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 rca-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/rca-analysis)<a href="https://agentmods.dev/skills/microsoft/sre-agent/rca-analysis"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/rca-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/rca-analysis"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/rca-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 | $0.00073 | $0.00972 |
| Opus 5.5 | $0.00029 | $0.00389 |
| Sonnet 5.5 | $0.00015 | $0.00194 |
| Haiku 4.5 | $0.00007 | $0.00097 |
Grade A, and why
rca-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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zava Learning — Root Cause Analysis
Produce the analytical heart of the incident report: a defensible, evidence-backed RCA in the
house format. Resource Group: @@RG@@. Services: learner-portal, course-api,
assessment-api. Retrieve zava-brand and zava-report-template with SearchMemory and
follow the Root cause + Timeline sections of the template.
Gather evidence first (never assert without it)
- Timeline: reconstruct detection → mitigation → durable fix from
GetActivityLogsSummary,GetChangeHistory, and the relevant App Insights / Log Analytics queries (QueryAppInsightsByResourceId,QueryLogAnalyticsByResourceId) filtered by the affectedcloud_RoleName. Every timeline row cites a source. - Align the symptom onset to the nearest config/revision/deployment change.
Structure the RCA
-
What happened — the symptom and customer impact in plain language (no cause in the title).
-
Timeline — UTC table: time · event · evidence/source.
-
5-Whys (MANDATORY, render the ladder) — write an explicit, numbered Why-ladder, not a one-line summary. Start from the student-visible symptom and ask "why?" repeatedly until you reach the latent root cause — typically five steps:
- Why 1: students saw → because
- Why 2: → because <misbehaving component/config>
- Why 3: misbehaved → because <the change/state that caused it>
- Why 4: that change happened → because <how it got introduced / trigger>
- Why 5 (latent root cause): it was possible/undetected → because
Each rung cites its evidence. Then state the trigger (what set it off now) separately from the latent cause (the condition that made it possible) — the latent cause is Why 5. Render this ladder in the RCA section; never collapse it to a single trigger/latent two-liner.
-
Contributing factors — gaps in detection, guardrails, or process that widened impact.
-
Why it was hard / easy to detect — informs the detective recommendations.
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 · 69 lines · 73 tokens per session scan A 0fd6692ad035
rca-analysis is a skill published in the GitHub repository microsoft/sre-agent (179 stars, last pushed 3d ago), licensed MIT. It adds 73 tokens to every session and 972 once invoked, about $0.0003 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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