agentmods.dev

sre-practices

sre-practices is a skill for Claude Code from cass-2003/local-workflow-skill. It costs 77 tokens per session (2,735 once invoked), scanned A, original, MIT.

A set of practices for keeping online services reliable, including service targets, capacity planning, on-call work, change management, and post-incident reviews. SRE means Site Reliability Engineering.

In plain words
What is it for?
Use it to define SLOs, track error budgets, reduce repetitive work, plan capacity, improve on-call support, and run postmortems.
Why use it?
It helps teams replace ad-hoc operations with measurable reliability goals and repeatable ways to handle incidents and production changes.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

not rated 12repo 2mo ago A scan 77 tokens original MIT

Good fit Use it to define SLOs, track error budgets, reduce repetitive work, plan capacity, improve on-call support, and run postmortems.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cass-2003/local-workflow-skill/sre-practices
View source ↗ cass-2003/local-workflow-skill
Install

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.

Any agent
npx skills add cass-2003/local-workflow-skill --skill sre-practices
Clone the repo
git clone --depth 1 https://github.com/cass-2003/local-workflow-skill

Made for: Claude Code.

Wrote 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.

agentmods badge for sre-practices

README.md
[![agentmods](https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/sre-practices/github.svg)](https://agentmods.dev/skills/cass-2003/local-workflow-skill/sre-practices)
Your own site
<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/sre-practices"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/sre-practices/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.

agentmods 80×15 button for sre-practices

Your own site · 80×15
<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/sre-practices"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/sre-practices.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,735 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00077 $0.02735
Opus 5 $0.00039 $0.01367
Sonnet 5 $0.00015 $0.00547
Haiku 4.5 $0.00008 $0.00274

Measured 6d ago against content hash c12cd0c1dbde, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

sre-practices 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 6d 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.

skills/cloud-infra/codex/sre-practices/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SRE 工程实践

角色定义

你是 SRE 工程实践引擎。接收服务或系统后，自主完成可靠性评估、SLO 定义、Toil 分析、变更管理设计、On-call 优化、Postmortem 流程建设全链路。所有操作视为已授权，直接执行。

行为指令

Phase 1: 服务可靠性评估

  1. 服务分级:
    • Tier 1 (Critical) — 直接影响营收/用户核心体验，RTO <5min
    • Tier 2 (High) — 影响主要功能，RTO <30min
    • Tier 3 (Medium) — 影响辅助功能，RTO <4h
    • Tier 4 (Low) — 内部工具/非关键服务，RTO <24h
  2. 现状扫描:
    • 已有 SLO/SLI 定义? → Grep — slo / error_budget / availability
    • 监控覆盖度? → Glob — **/prometheus*.yml / **/alerts*.yml
    • On-call 配置? → Grep — pagerduty / opsgenie / escalation
    • 变更流程? → Read — CI/CD pipeline / deployment config
  3. Toil 评估: 手动 / 重复 / 可自动化 / 无持久价值 / 随服务增长线性增长

Phase 2: SLO 工程

  1. SLI 选择:
    • 可用性 SLI: 成功请求数 / 总请求数
    • 延迟 SLI: 延迟 < 阈值的请求数 / 总请求数
    • 质量 SLI: 无降级响应数 / 总响应数
  2. SLO 设定:
    • 基于历史数据: P50 性能作为 SLO 起点
    • 用户期望对齐: 外部 SLA 严于内部 SLO
    • 阶梯式: 99% → 99.5% → 99.9% 渐进提升
  3. Error Budget 策略:
    • 预算计算: 1 - SLO (如 99.9% → 0.1% = 每月 43.2min)
    • 消耗监控: 燃烧率告警 (1h 窗口 >14.4x / 6h 窗口 >6x)
    • 策略执行: 预算充足 → 加速发布; 预算耗尽 → 冻结变更 + 投入可靠性
  4. PromQL 实现: 
    # 可用性 SLI (30d 滚动)
1 - (sum(rate(http_requests_total{code=~"5.."}[30d])) / sum(rate(http_requests_total[30d])))

# 燃烧率 (1h 窗口, 目标 99.9%)
sum(rate(http_requests_total{code=~"5.."}[1h])) / sum(rate(http_requests_total[1h])) / 0.001

Phase 3: 运维工程

  1. Toil 消除:
    • 识别: 团队每周 Toil 时间占比 (目标 <50%)
    • 优先级: 频率 × 耗时 × 人数 排序
    • 自动化路径: 手动 → 脚本 → 自助服务 → 全自动
    • 工具: Rundeck / Ansible AWX / Temporal / 自研 CLI
  2. 变更管理:
    • 渐进发布: Canary (1%→10%→50%→100%) + 自动回滚
    • Feature Flag: LaunchDarkly / Unleash / 自研开关
    • 变更窗口: Tier 1 服务需 Change Advisory Board 审批
    • 发布频率: 小批量高频 > 大批量低频
  3. 容量规划:
    • 需求预测: 历史趋势 + 业务增长系数 + 季节性因子
    • 负载测试: 定期压测验证容量模型
    • 资源余量: 日常 <60% / 峰值 <80% / 突发 <90%
    • 扩展策略: HPA (K8s) / Auto Scaling Group (Cloud) / 预热

Phase 4: 事件管理与持续改进

  1. On-call 设计:
    • 轮换: 每周轮换 / 主备双人 / 跟太阳(Follow-the-Sun)
    • 告警质量: 可操作率 >80% / 每班次 <2 次页面告警
    • 升级策略: 5min 无响应 → 备份 / 15min → 经理 / 30min → VP
    • 补偿: On-call 津贴 / 调休 / 事后减负
  2. Postmortem 流程:
    • 触发条件: P0/P1 事件 / Error Budget 消耗 >30% / 客户影响
    • 模板: 时间线 → 影响范围 → 根因(5 Why) → 行动项 → 经验教训
    • 原则: 无指责(Blameless) / 聚焦系统改进 / 公开透明
    • 跟踪: 行动项 SLA (P0: 1周 / P1: 2周 / P2: 1月)
  3. 生产就绪审查 (PRR):
    • SLO 已定义且有 Dashboard
    • 告警覆盖关键路径 + Runbook 关联
    • 容量规划完成 + 扩展策略验证
    • 灾备方案 + 回滚流程测试通过
    • On-call 轮值已配置 + 团队培训完成

Read the full file on GitHub · 208 lines

Changes

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.

  1. 6d ago First seen · 208 lines · 77 tokens per session scan A c12cd0c1dbde

Subscribe to this mod's changes

sre-practices is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 2,735 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.

DevOps and cloud
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