instrument-llm-analytics
이 스킬을 사용하여 새 코드나 변경된 코드에서 AI 모델 사용을 추적하는 PostHog LLM 분석을 추가하세요. LLM 기능을 구현하거나 PR을 검토한 후에 사용하여 모든 생성이 토큰 수, 지연 시간, 비용과 함께 캡처되도록 하세요. PostHog가 아직 설치되지 않은 경우, 이 스킬은 초기 SDK 설정도 다룹니다. 모든 제공자나 프레임워크를 지원합니다.
npx skills add https://github.com/posthog/ai-plugin --skill instrument-llm-analyticsAdd PostHog AI Observability
Use this skill to add PostHog AI Observability that traces AI model usage in new or changed code. Use it after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. If PostHog is not yet installed, this skill also covers initial SDK setup. Supports any provider or framework.
Supported providers: OpenAI, Azure OpenAI, Anthropic, Google, Cohere, Mistral, Perplexity, DeepSeek, Groq, Together AI, Fireworks AI, xAI, Cerebras, Hugging Face, Ollama, OpenRouter.
Supported frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, DSPy, LangGraph, Pydantic AI, Vercel AI, LiteLLM, Instructor, Semantic Kernel, Mirascope, Mastra, SmolAgents, OpenAI Agents.
Proxy/gateway: Portkey, Helicone.
Instructions
Follow these steps IN ORDER:
STEP 1: Analyze the codebase and detect the LLM stack.
- Look for LLM provider SDKs (openai, anthropic, google-generativeai, etc.) and AI frameworks (langchain, llamaindex, crewai, etc.) in dependency files and imports.
- Look for lockfiles to determine the package manager.
- Check for existing PostHog or observability setup. If PostHog is already installed and LLM tracing is configured, skip to STEP 4 to add tracing for any new LLM calls.
STEP 2: Research instrumentation. (Skip if PostHog LLM tracing is already set up.) 2.1. Find the reference file below that matches the detected provider or framework — it is the source of truth for callback setup, middleware configuration, and event capture. Read it now. 2.2. If no reference matches, use manual-capture.md as a fallback — it covers the generic event capture approach that works with any provider.
STEP 3: Install the PostHog SDK. (Skip if PostHog is already set up.)
- Add the PostHog SDK and any required callback/integration packages.
- Do not manually edit dependency files — use the package manager's install command.
STEP 4: Add LLM tracing.
- Instrument LLM calls to capture input tokens, output tokens, model name, latency, and costs for every generation.
- Follow the provider-specific reference for the exact callback/middleware setup.
- Do not alter the fundamental architecture of existing files. Make additions minimal and targeted.
- You must read a file immediately before attempting to write it.
STEP 5: Link to users.
- Associate LLM generations with identified users via distinct IDs when possible.
STEP 6: Set up environment variables.
- Check if the project already has PostHog environment variables configured (e.g. in
.env,.env.local, or framework-specific env files). If valid values already exist, skip this step. - If the PostHog project token is missing, use the PostHog MCP server's
projects-gettool to retrieve the project'sapi_token. If multiple projects are returned, ask the user which project to use. If the MCP server is not connected or not authenticated, ask the user for their PostHog project token instead. - For the PostHog host URL: check the
projects-getMCP response for aregionfield —USmaps tohttps://us.i.posthog.com,EUmaps tohttps://eu.i.posthog.com. If the region is not available from the MCP response or from existing project configuration, ask the user: "Are you on PostHog US Cloud or EU Cloud?" Do not assume US Cloud. - Write these values to the appropriate env file using the framework's naming convention.
- Reference these environment variables in code instead of hardcoding them.
Reference files
references/openai.md- Openai observability installationreferences/azure-openai.md- Azure openai observability installationreferences/README.md- PostHog.ai for .netreferences/anthropic.md- Anthropic ai observability installationreferences/google.md- Google ai observability installationreferences/cohere.md- Cohere ai observability installationreferences/mistral.md- Mistral ai observability installationreferences/perplexity.md- Perplexity ai observability installationreferences/deepseek.md- Deepseek ai observability installationreferences/groq.md- Groq ai observability installationreferences/together-ai.md- Together ai observability installationreferences/fireworks-ai.md- Fireworks ai observability installationreferences/xai.md- Xai observability installationreferences/cerebras.md- Cerebras ai observability installationreferences/hugging-face.md- Hugging face ai observability installationreferences/ollama.md- Ollama ai observability installationreferences/openrouter.md- Openrouter ai observability installationreferences/langchain.md- Langchain ai observability installationreferences/llamaindex.md- Llamaindex ai observability installationreferences/crewai.md- Crewai observability installationreferences/autogen.md- Autogen ai observability installationreferences/dspy.md- Dspy ai observability installationreferences/langgraph.md- Langgraph ai observability installationreferences/pydantic-ai.md- Pydantic ai observability installationreferences/vercel-ai.md- Vercel ai SDK observability installationreferences/litellm.md- Litellm ai observability installationreferences/instructor.md- Instructor ai observability installationreferences/semantic-kernel.md- Semantic kernel ai observability installationreferences/mirascope.md- Mirascope ai observability installationreferences/mastra.md- Mastra ai observability installationreferences/smolagents.md- Smolagents ai observability installationreferences/openai-agents.md- Openai agents SDK observability installationreferences/portkey.md- Portkey ai observability installationreferences/helicone.md- Helicone ai observability installationreferences/manual-capture.md- Manual capture ai observability installationreferences/basics.md- Ai observability basicsreferences/traces.md- Tracesreferences/calculating-costs.md- Calculating llm costsreferences/COMMANDMENTS.md- Framework-specific rules the integration must follow
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.
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.