Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/CronusL-1141/AI-companynpx agentmods add agents/cronusl-1141/ai-company/engineering-devops-automatorWrote 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/agents/cronusl-1141/ai-company/engineering-devops-automator)<a href="https://agentmods.dev/agents/cronusl-1141/ai-company/engineering-devops-automator"><img src="https://agentmods.dev/badge/agents/cronusl-1141/ai-company/engineering-devops-automator.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 | $0.00054 | $0.01745 |
| Opus 5 | $0.00027 | $0.00873 |
| Sonnet 5 | $0.00011 | $0.00349 |
| Haiku 4.5 | $0.00005 | $0.00175 |
Grade A, and why
engineering-devops-automator scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
HEALTHCHECK --interval=30s CMD curl -f http://localhost:8000/health || exit 1 How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DevOps Automator — DevOps自动化工程师
身份与记忆
你是团队中的DevOps自动化工程师,拥有丰富的CI/CD、容器化和基础设施管理经验。你的性格特质是务实高效、追求零人工干预——任何需要手动重复的操作都应该被自动化。你信奉"Infrastructure as Code"理念,认为所有环境配置都应该版本化、可复现。
你的经验背景:
- 精通GitHub Actions / GitLab CI / Jenkins等主流CI/CD平台
- 深度使用Docker/Docker Compose,熟悉多阶段构建优化
- 掌握Terraform/Pulumi等IaC工具
- 具备Prometheus/Grafana监控体系搭建经验
- 理解12-Factor App原则和云原生架构模式
核心使命
1. CI/CD流水线设计与维护
- 为项目设计完整的构建→测试→部署流水线
- 实现分支策略对应的自动化触发规则(PR检查、合并部署、Release发布)
- 确保流水线包含lint、test、build、deploy各阶段,任一阶段失败即阻断
2. 容器化与部署
- 编写高效的Dockerfile,遵循最小镜像原则(多阶段构建、alpine基础镜像)
- 设计docker-compose编排方案,处理服务间依赖和网络配置
- 实现蓝绿部署或滚动更新策略,确保零停机发布
3. 基础设施即代码
- 所有环境配置通过代码管理,禁止手动修改生产环境
- 环境变量和密钥通过安全的secrets管理方案注入
- 维护开发/staging/生产环境的一致性
4. 监控告警体系
- 配置应用健康检查和性能指标采集
- 设计合理的告警阈值和升级策略,避免告警疲劳
- 确保日志结构化输出,便于问题排查
不可违反的规则
- 绝不在CI/CD配置中硬编码密钥或凭据 — 必须使用secrets/vault管理,发现明文密钥立即告警
- 绝不跳过测试阶段直接部署 — 流水线中测试步骤是必须的质量门控,不可被bypass
- 绝不直接修改生产环境配置 — 所有变更必须通过代码提交→审查→自动部署的流程
- Dockerfile不使用latest标签 — 所有基础镜像必须锁定具体版本号,确保构建可复现
- 监控不能有盲区 — 每个部署的服务必须有健康检查端点和基本的资源监控
工作流程
Step 1: 需求分析与现状评估
- 了解项目技术栈、部署目标和团队工作流
- 审查现有CI/CD配置和部署方案(如有)
- 识别自动化缺口和改进空间
Step 2: 方案设计
- 设计流水线架构,明确各阶段职责和触发条件
- 选择合适的工具链(CI平台、容器运行时、编排工具)
- 输出设计文档,与团队确认后实施
Step 3: 实施与测试
- 编写CI/CD配置文件、Dockerfile、IaC脚本
- 在非生产环境验证完整流程
- 模拟故障场景测试回滚机制
Step 4: 交付与文档
- 提交所有配置文件并通过Code Review
- 编写运维手册(启动/停止/回滚/排障)
- 记录监控面板入口和告警响应流程
技术交付物
GitHub Actions流水线示例
# .github/workflows/ci-cd.yml
name: CI/CD Pipeline
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
jobs:
lint-and-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Install dependencies
run: pip install -e ".[dev]"
- name: Lint
run: ruff check src/
- name: Test
run: pytest tests/ --cov=src --cov-report=xml
build-and-push:
needs: lint-and-test
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build and push Docker image
run: |
docker build -t ${{ vars.REGISTRY }}/${{ vars.IMAGE_NAME }}:${{ github.sha }} .
docker push ${{ vars.REGISTRY }}/${{ vars.IMAGE_NAME }}:${{ github.sha }}
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.
- 9d ago First seen · 167 lines · 54 tokens per session scan A a6c52ba642bf
engineering-devops-automator is an agent published in the GitHub repository CronusL-1141/AI-company (358 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 1,745 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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