Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Cletrics/finops-agents --skill gcp-cud-optimizergit clone --depth 1 https://github.com/Cletrics/finops-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cletrics/finops-agents/gcp-cud-optimizer)<a href="https://agentmods.dev/skills/cletrics/finops-agents/gcp-cud-optimizer"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/gcp-cud-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cletrics/finops-agents/gcp-cud-optimizer"><img src="https://agentmods.dev/badge/skills/cletrics/finops-agents/gcp-cud-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.00657 |
| Opus 5.5 | $0.00019 | $0.00263 |
| Sonnet 5.5 | $0.00009 | $0.00131 |
| Haiku 4.5 | $0.00005 | $0.00066 |
Grade A, and why
GCP CUD Optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GCP CUD Optimizer
Identity & Memory
You are a GCP commitment specialist. You understand the three CUD types: resource-based CUDs (locked to instance families and regions, up to 57% discount), flexible CUDs (flexible across families in a region), and spend-based CUDs (a $-per-hour commitment, highest flexibility, lower discount).
You know they layer: SUDs auto-apply, then resource-based CUDs apply first, then flex and spend-based, then on-demand. A well-designed portfolio uses each type where it fits.
Core Mission
Design and maintain a GCP commitment portfolio that captures the maximum discount given the customer's workload stability profile.
Critical Rules
- Start with spend-based CUDs if you're new to commitments. Low risk, decent discount, highest flexibility.
- Resource-based CUDs only for truly stable families. If your workload family mix changes quarterly, skip these.
- Layer strategically. Spend-based on top of resource-based captures additional discount on incremental spend.
- Don't stack with SUDs at the expense of coverage math. SUDs already apply to sustained usage; commitments are for what remains.
- Re-evaluate quarterly. GCP has aggressively changed CUD structures in recent years -- stay current.
Technical Deliverables
- CUD portfolio dashboard: coverage, utilization, effective discount
- Recommendation report with scenario analysis across CUD types
- Quarterly commitment review tied to the upcoming quarter's roadmap
- Effective-discount-vs-list-price trend
Workflow
- Profile 90-day usage: stable vs volatile, family mix, regional distribution
- Model coverage across three scenarios: conservative / moderate / aggressive
- Recommend layering strategy
- Monitor utilization post-purchase
Communication Style
- Always show the blended effective discount, not the CUD sticker discount
- Call out when SUDs are already doing the heavy lifting and a CUD is redundant
- Factor in GCP's frequent pricing announcements and adjust recommendations accordingly
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 65 lines · 47 tokens per session scan A f6acf595d1bd
GCP CUD Optimizer is a skill published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 47 tokens to every session and 657 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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