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 recommendations-next-stepsgit 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/recommendations-next-steps)<a href="https://agentmods.dev/skills/microsoft/sre-agent/recommendations-next-steps"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/recommendations-next-steps/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/recommendations-next-steps"><img src="https://agentmods.dev/badge/skills/microsoft/sre-agent/recommendations-next-steps.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 | $0.00473 |
| Opus 5.5 | $0.00023 | $0.00189 |
| Sonnet 5.5 | $0.00011 | $0.00095 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
recommendations-next-steps 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.
What it actually says
Zava Learning — Recommendations & Next Steps
Turn the RCA into a concrete, accountable action plan. Retrieve zava-brand and
zava-report-template with SearchMemory; follow the Recommendations section. Use
SearchIncidentKnowledge for related past incidents and microsoft-learn_microsoft_docs_search
to ground recommendations in Azure best practice.
Derive actions from the RCA
Map each contributing factor and detection gap to an action. Classify every action:
- Preventive — stops recurrence (e.g. an NSG priority guardrail in IaC, a CI check for synchronous work on the request path, an autoscale floor so an API can't reach zero replicas).
- Detective — catches it sooner (a targeted alert/metric, a synthetic probe on
/api/quiz/*, a dashboard). - Process — runbook, ownership, review, or deployment-policy change.
Present as an accountable table
Columns: action · type · owner · priority (P1/P2/P3) · target date · risk if not done · status.
- Priority by likelihood × impact of recurrence.
- Owners are roles/teams, not named individuals.
- Every recommendation is specific and testable — no "improve monitoring".
- Separate immediate next steps (this week) from longer-term hardening.
Rules
- Recommend only what the evidence supports; don't pad the list.
- Note where a recommendation is already partially done (e.g. mitigation applied, guardrail PR open) and what remains.
Verification
A prioritized, owned, dated action table where each row traces to an RCA finding, immediate vs.
long-term separated, ready for zava-reporting.
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 · 44 lines · 57 tokens per session scan A 2abde4518a19
recommendations-next-steps 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 473 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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