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llm-analytics-setupLLM 分析设置

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

1,188

周安装

50

GitHub Stars

31

下载量

416
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:llm-analytics-setup(LLM 分析设置)
来源仓库:https://github.com/posthog/skills
仓库路径:skills/llm-analytics-setup
安装命令:
npx skills add https://github.com/posthog/skills --skill llm-analytics-setup
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/posthog/skills --skill llm-analytics-setup

简介

llm-analytics-setup 用于辅助数据整理、表格处理和指标计算,适合清洗字段、汇总数据和生成统计口径。

  • 它能检查异常值、准备图表素材,并将分析结果转为可读说明。
  • 通过 npx skills add 命令从指定仓库安装,具体用法请参考原始 README。
  • 使用时需确认数据来源和时间范围,避免把样本数据当全量事实;涉及敏感数据时应先确认脱敏方式。
  • 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

PostHog LLM analytics

This skill helps you add PostHog LLM analytics to any application using AI/LLM providers.

Reference files

  • references/openai.md - Openai llm analytics installation - docs
  • references/azure-openai.md - Azure openai llm analytics installation - docs
  • references/anthropic.md - Anthropic llm analytics installation - docs
  • references/google.md - Google llm analytics installation - docs
  • references/cohere.md - Cohere llm analytics installation - docs
  • references/mistral.md - Mistral llm analytics installation - docs
  • references/perplexity.md - Perplexity llm analytics installation - docs
  • references/deepseek.md - Deepseek llm analytics installation - docs
  • references/groq.md - Groq llm analytics installation - docs
  • references/together-ai.md - Together ai llm analytics installation - docs
  • references/fireworks-ai.md - Fireworks ai llm analytics installation - docs
  • references/xai.md - Xai llm analytics installation - docs
  • references/cerebras.md - Cerebras llm analytics installation - docs
  • references/hugging-face.md - Hugging face llm analytics installation - docs
  • references/ollama.md - Ollama llm analytics installation - docs
  • references/openrouter.md - Openrouter llm analytics installation - docs
  • references/langchain.md - Langchain llm analytics installation - docs
  • references/llamaindex.md - Llamaindex llm analytics installation - docs
  • references/crewai.md - Crewai llm analytics installation - docs
  • references/autogen.md - Autogen llm analytics installation - docs
  • references/dspy.md - Dspy llm analytics installation - docs
  • references/langgraph.md - Langgraph llm analytics installation - docs
  • references/pydantic-ai.md - Pydantic ai llm analytics installation - docs
  • references/vercel-ai.md - Vercel ai SDK llm analytics installation - docs
  • references/litellm.md - Litellm llm analytics installation - docs
  • references/instructor.md - Instructor llm analytics installation - docs
  • references/semantic-kernel.md - Semantic kernel llm analytics installation - docs
  • references/mirascope.md - Mirascope llm analytics installation - docs
  • references/mastra.md - Mastra llm analytics installation - docs
  • references/smolagents.md - Smolagents llm analytics installation - docs
  • references/openai-agents.md - Openai agents SDK llm analytics installation - docs
  • references/portkey.md - Portkey llm analytics installation - docs
  • references/helicone.md - Helicone llm analytics installation - docs
  • references/manual-capture.md - Manual capture llm analytics installation - docs
  • references/basics.md - Llm analytics basics - docs
  • references/traces.md - Traces - docs
  • references/calculating-costs.md - Calculating llm costs - docs

Each provider reference contains installation instructions, SDK setup, and code examples specific to that provider or framework. Find the reference that matches the user's stack and follow its instructions.

If the user's provider isn't listed, use manual-capture.md as a fallback — it covers the generic event capture approach that works with any provider.

Key principles

  • Environment variables: Always use environment variables for PostHog and LLM provider keys. Never hardcode them.
  • Minimal changes: Add LLM analytics alongside existing LLM calls. Don't replace or restructure existing code.
  • Trace all generations: Capture input tokens, output tokens, model name, latency, and costs for every LLM call.
  • Link to users: Associate LLM generations with identified users via distinct IDs when possible.
  • One provider at a time: Only instrument the provider(s) the user is actually using. Don't add instrumentation for providers not present in the codebase.

Framework guidelines

  • Remember that source code is available in the venv/site-packages directory
  • posthog is the Python SDK package name
  • Install dependencies with pip install posthog or pip install -r requirements.txt and do NOT use unquoted version specifiers like >= directly in shell commands
  • In CLIs and scripts: MUST call posthog.shutdown() before exit or all events are lost
  • Always use the Posthog() class constructor (instance-based API) instead of module-level posthog.api_key config
  • Always include enable_exception_autocapture=True in the Posthog() constructor to automatically track exceptions
  • NEVER send PII in capture() event properties — no emails, full names, phone numbers, physical addresses, IP addresses, or user-generated content
  • PII belongs in identify() person properties, NOT in capture() event properties. Safe event properties are metadata like message_length, form_type, boolean flags.
  • Register posthog_client.shutdown with atexit.register() to ensure all events are flushed on exit
  • The Python SDK has NO identify() method — use posthog_client.set(distinct_id=user_id, properties={...}) to set person properties, or use identify_context(user_id) within a context

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

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

38.76%
按下载量换算161

Claude

27.67%
按下载量换算115

Cursor

18.57%
按下载量换算77

Gemini CLI

9.94%
按下载量换算41

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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