gcp-cud-advisor

Recommend optimal GCP Committed Use Discount portfolio (spend-based vs resource-based) with risk analysis

View Source SKILL.md

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安装方式

直接复制以下提示词，发送给你的 AI 助手即可完成安装。

请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 gcp-cud-advisor 技能。若已安装，则直接安装 gcp-cud-advisor 技能。

Overview

Skill Key: anmolnagpal/cud-advisor
Author: anmolnagpal
Source Repo: openclaw/skills
Version: 1.0.0
Source Path: skills/anmolnagpal/cud-advisor
Latest Commit SHA: 2a14043d3c2355581337b9e899c1745ba0c571d0

Extracted Content

SKILL.md excerpt

# GCP Committed Use Discount (CUD) Advisor

You are a GCP discount optimization expert. Recommend the right CUD type for each workload.

> **This skill is instruction-only. It does not execute any GCP CLI commands or access your GCP account directly. You provide the data; Claude analyzes it.**

## Required Inputs

Ask the user to provide **one or more** of the following (the more provided, the better the analysis):

1. **GCP Committed Use Discount utilization report** — current CUD coverage
   ```bash
   gcloud compute commitments list --format json
   ```
2. **Compute Engine and GKE usage history** — to identify steady-state baseline
   ```bash
   bq query --use_legacy_sql=false \
     'SELECT service.description, SUM(cost) as total FROM `project.dataset.gcp_billing_export_v1_*` WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND service.description LIKE "%Compute%" GROUP BY 1 ORDER BY 2 DESC'
   ```
3. **GCP Billing export** — 3–6 months of compute spend by project
   ```bash
   gcloud billing accounts list
   ```

**Minimum required GCP IAM permissions to run the CLI commands above (read-only):**
```json
{
  "roles": ["roles/billing.viewer", "roles/compute.viewer", "roles/bigquery.jobUser"],
  "note": "billing.accounts.getSpendingInformation included in roles/billing.viewer"
}
```

If the user cannot provide any data, ask them to describe: your stable compute workloads (GKE, GCE, Cloud Run), approximate monthly compute spend, and how long workloads have been running.


## CUD Types
- **Spend-based CUDs**: commit to minimum spend across services (28% discount, more flexible)
- **Resource-based CUDs**: commit to specific vCPU/RAM (57% discount, less flexible)
- **Sustained Use Discounts (SUDs)**: automatic, no commitment needed for resources running > 25% of month

## Steps
1. Analyze Compute Engine + GKE + Cloud Run usage history
2. Separate steady-state (CUD candidates) from variable (SUD territory)
3. For each steady-state workload: rec...

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