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groq-observabilitygroq 可观测性

Agent Skill

用于辅助前端页面、组件、样式和交互逻辑的开发与维护。它适合让 Agent 生成或审查 React、Next.js、Vue、Tailwind、CSS 等相关代码,整理组件结构,或定位布局和性能问题。使用时需要结合项目现有设计系统、路由和构建方式,避免只生成孤立片段;涉及页面改动时,应配合本地预览和构建检查确认视觉效果。

总安装

642

周安装

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225
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:groq-observability(groq 可观测性)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/groq-observability
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill groq-observability
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/jeremylongshore/claude-code-plugins-plus-skills --skill groq-observability

简介

groq-observability 用于辅助前端页面、组件和样式开发,支持 React、Vue 等项目。

  • 适用于生成或审查前端代码、整理组件结构或定位布局问题。
  • 需结合项目现有设计系统和构建方式使用,避免生成孤立片段。
  • 涉及页面改动时应配合本地预览和构建检查确认视觉效果。
  • 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

Groq Observability

Overview

Monitor Groq LPU inference API for latency, token throughput, and cost. Groq's defining characteristic is extreme speed -- responses arrive in 50-200ms for small completions, with token generation rates of 500-800 tokens/second.

Prerequisites

  • Groq API integration at api.groq.com
  • Metrics backend (Prometheus or similar)
  • Understanding of Groq's rate limit structure (per-key RPM and TPM)

Instructions

Step 1: Instrument the Groq Client

import Groq from 'groq-sdk';

async function trackedCompletion(groq: Groq, model: string, messages: any[]) {
  const start = performance.now();
  const res = await groq.chat.completions.create({ model, messages });
  const duration = performance.now() - start;
  const tps = (res.usage?.completion_tokens || 0) / (duration / 1000);  # 1000: 1 second in ms

  emitHistogram('groq_latency_ms', duration, { model });
  emitGauge('groq_tokens_per_second', tps, { model });
  emitCounter('groq_tokens_total', res.usage?.total_tokens || 0, { model, direction: 'total' });
  emitCounter('groq_tokens_total', res.usage?.prompt_tokens || 0, { model, direction: 'input' });
  emitCounter('groq_tokens_total', res.usage?.completion_tokens || 0, { model, direction: 'output' });

  // Groq pricing is very low -- track for volume visibility
  const pricing: Record<string, number> = { 'llama-3.3-70b-versatile': 0.59, 'llama-3.1-8b-instant': 0.05, 'mixtral-8x7b-32768': 0.24 };  # 32768 = configured value
  const costPer1M = pricing[model] || 0.10;
  emitCounter('groq_cost_usd', (res.usage?.total_tokens || 0) / 1e6 * costPer1M, { model });

  return res;
}

Step 2: Track Rate Limit Headers

// Groq returns rate limit info in response headers
function parseRateLimitHeaders(headers: Headers) {
  emitGauge('groq_rate_limit_remaining_requests', parseInt(headers.get('x-ratelimit-remaining-requests') || '0'));
  emitGauge('groq_rate_limit_remaining_tokens', parseInt(headers.get('x-ratelimit-remaining-tokens') || '0'));
  const resetMs = parseInt(headers.get('x-ratelimit-reset-requests') || '0');
  emitGauge('groq_rate_limit_reset_ms', resetMs);
}

Step 3: Alert on Performance Degradation

groups:
  - name: groq
    rules:
      - alert: GroqLatencyHigh
        expr: histogram_quantile(0.95, rate(groq_latency_ms_bucket[5m])) > 1000  # 1000: 1 second in ms
        annotations: { summary: "Groq P95 latency exceeds 1 second (normally <200ms)" }
      - alert: GroqRateLimitNear
        expr: groq_rate_limit_remaining_requests < 10
        annotations: { summary: "Groq rate limit nearly exhausted -- requests may be throttled" }
      - alert: GroqThroughputDrop
        expr: groq_tokens_per_second < 100
        for: 5m
        annotations: { summary: "Groq tokens/sec dropped below 100 (expected 500+)" }  # HTTP 500 Internal Server Error
      - alert: GroqErrors
        expr: rate(groq_errors_total[5m]) > 0.05
        annotations: { summary: "Groq API error rate elevated" }

Step 4: Build a Speed-Focused Dashboard

Key panels: time-to-first-token distribution (Groq's USP), tokens/second by model, rate limit utilization gauge, request volume by model, cumulative cost (low but trackable), and latency comparison across models. Since Groq's main value is speed, latency degradation is the highest-priority alert.

Step 5: Log Request Metadata

{"ts":"2026-03-10T14:30:00Z","model":"llama-3.3-70b-versatile","latency_ms":142,"ttft_ms":48,"tokens_per_sec":623,"prompt_tokens":256,"completion_tokens":89,"cost_usd":0.00002,"rate_limit_remaining":45}  # 2026: 256: 623 = configured value

Error Handling

IssueCauseSolution
429 with long retry-afterRPM or TPM limit hitImplement token-aware request queuing
Latency spike to 2s+Model overloaded or large promptReduce prompt size or switch to lighter model
503 Service UnavailableGroq capacity issueImplement fallback to alternative provider
Tokens/sec dropStreaming disabledEnable streaming for better perceived performance

Examples

Basic usage: Apply groq observability to a standard project setup with default configuration options.

Advanced scenario: Customize groq observability for production environments with multiple constraints and team-specific requirements.

Output

  • Configuration files or code changes applied to the project
  • Validation report confirming correct implementation
  • Summary of changes made and their rationale

Resources

  • Official monitoring documentation
  • Community best practices and patterns
  • Related skills in this plugin pack

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