为 Grafana、SigNoz 等平台构建能够回答实际运维人员问题的监控仪表板。适用于将指标转化为可用的仪表板,而非华而不实的展示板。
Scanned 9/4/2026
Install to Claude Code
npx -y skills add affaan-m/everything-claude-code --skill dashboard-builder --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Dashboard Builder?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/affaan-m-dashboard-builder-ed8e0ee5)More formats (shields.io, HTML) on the badges page.
---
name: dashboard-builder
description: 为 Grafana、SigNoz 等平台构建能够回答实际运维人员问题的监控仪表板。适用于将指标转化为可用的仪表板,而非华而不实的展示板。
origin: ECC direct-port adaptation
version: "1.0.0"
---
# 仪表盘构建器
当任务需要构建一个可供操作人员使用的仪表盘时使用此方案。
目标不是"展示所有指标",而是回答以下问题:
* 系统健康吗?
* 瓶颈在哪里?
* 发生了什么变化?
* 应该采取什么行动?
## 使用场景
* "构建一个Kafka监控仪表盘"
* "为Elasticsearch创建一个Grafana仪表盘"
* "为这个服务制作一个SigNoz仪表盘"
* "将这个指标列表转化为真正的运维仪表盘"
## 约束条件
* 不要从视觉布局开始;要从操作人员的问题出发
* 不要仅仅因为指标存在就包含所有可用指标
* 不要在没有结构的情况下混合健康、吞吐量和资源面板
* 不要发布没有标题、单位和合理阈值的面板
## 工作流程
### 1. 定义操作问题
围绕以下方面组织:
* 健康/可用性
* 延迟/性能
* 吞吐量/容量
* 饱和度/资源
* 服务特定风险
### 2. 研究目标平台架构
首先检查现有仪表盘:
* JSON结构
* 查询语言
* 变量
* 阈值样式
* 分区布局
### 3. 构建最小可用面板
推荐结构:
1. 概览
2. 性能
3. 资源
4. 服务特定分区
### 4. 剔除装饰性面板
每个面板都应回答一个真实问题。如果不能,则移除。
## 示例面板集
### Elasticsearch
* 集群健康
* 分片分配
* 搜索延迟
* 索引速率
* JVM堆/GC
### Kafka
* 代理数量
* 副本不足的分区
* 消息流入/流出
* 消费者滞后
* 磁盘和网络压力
### API网关/入口
* 请求速率
* p50/p95/p99延迟
* 错误率
* 上游健康
* 活跃连接数
## 质量检查清单
* \[ ] 有效的仪表盘JSON
* \[ ] 清晰的分区分组
* \[ ] 包含标题和单位
* \[ ] 阈值/状态颜色有意义
* \[ ] 存在常用过滤器的变量
* \[ ] 默认时间范围和刷新频率合理
* \[ ] 没有对操作人员无价值的装饰性面板
## 相关技能
* `research-ops`
* `backend-patterns`
* `terminal-ops`
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.