microsoft/sre-agent is the community repository for Azure SRE Agent, an AI assistant for diagnosing and resolving production problems and reducing routine operations work. It is for teams working on service reliability and includes documentation, discussions, videos, and hands-on labs for using the product. The catalogue skills relate to the agent's official plugins and resources.
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 microsoft/sre-agent --skill cost-analysisgit clone --depth 1 https://github.com/microsoft/sre-agentWrote 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/microsoft/sre-agent/cost-analysis)<a href="https://agentmods.dev/skills/microsoft/sre-agent/cost-analysis"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/cost-analysis/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/microsoft/sre-agent/cost-analysis"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/cost-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00883 |
| Opus 5.5 | $0.00023 | $0.00353 |
| Sonnet 5.5 | $0.00011 | $0.00177 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
cost-analysis 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zava Learning — Cloud Cost Analysis
Resource Group: @@RG@@. Read-only audit — never resize, deallocate, or delete a resource.
This is a weekly spend posture review. Recommend optimisations for a human to apply; change nothing.
1. Pull actual spend (preferred path)
Use Cost Management for real billed amounts, scoped to the RG:
- This week vs. last week by resource:
az costmanagement query --type ActualCost --timeframe Custom --time-period from=<start> to=<end> --scope "/subscriptions/<sub>/resourceGroups/@@RG@@" --dataset-grouping name=ResourceId type=Dimension --dataset-aggregation totalCost.name=Cost totalCost.function=Sum(run it for the current 7-day window and the prior 7-day window, then diff per ResourceId). - Or month-to-date by service: same query grouped by
ServiceName. Ifaz costmanagementis missing or returns an auth error (the agent identity lacks Cost Management Reader), FALL BACK toaz consumption usage list --start-date <d> --end-date <d>filtered to the RG. Record which data source you used so the report is honest about provenance.
2. If billing data is unavailable, estimate from inventory
As a last resort (no Cost Management and no consumption access), build an estimate:
- Inventory the RG:
az resource list -g @@RG@@ --query "[].{name:name,type:type,sku:sku}". - Note the cost-bearing resources (PostgreSQL Flexible Server SKU, the reporting VM SKU + managed disks, Container Apps environments + per-app replicas/scale, Application Gateway tier + capacity units, Log Analytics ingestion, Key Vault, ACR SKU, public IPs).
- Use
microsoft-learn_microsoft_docs_search/ known Azure retail pricing to produce a rough, clearly-labelled estimate, not a billed figure. State the assumption explicitly.
3. Find waste and right-sizing opportunities (read-only signals)
- Idle/oversized: VMs that are deallocated yet still incurring disk cost; disks not attached to
any VM (
az disk list -g @@RG@@ --query "[?managedBy==null]"); over-provisioned VM/DB SKUs vs. observed CPU/memory; Container Apps with high min-replicas but low traffic. - Orphaned: unattached public IPs, empty App Gateway backend pools, unused NICs, old ACR images.
- Trend: any ResourceId whose week-over-week cost rose sharply — flag it with the Δ%.
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 · 60 lines · 57 tokens per session scan A 4fbbef252474
cost-analysis is a skill published in the GitHub repository microsoft/sre-agent (179 stars, last pushed 3d ago), licensed MIT. It adds 57 tokens to every session and 883 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 2026-08-30.
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