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sre-practicessre 实践

Agent Skill

sre-practices 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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下载量

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:sre-practices(sre 实践)
来源仓库:https://github.com/codekungfu/sre-practices
安装命令:
openclaw skills install sre-practices
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

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openclaw skills install sre-practices

简介

深度 SRE 工作流程包括 SLO/SLI 和错误预算管理。

  • 覆盖警报优化、容量规划和事件准备情况。
  • 平衡可靠性与交付速度的实践指南。sre-practices 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适用于运维团队建立稳定服务体系。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 提供可落地的 SRE 最佳实践参考。

SKILL.md

name
sre-practices
description
Deep SRE workflow—SLOs/SLIs, error budgets, alerting, toil reduction, incident readiness, capacity, and balancing reliability with delivery. Use when improving production culture, defining service reliability targets, or reducing on-call pain.

SRE Practices (Deep Workflow)

SRE is not “ops with a fancy title”—it is engineering reliability with explicit trade-offs between velocity and stability, measured with SLOs and managed through error budgets and toil budgets.

When to Offer This Workflow

Trigger conditions:

  • Defining or revisiting SLOs; too many pages or too few alerts
  • “We need five nines” without user-visible meaning
  • High toil: manual deploys, ticket-driven scaling, runbooks that never shrink
  • Post-incident push for “more reliability” without cost discussion

Initial offer:

Walk through six stages: (1) user journeys & SLIs, (2) SLO targets & windows, (3) error budgets & policy, (4) alerting & on-call, (5) toil & automation, (6) continuous improvement. Confirm service tiering and business criticality.


Stage 1: User Journeys & SLIs

Goal: Measure what users actually experience, not only server uptime.

Activities

  • List critical journeys: signup, pay, search, API sync, etc.
  • For each, pick SLI types: availability, latency, freshness, correctness (where measurable)
  • Define SLI implementation: e.g., “successful HTTP 2xx from LB / all requests excluding health checks” vs deeper synthetic probes

Good SLIs

  • Specific, measurable, aligned with pain—avoid vanity metrics

Exit condition: SLI definitions documented with data sources (metrics, logs, probes).


Stage 2: SLO Targets & Windows

Goal: Set achievable targets with explicit consequences.

Process

  • Choose window: rolling 30d common; align with release cadence
  • Set target (e.g., 99.9% availability) from error budget math: allowed downtime per month
  • Tier services: not everything needs 99.99%

Realism

  • Account for dependencies you don’t control (public cloud, third-party APIs)—SLO cannot exceed dependency SLO unless architecture isolates failures.

Exit condition: Published SLO document per service or journey with measurement method.


Stage 3: Error Budget Policy

Goal: Decide how to spend budget—feature velocity vs reliability work.

Policy Examples

  • Budget healthy → ship aggressively; budget low → freeze risky changes, focus on reliability
  • Exceptions process: who can override, with what review

Communication

  • Product/engineering shared ownership of budget—not “SRE says no” in the dark

Exit condition: Written policy: what happens when budget burns at 25/50/100%.


Stage 4: Alerting & On-Call

Goal: Pages are symptom-based, actionable, low noise.

Principles

  • Alert on user pain or imminent SLO threat, not every blip
  • Severity maps to response: SEV1 customer-wide vs warning
  • Runbooks linked; ownership clear

On-Call Health

  • Limit pages per engineer per week; track toil hours
  • Post-incident follow-through to reduce repeat pages

Exit condition: Alert inventory reviewed; tuning backlog for noisy alerts.


Stage 5: Toil & Automation

Goal: Reduce manual, repetitive, automatable work with measurable toil budgets.

Identify Toil

  • Frequent tickets, manual scaling, click-ops deploys, data fixes without guardrails

Remediate

  • Eliminate > automate > document—in that preference order when safe
  • Self-service platforms with guardrails beat hero scripts

Exit condition: Toil reduction roadmap with owners; ideally 50% toil cap aspiration per team norm (Google SRE guideline—adapt to org).


Stage 6: Continuous Improvement

Goal: Reliability work is prioritized like features.

Loops

  • Incident → action items with tracking
  • Game days / failure injection where mature
  • Quarterly SLO review—targets drift with product changes

Final Review Checklist

  • [ ] SLIs tied to user-visible outcomes
  • [ ] SLO targets realistic vs dependencies
  • [ ] Error budget policy agreed with product
  • [ ] Alerts actionable; noise tracked
  • [ ] Toil identified with automation path

Tips for Effective Guidance

  • Translate 99.9% to minutes of downtime per month—makes trade-offs concrete.
  • Never promise zero incidents; promise learning and measurable improvement.
  • Separate SLI (measurement) from SLO (target) from SLA (contract)—terms get confused.

Handling Deviations

  • Early startup: start with basic monitoring + incident reviews before full SLO program.
  • No SRE role: practices still apply—relabel “production excellence” if needed.

适合场景

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能力 5

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