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opentelemetryOpenTelemetry 命令行

Agent Skill

opentelemetry 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

873

周安装

36

GitHub Stars

12

下载量

285
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:opentelemetry(OpenTelemetry 命令行)
来源仓库:https://github.com/claude-dev-suite/claude-dev-suite
仓库路径:skills/opentelemetry
安装命令:
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill opentelemetry
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/claude-dev-suite/claude-dev-suite --skill opentelemetry

简介

opentelemetry 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态进行整理。

  • 它提供 Traces、Metrics 和 Logs 三大观测维度,支持分布式追踪和指标整理配置。
  • 使用时需明确采样策略和导出目标,避免过度整理影响性能;涉及生产环境时应配置合适的保留周期。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

OpenTelemetry - Quick Reference

Full Reference: See advanced.md for context propagation, detailed metrics API, Collector configuration, Docker Compose stack, sampling strategies, and semantic conventions.
Deep Knowledge: Use mcp__documentation__fetch_docs with technology: opentelemetry for comprehensive documentation.

Concepts

┌─────────────────────────────────────────────────────────────┐
│                      OpenTelemetry                          │
├─────────────────┬─────────────────┬─────────────────────────┤
│     Traces      │     Metrics     │         Logs            │
│  (Distributed)  │  (Aggregated)   │     (Structured)        │
├─────────────────┴─────────────────┴─────────────────────────┤
│                    OTLP Protocol                            │
├─────────────────────────────────────────────────────────────┤
│                 Collector (optional)                        │
├─────────────────────────────────────────────────────────────┤
│  Jaeger │ Zipkin │ Prometheus │ Grafana │ DataDog │ etc.    │
└─────────────────────────────────────────────────────────────┘

Node.js Setup

npm install @opentelemetry/sdk-node \
  @opentelemetry/auto-instrumentations-node \
  @opentelemetry/exporter-trace-otlp-http

Basic Configuration

// tracing.ts - Load FIRST before any other imports
import { NodeSDK } from '@opentelemetry/sdk-node';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { Resource } from '@opentelemetry/resources';
import { SEMRESATTRS_SERVICE_NAME } from '@opentelemetry/semantic-conventions';

const sdk = new NodeSDK({
  resource: new Resource({
    [SEMRESATTRS_SERVICE_NAME]: 'my-service',
  }),
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'http://localhost:4318/v1/traces',
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

process.on('SIGTERM', () => {
  sdk.shutdown().finally(() => process.exit(0));
});

Application Entry Point

// index.ts
import './tracing'; // MUST be first import
import express from 'express';

const app = express();

Manual Traces

import { trace, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('my-service', '1.0.0');

async function processOrder(orderId: string) {
  return tracer.startActiveSpan('process-order', async (span) => {
    try {
      span.setAttribute('order.id', orderId);
      await processOrderLogic(orderId);
      span.setStatus({ code: SpanStatusCode.OK });
    } catch (error) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}

Spring Boot Setup

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-tracing-bridge-otel</artifactId>
</dependency>
<dependency>
    <groupId>io.opentelemetry</groupId>
    <artifactId>opentelemetry-exporter-otlp</artifactId>
</dependency>
management:
  tracing:
    sampling:
      probability: 1.0
  otlp:
    tracing:
      endpoint: http://localhost:4318/v1/traces

Environment Variables

OTEL_SERVICE_NAME=my-service
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
OTEL_TRACES_SAMPLER=parentbased_traceidratio
OTEL_TRACES_SAMPLER_ARG=0.1  # 10% sampling

When NOT to Use This Skill

  • Simple application logging: Use logging frameworks instead
  • Monolithic applications: May be overkill
  • Vendor-specific APM: DataDog, New Relic have their own SDKs
  • Development/debugging only: Standard logging may suffice
  • Legacy systems: Migration effort may be high

Anti-Patterns

Anti-PatternWhy It's BadSolution
Creating spans for every functionMassive overheadSpan only meaningful operations
100% sampling in productionPerformance impact, costUse 1-10% sampling
Not propagating contextBreaks distributed tracesInject/extract at service boundaries
Logging PII in span attributesSecurity/compliance violationFilter sensitive data
Forgetting to end spansMemory leakAlways end spans in finally block
Synchronous exportersBlocks applicationUse batch processors

Quick Troubleshooting

IssueCauseSolution
Traces not appearingExporter not configuredCheck OTEL_EXPORTER_OTLP_ENDPOINT
High memory usageToo many spansEnable sampling
Missing context propagationNot injecting headersUse propagation API
Performance degradationHigh sampling rateReduce to 1-10%
SDK initialization errorImport order wrongInitialize SDK before other imports

Monitoring Metrics

MetricTarget
Trace latency (p99)< 10ms overhead
Span drop rate< 1%
Collector memory< 1GB
Export success rate> 99%

Checklist

  • SDK initialized before other imports
  • Resource attributes configured
  • Context propagation implemented
  • Sampling strategy defined
  • Graceful shutdown configured
  • Collector deployed
  • Dashboards created (Grafana)
  • Alerts configured

Reference

Deep Knowledge: Use mcp__documentation__fetch_docs with technology: opentelemetry for comprehensive documentation

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.45%
按下载量换算107

Claude

29.35%
按下载量换算84

Cursor

16.62%
按下载量换算47

Gemini CLI

8.68%
按下载量换算25

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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