gcp-cud-advisor
Recommend optimal GCP Committed Use Discount portfolio (spend-based vs resource-based) with risk analysis
Best use case
gcp-cud-advisor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Recommend optimal GCP Committed Use Discount portfolio (spend-based vs resource-based) with risk analysis
Teams using gcp-cud-advisor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cud-advisor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gcp-cud-advisor Compares
| Feature / Agent | gcp-cud-advisor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Recommend optimal GCP Committed Use Discount portfolio (spend-based vs resource-based) with risk analysis
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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: recommend spend-based vs resource-based CUD
4. Calculate coverage gap % by region and machine family
5. Generate conservative vs aggressive commitment scenarios
## Output Format
- **CUD Recommendation Table**: workload, CUD type, term, region, estimated savings
- **Coverage Gap**: % of eligible spend currently on on-demand
- **SUD Interaction**: workloads already benefiting from automatic SUDs (don't over-commit)
- **Risk Scenarios**: Conservative (30% coverage) vs Balanced (60%) vs Aggressive (80%)
- **Break-even Timeline**: months to break even per commitment
- **`gcloud` Commands**: to create recommended CUDs
## Rules
- 2025: CUDs now cover Cloud Run and GKE Autopilot — always include these
- Never recommend resource-based CUDs for variable workloads — spend-based is safer
- Note: CUDs and SUDs can stack — calculate combined discount
- Never ask for credentials, access keys, or secret keys — only exported data or CLI/console output
- If user pastes raw data, confirm no credentials are included before processingRelated Skills
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