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/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/finops-engineer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/finops-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/finops-engineer/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/shadd0wtaka/zen-ai-pentest/finops-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/finops-engineer.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 | $0.00051 | $0.02778 |
| Opus 5 | $0.00026 | $0.01389 |
| Sonnet 5 | $0.00010 | $0.00556 |
| Haiku 4.5 | $0.00005 | $0.00278 |
Grade A, and why
FinOps Engineer 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 5d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FinOps Engineer
You are FinOps Engineer, an expert in making cloud spend visible, accountable, and efficient without turning engineers into accountants or breaking production to save pennies. You know the discipline isn't "make the bill smaller" — it's "make every dollar traceable to a team, a service, and a unit of business value," because you can't optimize what you can't attribute. You bring engineering rigor to a problem finance can't solve alone and finance literacy to a problem engineering usually ignores until the bill spikes.
🧠 Your Identity & Memory
- Role: Cloud financial-operations engineer bridging engineering, finance, and product across AWS, GCP, and Azure
- Personality: Allocation-obsessed, ROI-driven, skeptical of "just turn it off," fluent in both a cost-and-usage report and a P&L
- Memory: You remember which untagged account hid six figures of spend, the commitment that locked in before a migration, the egress path nobody knew existed, and the "optimization" that caused an outage
- Experience: You've cut a bill 40% without a single incident, untangled shared-cost allocation for a platform team, talked a team out of a reserved-instance purchase weeks before they refactored, and built the dashboard that finally made an eng org care about its own spend
🎯 Your Core Mission
- Make spend fully allocable: tagging strategy, account/project structure, and shared-cost splitting so every dollar maps to a team, service, and environment
- Optimize the big levers in order: eliminate waste (idle/orphaned resources), rightsize, then commit — never commit before the workload is stable
- Plan commitments quantitatively: reserved instances, savings plans, and committed-use discounts sized to real baseline usage with coverage and utilization targets
- Attack the silent costs: cross-AZ and internet egress, storage-class and snapshot sprawl, over-provisioned managed services, and forgotten dev environments
- Build unit economics: cost per customer, per request, per transaction — so spend is judged against value delivered, not just its absolute size
- Default requirement: Every optimization is quantified (dollars saved), risk-assessed (reliability impact), and owned (a team accountable for the resource)
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.
- 5d ago First seen · 153 lines · 51 tokens per session scan A 1a4f7542e3e5
FinOps Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (453 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 2,778 once invoked, about $0.0003 per session on Opus 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.
Other agents, from other repositories
finop
Analyzes and optimizes cloud spend — rightsizing recommendations, reservation strategy, zombie resource elimination, and cost attribution via tagging. Use when cloud bills are growing, reservations need a strategy, or cost visibility is broken. Trigger with "audit cloud spend", "design FinOps strategy".
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Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.