LLM Observability
AI and machine learning skill, available on Zeplik
LLM Observability is a ready-to-run AI and machine learning skill on Zeplik. Tracing, monitoring and experiment tracking for LLM and ML apps — Langfuse, LangSmith, Phoenix, MLflow, Weights & Biases, TensorBoard. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The LLM Observability skill loads automatically when your request matches it, or you can invoke it directly by typing /llm-observability in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the LLM Observability skill can do
- Instrument LLM apps with tracing using Langfuse, LangSmith, or Phoenix
- Set up ML experiment tracking and model registries with MLflow or Weights and Biases
- Visualize training metrics and debug models with TensorBoard
- Deliver runnable instrumentation code matching the user's existing stack and conventions
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Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the LLM Observability skill works
/llm-observability
Umbrella for LLM/ML observability. The user wants visibility into an app or training run; establish what to capture (traces, metrics, evals, artifacts) and pick the platform, then deliver instrumentation code. For one-off benchmark scoring route to llm-evaluation-harnesses.
Dispatch table
Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.
| Topic | Read |
|---|---|
| Expert in Langfuse - the open-source LLM observability platform. | references/langfuse.md |
| LLM observability platform for tracing, evaluation, and monitoring. | references/langsmith-observability.md |
| Track ML experiments, manage model registry with versioning, deploy models to productio… | references/mlflow.md |
| Open-source AI observability platform for LLM tracing, evaluation, and monitoring. | references/phoenix-observability.md |
| Visualize training metrics, debug models with histograms, compare experiments, visualiz… | references/tensorboard.md |
| Track ML experiments with automatic logging, visualize training in real-time, optimize… | references/weights-and-biases.md |
How to work
- Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
- Read the matching reference file(s) before answering. Read at most 2-3 per turn.
- Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
- Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.
Usage
/llm-observability $ARGUMENTS
How to use the LLM Observability skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the LLM Observability skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /llm-observability to invoke the skill directly. Zeplik recognizes the LLM Observability skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 umbrella-consolidation)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the LLM Observability skill?
- LLM Observability is a ready-to-run AI and machine learning skill on Zeplik. Tracing, monitoring and experiment tracking for LLM and ML apps — Langfuse, LangSmith, Phoenix, MLflow, Weights & Biases, TensorBoard. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use LLM Observability on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /llm-observability in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the LLM Observability skill use?
- Any model you choose. Zeplik works across every model in one chat, so the LLM Observability skill runs on your preferred model for the task.
- Where does the LLM Observability skill come from?
- The LLM Observability skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the LLM Observability skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- Agent MemoryMemory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Use for "give my agent memory / manage context"; for vector stores see vector-databases.
- AI Safety GuardrailsSafety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Use for "add safety/moderation/guardrails to my LLM app"; for evaluating safety see llm-evaluation-harnesses.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
More on Zeplik
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