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 zava-reportinggit 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/zava-reporting)<a href="https://agentmods.dev/skills/microsoft/sre-agent/zava-reporting"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/zava-reporting/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/zava-reporting"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/zava-reporting.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 | $0.00119 | $0.02338 |
| Opus 5.5 | $0.00048 | $0.00935 |
| Sonnet 5.5 | $0.00024 | $0.00468 |
| Haiku 4.5 | $0.00012 | $0.00234 |
Grade A, and why
zava-reporting 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zava Learning — Executive Reporting & Packaging
Assemble the final, branded incident deliverables. This is the packaging layer: it consumes the
RCA (rca-analysis), before/after evidence (evidence-before-after), recommendations
(recommendations-next-steps), and the PR / Change Request (pr-delivery,
servicenow-change-management), and renders them in the Zava house style.
Deliverable-only: produce the artifacts and content; do not auto-send. Hand the email body and Teams card to the operator to send. Scope: by default produce all three deliverables; if the calling agent asks for a subset (e.g. only the HTML report), produce only those and skip the rest.
Always load the standard first
Retrieve zava-brand (style) and zava-report-template (structure, deck order, email/Teams
formats) with SearchMemory and apply them exactly. Reuse the visual evidence already produced by
evidence-before-after — whether that is a before/after path diagram or comparison charts — rather
than re-rendering it.
Present the in-thread executive summary FIRST
The incident thread renders markdown + inline images, not raw HTML — so do not paste the HTML email into the thread. Instead, write the executive summary directly in the thread as brand-styled markdown so the operator sees the full story in place:
- Use markdown structure: a symptom-only title line, then Summary, Customer impact,
Timeline (compact table), Root cause (the rendered 5-Whys ladder from
rca-analysis), What we did, Before/After, Recommendations, and an Artifacts & links section (see below). Apply thezava-brandtone and severity labels. - Show the visuals inline. The
Plot*tools post their image straight into the thread — reuse theevidence-before-aftercharts (or callPlotBarChart/PlotPieChartfor a summary visual) so they render inline next to the narrative. For a connectivity/config fault, include the before→after path diagram as a fenced code block (ASCII) directly in the markdown. - Keep every heading symptom-only; the root cause appears in the body, never in a title.
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 · 141 lines · 119 tokens per session scan A 75eb52c65c3b
zava-reporting is a skill published in the GitHub repository microsoft/sre-agent (179 stars, last pushed 3d ago), licensed MIT. It adds 119 tokens to every session and 2,338 once invoked, about $0.0005 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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