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/rca-analysisnpx skills add microsoft/sre-agent --skill rca-analysisgit clone --depth 1 https://github.com/microsoft/sre-agentWhat 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 | $0.00073 | $0.00972 |
| Opus 5 | $0.00036 | $0.00486 |
| Sonnet 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 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.
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.
- 3d 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 (149 stars, last pushed 9d ago), licensed MIT. It adds 73 tokens to every session and 972 once invoked, about $0.0004 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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