LLM Analytics Setup Agent Skill

by PostHog

PostHog LLM analytics for all supported providers

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 llm-analytics-setup

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

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

4 sections, as written by the author

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

Other skills for the same job

different authors, same section of the catalogue

Datacommons Client

by christophacham

×3

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

20k tokens

Senior Data Scientist

by ComeOnOliver

×3

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

35k tokens

Tensorboard

by Orchestra-Research

×1

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

How to use it

Copy the folder

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

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.