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Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling".
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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/jeremylongshore/tons-of-skills-marketplace --skill configuring-auto-scaling-policies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。
Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before / interrupt_after and Command(resume=...) — JSON-serializable state, clean resume semantics, and UI wiring for approval decisions. Use when adding an approval gate before an expensive tool call, wiring a Slack/web UI for agent approvals, or debugging a graph that crashes on interrupt. Trigger with "langgraph human in loop", "langgraph interrupt_before", "langgraph approval flow", "Command resume", "langgraph HITL".
正在显示 SKILL.md
| name | configuring-auto-scaling-policies |
| description | Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling". |
| allowed-tools | Read, Write, Edit, Grep, Glob, Bash(cmd:*) |
| version | 1.25.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
| tags | ["devops","scaling","auto-scaling"] |
| compatibility | Designed for Claude Code |
Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.
aws, gcloud, or az)kubectl configured with cluster access and metrics-server deployed| Error | Cause | Solution |
|---|---|---|
No scaling activity despite high load | Metric not reaching threshold or cooldown active | Verify metric source in CloudWatch/Stackdriver; check cooldown timer with describe-scaling-activities |
Scaling too aggressively (flapping) |
| Cooldown too short or threshold too sensitive |
| Increase cooldown period and widen the gap between scale-out and scale-in thresholds |
Max capacity reached | Instance/pod limit hit during traffic spike | Raise max_size or implement request queuing as a backpressure mechanism |
HPA unable to compute replica count | Metrics server not deployed or metric unavailable | Install metrics-server and verify kubectl top pods returns data |
FailedScaleUp: insufficient capacity | Cloud provider out of capacity in selected AZ/region | Add multiple AZs to the ASG or use mixed instance types with allocation strategy |