用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/bolivian-peru/os-moda --skill deploy-ai-agent命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
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| name | deploy-ai-agent |
| description | Deploy and manage AI agent workloads with GPU checks, API key management, and health monitoring |
| activation | auto |
| tools | ["app_deploy","app_list","app_logs","app_stop","app_restart","app_remove","system_discover","system_health","system_query","shell_exec","file_write","file_read","memory_store","journal_logs","watcher_add"] |
Deploy AI agent workloads (LangChain, CrewAI, AutoGen, custom frameworks) as managed systemd services with resource monitoring, API key management, and health checks.
system_health to verify RAM, disk, and CPU are sufficient. Check for GPU with shell_exec running nvidia-smi or ls /dev/driapp_deploy with appropriate resource limits, environment variables pointing to secrets, and a health-check-friendly portapp_logs for successful startup. Use system_discover to confirm the agent's port is listening.watcher_add to auto-restart on failurememory_store to save deployment details for future referenceapp_deploy({
name: "my-agent",
command: "/var/lib/osmoda/apps/my-agent/venv/bin/uvicorn",
args: ["app:app", "--host", "0.0.0.0", "--port", "8000"],
working_dir: "/var/lib/osmoda/apps/my-agent",
env: {
ANTHROPIC_API_KEY_FILE: "/var/lib/osmoda/secrets/anthropic-key",
OPENAI_API_KEY_FILE: "/var/lib/osmoda/secrets/openai-key"
},
port: 8000,
memory_max: "1G",
cpu_quota: "200%"
})
app_deploy({
name: "crew-agent",
command: "/var/lib/osmoda/apps/crew-agent/venv/bin/python",
args: ["-m", "crew_agent.main"],
working_dir: "/var/lib/osmoda/apps/crew-agent",
env: {
ANTHROPIC_API_KEY_FILE: "/var/lib/osmoda/secrets/anthropic-key"
},
port: 8001,
memory_max: "2G"
})
app_deploy({
name: "node-agent",
command: "/usr/bin/node",
args: ["index.js"],
working_dir: "/home/user/agent",
env: {
NODE_ENV: "production",
PORT: "3000",
API_KEY_FILE: "/var/lib/osmoda/secrets/agent-api-key"
},
port: 3000,
memory_max: "512M"
})
Never put API keys in environment variables directly. Write them to the secrets directory:
file_write to /var/lib/osmoda/secrets/<key-name> (0600 permissions)*_FILE convention) or read it in the app's entrypointBefore deploying, verify:
| Resource | Check | Minimum |
|---|---|---|
| RAM | system_health → memory_available | 1 GB free for small agents, 4 GB+ for GPU workloads |
| Disk | system_health → disks[0].available | 2 GB for deps + model cache |
| CPU | system_health → cpu_usage | 2+ cores recommended |
| GPU | shell_exec: nvidia-smi or ls /dev/dri | Optional — needed for local model inference |
| Python | shell_exec: python3 --version | 3.10+ for most frameworks |
| Node.js | shell_exec: node --version | 18+ for modern agent frameworks |
After deployment, set up a watcher:
watcher_add({
name: "my-agent-health",
check: {
type: "http_get",
url: "http://127.0.0.1:8000/health",
expected_status: 200
},
interval_secs: 30,
actions: ["restart", "notify"]
})
For agents without HTTP endpoints, use a process check:
watcher_add({
name: "my-agent-alive",
check: {
type: "systemd_unit",
unit: "osmoda-app-my-agent.service"
},
interval_secs: 60,
actions: ["restart", "notify"]
})
app_logs({ name: "my-agent", lines: 50 }) for Python import errors or missing dependenciesmemory_max or check if the model is too large for available RAMfile_read({ path: "/var/lib/osmoda/secrets/anthropic-key" })system_discover to find what's on that port, then pick another基于 SOC 职业分类