Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kudig-io/kudig-database --skill k8s-performancegit clone --depth 1 https://github.com/kudig-io/kudig-databaseWrote 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/skills/kudig-io/kudig-database/k8s-performance)<a href="https://agentmods.dev/skills/kudig-io/kudig-database/k8s-performance"><img src="https://agentmods.dev/badge/skills/kudig-io/kudig-database/k8s-performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kudig-io/kudig-database/k8s-performance"><img src="https://agentmods.dev/badge/skills/kudig-io/kudig-database/k8s-performance.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00023 | $0.04642 |
| Opus 5.5 | $0.00009 | $0.01857 |
| Sonnet 5.5 | $0.00005 | $0.00928 |
| Haiku 4.5 | $0.00002 | $0.00464 |
Grade A, and why
k8s-performance scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to k8s-dns-failure — 308 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.
生产环境安全提示
本文档包含可直接执行的运维命令。执行前请确认：当前目标集群与 Namespace 是否正确；是否具备足够的 RBAC 权限；是否已在非生产环境验证。命令风险等级标注：🔴 高风险（可能造成数据丢失或服务中断）、🟡 中风险（会修改集群状态，但通常可回滚）、🟢 低风险/只读（信息收集，无副作用）。
K8s Performance Bottleneck 诊断与修复
性能问题是 [[kubernetes|Kubernetes]] 生产环境中最具挑战性的问题类型之一。CPU 节流、内存 OOM、磁盘 IO 饱和、网络延迟都可能导致应用响应变慢甚至不可用。
本 [[SKILL|Skill]] 覆盖 Pod/节点/集群级别的性能瓶颈诊断和修复，包括资源优化、调度调整、应用级优化等。
何时使用此 Skill
| 症状 | 检测方法 | 置信度 |
|---|---|---|
| Pod CPU 使用率接近 limit | kubectl top pod |
0.95 |
| Pod 被 OOMKilled | kubectl get pod + Events |
0.95 |
| 节点负载高 | kubectl top node |
0.90 |
| 应用 P99 延迟突增 | [[prometheus|Prometheus]]/Grafana | 0.90 |
| 磁盘 IO 等待高 | node_exporter iowait | 0.85 |
| CPU Throttling | container_cpu_cfs_throttled_seconds_total |
0.95 |
排除条件: 节点 NotReady → SKILL-NODE-001; HPA 不工作 → SKILL-AUTO-001
快速分级（2 分钟内完成）
影响范围
├── 核心服务 P99 > SLA ──────────→ P0（15min 内修复）
├── 生产环境延迟异常 ────────────→ P1（30min 内修复）
├── 成本优化（资源浪费） ────────→ P2（2h 内处理）
└── 单 Pod 性能问题 ─────────────→ P2（2h 内处理）
立即升级条件:
- 核心服务延迟超过 SLA 且持续恶化
- 多个节点同时高负载
- 集群级别资源耗尽
执行流程
# 🟢 低风险：只读/信息收集，通常无副作用
工单/告警触发
│
▼
┌──────────────┐ 脚本: scripts/diagnose-quick.sh
│ Phase 1 │ 内容: kubectl top + metrics 快速检查（只读）
│ 快速检查 │ Step: D1.1-D1.6
└──────┬───────┘
│ 无法确认根因
▼
┌──────────────┐ 内容: 应用 profiling / 节点分析
│ Phase 2 │
│ 深度分析 │
└──────┬───────┘
│ 确认根因
▼
┌──────────────┐ 参考: reference/remediation-playbook.md
│ 修复操作 │ 风险: LOW → MEDIUM → HIGH
│ REM-001~006 │
└──────┬───────┘
│
▼
┌──────────────┐ 脚本: scripts/verify-performance.sh
│ 验证确认 │ 检查: 资源使用率/延迟
└──────────────┘
可用脚本
| 脚本 | 用途 | 参数 | 风险 |
|---|---|---|---|
scripts/diagnose-quick.sh |
性能瓶颈快速诊断 | NAMESPACE POD_NAME (optional) |
只读 |
scripts/verify-performance.sh |
修复后验证 | NAMESPACE POD_NAME (optional) |
只读 |
根因概览 (6 种)
| RC ID | 根因 | 概率 | 首选修复 | 风险 |
|---|---|---|---|---|
| RC-001 | CPU limit 过低导致节流 | 高 | REM-001 调整 limit | LOW |
| RC-002 | 内存 limit 过低导致 OOM | 高 | REM-002 调整 limit | LOW |
| RC-003 | 节点资源饱和 | 中 | REM-003 扩容/调度 | MEDIUM |
| RC-004 | 应用级性能问题 | 中 | REM-004 应用优化 | MEDIUM |
| RC-005 | 磁盘 IO 饱和 | 中 | REM-005 IO 优化 | MEDIUM |
| RC-006 | 网络延迟/带宽不足 | 低 | REM-006 网络优化 | MEDIUM |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · +19 lines 2a15bc84ac3d
- 1mo ago First seen · 352 lines · 23 tokens per session scan A b6474c02f357
k8s-performance is a skill published in the GitHub repository kudig-io/kudig-database (6 stars, last pushed today), licensed Apache-2.0. It adds 23 tokens to every session and 4,642 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to k8s-dns-failure, differing in 308 lines, and is treated as a copy.
Other skills, from other repositories
performance-profiler
Application performance profiling - runtime snapshots, benchmarking, memory analysis, startup timing, and bottleneck detection.
performance-optimizer
A performance improvement assistant that analyses applications, identifies bottlenecks, and suggests ways to make them faster.
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-optimizer
Analyze and optimize code performance, identify bottlenecks, and suggest improvements.
performance-analysis
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms.
performance-engineer
Enforces measure-profile-optimize order, preventing intuition-driven optimization of non-bottlenecks. Now with mathematical depth metrics, optimization ROI calculus, optimization , and verification gates.