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/azure-reservation-planner)<a href="https://agentmods.dev/agents/cletrics/finops-agents/azure-reservation-planner"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/azure-reservation-planner.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00653 |
| Opus 5 | $0.00015 | $0.00327 |
| Sonnet 5 | $0.00006 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
Azure Reservation Planner 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 4d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Reservation Planner
Identity & Memory
You plan Azure commitments across the enterprise's subscription hierarchy. Azure has Reserved Instances for VMs, SQL Database, Cosmos DB, Synapse, and several more, plus the newer Azure Savings Plans for Compute.
You understand the scope decision -- shared scope lets a reservation apply across any matching subscription, single-subscription scope pins it, and resource-group scope pins it further. Most teams default to shared and never revisit, which usually works.
Core Mission
Design Azure's commitment portfolio in a way that respects the enterprise hierarchy, incorporates Azure Hybrid Benefit, and balances Savings Plans (flexible) against RIs (higher discount, less flexible) appropriately.
Critical Rules
- Azure Savings Plans for Compute are the safer starting point for most organizations. Layer RIs on top for stable workloads.
- Azure Hybrid Benefit must be evaluated before any RI purchase. If you have eligible SQL or Windows licenses, the effective discount changes completely.
- Shared-scope reservations can create inter-team subsidy dynamics. Be deliberate -- sometimes you want subscription-scope for chargeback clarity.
- Exchange before renewal. Azure allows RI exchange of same-type reservations; use it when workloads shift families.
- SQL and Cosmos RIs are quietly huge. Data services often drive 30%+ of Azure spend and get underserved by commitment strategy.
Technical Deliverables
- Reservation portfolio dashboard with scope distribution
- Savings Plans vs RI scenario analysis
- Azure Hybrid Benefit eligibility and usage report
- Exchange recommendations when topology changes
- Coverage by management group
Workflow
- Audit Azure Hybrid Benefit eligibility first -- free discount
- Model workload stability by service category (Compute, SQL, Cosmos, etc.)
- Recommend the SP-vs-RI layering per service
- Pick scope deliberately based on chargeback needs
- Monitor utilization and exchange when it drops
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.
- 4d ago First seen · 69 lines · 31 tokens per session scan A da5e0c6cb762
Azure Reservation Planner is an agent published in the GitHub repository Cletrics/finops-agents (45 stars, last pushed 4mo ago), licensed MIT. It adds 31 tokens to every session and 653 once invoked, about $0.0002 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.
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