Automating devops
DevOps knowledge reference covering Git workflows, testing strategies, DevSecOps, release pipeline orchestration (release.yml, multi-arch images, cosign integration), CI/CD pipelines, database management, observability, and performance optimization. Use when working with Git, CI/CD, release pipelines, ghcr image publishing, testing, monitoring, or infrastructure automation.From its SKILL.md
npx -y skills add telagod/code-abyss --skill automating-devopsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
2.2 KB, 663 tokens by cl100k_base, as published. Nobody here has run it
炼器秘典 · DevOps
判断先于执行：决定「是否做 / 选什么 / 如何取舍」（栈、方案、架构、权衡）前，先读领域判断内核
skills/_kernel/backend/SKILL.md——它管 judgment，本秘典管 execution；冲突时以内核判断为准。
路由
| 意图 | 秘典 | 核心 |
|---|---|---|
| 版本控制 | git-workflow | Git 分支策略、PR 流程、rebase vs merge |
| 测试 | testing | 单元/集成/E2E、TDD、覆盖率 |
| 安全开发 | devsecops | CI/CD 安全、SAST/DAST、供应链 |
| Release 编排 | release-pipelines | release.yml 多 job 骨架、metadata tag、cosign 集成、踩坑字典 |
| 数据库 | database | SQL/NoSQL 选型、索引优化、迁移 |
| 性能 | performance | Profiling、火焰图、基准/负载测试 |
| 可观测 | observability | 日志/指标/追踪三支柱、SLO/SLI |
| 成本 | cost-optimization | FinOps、右尺寸、Spot、自动伸缩 |
CI/CD 管道模式
| 阶段 | 动作 | 工具示例 |
|---|---|---|
| Commit | lint + unit test + SAST | ESLint、pytest、Semgrep |
| Build | 构建 + 镜像打包 | Docker、Buildpacks |
| Test | 集成测试 + E2E | Playwright、k6 |
| Security | DAST + 依赖扫描 + 密钥检测 | OWASP ZAP、Trivy、gitleaks |
| Deploy | 渐进发布(canary/blue-green) | ArgoCD Rollouts、Flagger |
| Verify | 冒烟测试 + SLO 校验 | Prometheus、Grafana |
| Rollback | 自动回滚(SLO 违约) | ArgoCD、Helm rollback |
原则
自动化一切 | 快速反馈(<10min) | 主干开发短分支 | 不可变制品 | 环境即代码
What ships with it: 8 files
56.7 KB alongside SKILL.md
- cost-optimization.md6.3 KB
- database.md4.2 KB
- devsecops.md4.0 KB
- git-workflow.md3.7 KB
- observability.md7.1 KB
- performance.md7.7 KB
- release-pipelines.md17.4 KB
- testing.md6.3 KB
Gives 0 of the 12 instructions most performance cost skills give in 663 tokens
Counted across 797 of the 1,117 authors here whose files we hold, read 2026-09-06
- Check for product marketing context firstin 46 of 797, across 20 files
- Measure before optimizingin 31 of 797, across 25 files
- Profile first to identify the actual bottleneckin 23 of 797, across 22 files
- Verify your robots.txt allows AI crawlersin 21 of 797, across 12 files
- Import directly and avoid barrel filesin 19 of 797, across 15 files
- Spawn all runs in the same turnin 18 of 797, across 11 files
- Write a draft of the skillin 17 of 797, across 10 files
- Understand the user's intentin 17 of 797, across 10 files
- Use React.cache for per-request deduplicationin 16 of 797, across 11 files
- Profile before optimizingin 16 of 797, across 14 files
- Include specific numbers with sourcesin 15 of 797, across 8 files
- Add lazy loading to below-fold imagesin 15 of 797, across 10 files
Said here and by no other author read
- automate everything
- maintain fast feedback loops
- use trunk based development
- use immutable artifacts
- treat environments as code
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.