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backend-metrics后端指标

Agent Skill

backend-metrics 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

245

周安装

10

GitHub Stars

1

下载量

78
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

3

许可证

MIT

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:backend-metrics(后端指标)
来源仓库:https://github.com/workshop-ventures/skills
仓库路径:skills/backend-metrics
安装命令:
npx skills add https://github.com/workshop-ventures/skills --skill backend-metrics
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/workshop-ventures/skills --skill backend-metrics

简介

用于为 Node.js 后端添加 OpenTelemetry 指标,监控 HTTP 请求与响应时长。

  • 适合构建可观测性体系,追踪总请求数与分布直方图。
  • 通过 OTLP/gRPC 导出指标,支持多云与本地监控系统集成。
  • 实施前需确认 SDK 版本与 exporter 配置,避免资源消耗过高。
  • backend-metrics 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Backend Metrics and Observability

This skill adds OpenTelemetry metrics to track HTTP requests and response times.

Overview

OpenTelemetry provides vendor-neutral observability. This setup tracks:

  • Total HTTP requests (counter)
  • Response time distribution (histogram)

Installation

npm install @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/sdk-metrics @opentelemetry/exporter-metrics-otlp-grpc

Implementation

Step 1: Create Metrics Library

Create apps/backend/src/lib/metrics.ts:

import { metrics, Meter } from '@opentelemetry/api';
import { MeterProvider } from '@opentelemetry/sdk-metrics';
import { OTLPMetricExporter } from '@opentelemetry/exporter-metrics-otlp-grpc';
import { Resource } from '@opentelemetry/resources';
import {
  ATTR_SERVICE_NAME,
  ATTR_SERVICE_VERSION,
  ATTR_DEPLOYMENT_ENVIRONMENT_NAME,
} from '@opentelemetry/semantic-conventions';

import config from '../config';
import { createLogger } from './logger';

const log = createLogger('metrics');

let meterProvider: MeterProvider | null = null;

/**
 * Initialize OpenTelemetry metrics
 * Call this early in application startup
 */
export function initializeTelemetry(): void {
  if (!config.telemetry.enabled) {
    log.info('Telemetry disabled');
    return;
  }

  const resource = new Resource({
    [ATTR_SERVICE_NAME]: config.telemetry.serviceName,
    [ATTR_SERVICE_VERSION]: config.telemetry.serviceVersion,
    [ATTR_DEPLOYMENT_ENVIRONMENT_NAME]: config.telemetry.environment,
  });

  const metricExporter = new OTLPMetricExporter({
    url: config.telemetry.otlpEndpoint,
  });

  meterProvider = new MeterProvider({
    resource,
    readers: [
      {
        exporter: metricExporter,
        exportIntervalMillis: 60000, // Export every 60 seconds
      },
    ],
  });

  metrics.setGlobalMeterProvider(meterProvider);

  log.info(
    { endpoint: config.telemetry.otlpEndpoint },
    'Telemetry initialized'
  );
}

/**
 * Get a meter for creating instruments
 */
export function getMeter(name: string, version: string = '1.0.0'): Meter {
  return metrics.getMeter(name, version);
}

/**
 * Graceful shutdown
 */
export async function shutdownTelemetry(): Promise<void> {
  if (meterProvider) {
    await meterProvider.shutdown();
    log.info('Telemetry shutdown complete');
  }
}

Step 2: Add Config

Add to apps/backend/src/config/index.ts:

const config = {
  // ... existing config ...

  telemetry: {
    enabled: process.env.OTEL_ENABLED === 'true',
    serviceName: process.env.SERVICE_NAME || 'my-backend',
    environment: process.env.ENVIRONMENT || 'dev',
    serviceVersion: process.env.SERVICE_VERSION || '1.0.0',
    otlpEndpoint: process.env.OTEL_COLLECTOR_ENDPOINT || 'grpc://localhost:4317',
  },
};

export default config;

Step 3: Create Metrics Middleware

Create apps/backend/src/middleware/metrics.ts:

import { Context, Next } from 'koa';
import { getMeter } from '../lib/metrics';
import { createLogger } from '../lib/logger';

const log = createLogger('metrics-middleware');
const meter = getMeter('http-metrics');

// Create instruments
const requestCounter = meter.createCounter('http_requests_total', {
  description: 'Total number of HTTP requests',
});

const responseTimeHistogram = meter.createHistogram('http_response_time_ms', {
  description: 'HTTP response time in milliseconds',
  advice: {
    explicitBucketBoundaries: [10, 25, 50, 100, 250, 500, 1000, 2500, 5000],
  },
});

/**
 * Middleware to track HTTP metrics
 */
export async function metricsMiddleware(ctx: Context, next: Next): Promise<void> {
  const startTime = Date.now();

  await next();

  const duration = Date.now() - startTime;

  try {
    // Use matched route pattern if available, fallback to path
    const route = ctx._matchedRoute || ctx.path;
    const methodRoute = `${ctx.method} ${route}`;

    requestCounter.add(1, {
      method_route: methodRoute,
      status_code: ctx.status.toString(),
    });

    responseTimeHistogram.record(duration, {
      method_route: methodRoute,
      status_code: ctx.status.toString(),
    });
  } catch (err) {
    log.error({ err }, 'Error recording metric');
  }
}

Step 4: Initialize in main.ts

Update apps/backend/src/main.ts:

import { initializeTelemetry, shutdownTelemetry } from './lib/metrics';
import { metricsMiddleware } from './middleware/metrics';

// Initialize telemetry early (before app setup)
initializeTelemetry();

// ... app setup ...

// Add metrics middleware (after logging, before routes)
app.use(metricsMiddleware);

// ... routes ...

// Graceful shutdown
process.on('SIGTERM', async () => {
  await shutdownTelemetry();
  process.exit(0);
});

Metrics Reference

Request Counter

http_requests_total{method_route="GET /api/users", status_code="200"}

Labels:

  • method_route: HTTP method and route pattern
  • status_code: HTTP status code

Response Time Histogram

http_response_time_ms{method_route="GET /api/users", status_code="200"}

Bucket boundaries: 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2.5s, 5s

Custom Metrics

Add custom metrics for business logic:

import { getMeter } from '../lib/metrics';

const meter = getMeter('business-metrics');

// Counter for specific events
const orderCounter = meter.createCounter('orders_total', {
  description: 'Total orders placed',
});

// Use in your code
orderCounter.add(1, { status: 'completed', payment_method: 'card' });

// Gauge for current values
const activeUsersGauge = meter.createObservableGauge('active_users', {
  description: 'Number of currently active users',
});

activeUsersGauge.addCallback((result) => {
  result.observe(getActiveUserCount());
});

Environment Variables

OTEL_ENABLED=true
SERVICE_NAME=my-backend
ENVIRONMENT=production
SERVICE_VERSION=1.2.3
OTEL_COLLECTOR_ENDPOINT=grpc://otel-collector:4317

Checklist

  1. Install dependencies: npm install @opentelemetry/api @opentelemetry/sdk-node @opentelemetry/sdk-metrics @opentelemetry/exporter-metrics-otlp-grpc @opentelemetry/resources @opentelemetry/semantic-conventions
  2. Create lib/metrics.ts with initialization and getMeter
  3. Add config for telemetry settings
  4. Create middleware/metrics.ts for HTTP tracking
  5. Initialize in main.ts before app setup
  6. Add shutdown handler for graceful cleanup
  7. Configure environment variables for your collector

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

28.13%
按下载量换算22

OpenCode

22.31%
按下载量换算17

Antigravity

15.63%
按下载量换算12

Gemini CLI

11.16%
按下载量换算9

windsurf

7.94%
按下载量换算6

trae

3.28%
按下载量换算3

安全审计

Gen Agent Trust Hub

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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