规划，覆盖 AWS、Azure、GCP、Vertex AI、Bedrock、Databricks 等平台的具体优化建议。用在要优化账单、做 GPU/推理成本权衡、选择自建还是托管、做预留/权重调整或治理打标签时。特点是落地、面向企业实操的 FinOps 建议，不是抽象理论，会给出可执行的权衡和成本方案。

▸ 展开 SKILL.md 英文原文

Expert FinOps guidance covering cloud, AI, and SaaS technology spend. Includes AI cost management, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, AWS (EC2, Bedrock, SageMaker, GPU rightsizing, Savings Plans, CUR, commitment strategy), Azure (reservations, Savings Plans, AHB, OpenAI PTUs, portfolio liquidity), GCP (Vertex AI, Compute Engine, BigQuery), tagging governance, SaaS management (SAM, licence optimisation, SMPs, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Windsurf, Codex), ITAM, data platforms (Databricks allocation and governance with DBCU commitments, Microsoft Fabric capacity FinOps with F-SKUs, CU smoothing, reservations, pause/resume, Pro-to-Fabric migration), Snowflake, OCI, and GreenOps (AWS Sustainability Console, CSRD). Use for any query about technology cost, commitment portfolio management, rightsizing, cost allocation, SaaS sprawl, AI dev tool spend, or connecting spend to business value. Built by OptimNow.

垂直行业FinOps云成本AI 成本管理通用

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安装 / 使用

给你的 Agent 一句话（通用）

帮我安装这个 skill：https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/skills/cloud-finops/SKILL.md

或 curl 直取 SKILL.md

curl -fsSL "https://raw.githubusercontent.com/OptimNow/cloud-finops-skills/main/skills/cloud-finops/SKILL.md"

SKILL.MD 节选查看完整文件 ↗

# FinOps - Expert Guidance

> Built by OptimNow. Grounded in hands-on enterprise delivery, not abstract frameworks.

---

## How to use this skill

This skill covers cloud, AI, SaaS, and adjacent technology spend domains. Use
`references/optimnow-methodology.md` as a reasoning lens (diagnose before prescribing,
connect cost to value, recommend progressively); then load the domain reference(s)
matching the query.

### Domain routing

| Query topic | Load reference |
|---|---|
| AI costs, LLM inference, token economics, agentic cost patterns, AI ROI, AI cost allocation, GPU cost attribution, GPU telemetry, DCGM metrics, "GPU utilization is misleading", tensor core activity, GPU memory bandwidt

via SKILL·HOT · 数据来自 GitHub 公开信息 · 原文版权归作者所有