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 agentmods add skills/microsoft/sre-agent/recommendations-next-stepsnpx 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.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 | $0.00057 | $0.00473 |
| Opus 5 | $0.00028 | $0.00236 |
| Sonnet 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 3d 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.
- 3d 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 (149 stars, last pushed 9d ago), licensed MIT. It adds 57 tokens to every session and 473 once invoked, about $0.0003 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 2026-08-30.
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