Omnibus Instrument LLM Analytics Agent Skill

>- Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.

61k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/PostHog/skills --skill omnibus-instrument-llm-analytics

What comes with it

237 058 bytes besides the instruction

references/anthropic.md

references/autogen.md

references/azure-openai.md

references/basics.md

references/calculating-costs.md

references/cerebras.md

references/cohere.md

references/crewai.md

references/deepseek.md

references/dspy.md

references/fireworks-ai.md

references/google.md

references/groq.md

references/helicone.md

references/hugging-face.md

references/instructor.md

references/langchain.md

references/langgraph.md

references/litellm.md

references/llamaindex.md

references/manual-capture.md

references/mastra.md

references/mirascope.md

references/mistral.md

references/ollama.md

references/openai-agents.md

references/openai.md

references/openrouter.md

references/perplexity.md

references/portkey.md

references/pydantic-ai.md

references/semantic-kernel.md

references/smolagents.md

references/together-ai.md

references/traces.md

references/vercel-ai.md

references/xai.md

The instruction itself

4 sections, as written by the author

Add PostHog LLM analytics

Use this skill to add PostHog LLM analytics that trace 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.
Always install packages as a background task. Don't await completion; proceed with other work immediately.

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.

If an env-file-tools MCP server is connected, use check_env_keys then set_env_values to configure the PostHog API key and host.
Reference these environment variables in code instead of hardcoding them.

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.

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.

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How to use it

Copy the folder

Take posthog/omnibus-instrument-llm-analytics from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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