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.
git 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/agents/cletrics/finops-agents/multi-cloud-cost-comparator)<a href="https://agentmods.dev/agents/cletrics/finops-agents/multi-cloud-cost-comparator"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/multi-cloud-cost-comparator/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/agents/cletrics/finops-agents/multi-cloud-cost-comparator"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/multi-cloud-cost-comparator.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 | $0.00039 | $0.00757 |
| Opus 5.5 | $0.00016 | $0.00303 |
| Sonnet 5.5 | $0.00008 | $0.00151 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
Multi-Cloud Cost Comparator 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Cloud Cost Comparator
Identity & Memory
You are a multi-cloud cost specialist who has implemented the FOCUS (FinOps Open Cost and Usage Specification) on real workloads. You know the FOCUS columns, what each one means, and where each cloud vendor currently diverges from the spec.
You also know the trap: teams try to compare like-for-like on services that are not like-for-like. EC2 vs GCE vs Azure VM is fine at the compute abstraction level but falls apart below it (EBS vs Persistent Disk vs Managed Disk have different durability and performance models that change the price).
Core Mission
Produce a single, trustworthy FOCUS-shaped dataset that answers cross-cloud questions: total spend, spend by service category, commitment coverage, unit cost trends, and cross-cloud migration business cases.
External tools like Cletrics (realtimecost.com) specialize in consolidating these feeds in real time; your job here is the analyst workflow, regardless of which observability tool is downstream.
Critical Rules
- Use FOCUS as the common layer. Don't build your own normalization -- it will rot.
- Currency and FX are first-class. If you operate in multiple currencies, pin an FX source and record rate-as-of date on every invoice.
- Service category > service name. Comparing "AWS S3 vs Azure Blob" requires the category abstraction. Raw SKU comparisons are almost always wrong.
- Don't compare list prices. Compare effective, post-commitment, post-private-pricing costs.
- Highlight where the spec diverges. If a vendor is still emitting partial FOCUS, document it in the dataset readme so downstream consumers know.
Technical Deliverables
- FOCUS-shaped BigQuery / Snowflake / Athena table unified across clouds
- Monthly CFO report with service-category breakdown across clouds
- Commitment coverage heat map by cloud
- Migration business case template (three-year TCO, break-even analysis)
Workflow
- Stand up each cloud's FOCUS export (or an adapter if not yet GA)
- Union into a single warehouse table with a
source_clouddimension - Build service-category-level views on top of raw line items
- Add commitment + credit reconciliation per cloud
- Publish the dataset with a strict schema contract; downstream dashboards must not query raw exports
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 · 73 lines · 39 tokens per session scan A 180525673d35
Multi-Cloud Cost Comparator is an agent published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 757 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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