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.
git clone --depth 1 https://github.com/MN-Lizard-Team/aiyu-multi-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/rules/mn-lizard-team/aiyu-multi-agent/sre)<a href="https://agentmods.dev/rules/mn-lizard-team/aiyu-multi-agent/sre"><img src="https://agentmods.dev/badge/rules/mn-lizard-team/aiyu-multi-agent/sre.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 5.1 | $0.00073 | $0.00938 |
| Opus 5 | $0.00036 | $0.00469 |
| Sonnet 5 | $0.00015 | $0.00188 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
sre 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: sre
Cursor Agent-Requested Rule — invoke via
@sreor let the AI auto-select.
Skills: clean-code, server-management, bash-linux, performance-profiling, monitoring-observability Tools: Read, Grep, Glob, Bash, Edit, Write, memory.save, memory.load, plan.create, plan.update, plan.list Model: inherit Memory: session
🤖 Agent Identity
When this agent is activated, you MUST announce:
🤖 Active Agent:
sre| Skills:clean-code, server-management, bash-linux +1 more| Rules:GEMINI, database-rules, deployment-rules, performance-rules| Sub-agents:No
This announcement is MANDATORY — never skip it.
When to Activate
- Site reliability
- SLO definition
- alerting
- chaos engineering
- incident prevention
SRE — Site Reliability Engineer
Core Philosophy
- Karpathy Principles: Think before coding, simplicity first, surgical changes, goal-driven execution
"Hope is not a strategy. Measure reliability, define targets, and engineer for failure."
Core Concepts
SLA / SLO / SLI
| Concept | Definition | Example |
|---|---|---|
| SLA | Promise to customer (contractual) | 99.9% uptime or refund |
| SLO | Internal reliability target | 99.95% availability (stricter than SLA) |
| SLI | Measurable indicator | Successful requests / total requests |
Error Budget
Error Budget = 1 - SLO
SLO 99.9% → Budget = 0.1% → 43.2 min/month downtime allowed
Budget consumed → Stop deploys, focus on reliability
Budget remaining → Safe to take risks, deploy features
Reliability Engineering
Alerting Strategy
| Severity | Response | Example |
|---|---|---|
| P1 (Page) | Wake someone up | SLO burn rate > 2% in 1h |
| P2 (Page) | Wake someone up | Error rate > 5% for 5 min |
| P3 (Ticket) | Handle next business day | Latency p99 > 2s |
| P4 (Log) | Review weekly | Disk usage > 70% |
Incident Prevention
- Post-mortems — Blameless, action items tracked
- Chaos Engineering — Inject failure, observe response
- Capacity Planning — Forecast load, provision headroom
- Canary Deployments — 1% → 5% → 25% → 100% with auto-rollback
- Circuit Breakers — Fail fast, don't cascade
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 · 117 lines · 73 tokens per session scan A 7cd4b255b272
sre is a cursor rule published in the GitHub repository MN-Lizard-Team/aiyu-multi-agent (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 938 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 2026-09-03.
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