Skills for “llm”

Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom…

…so every agent turn is one trace in the Lens UI with its LLM calls, tokens, tool calls, memory and document retrievals, and which of them entered the prompt. Detects how the agent runs (LangGraph or ADK attach,…

…hand-written routing (keyword if/else chains, regex, or an LLM call that only picks a route) with a Redis Iris Intent Router through redis-iris, so a message maps to a defined intent and its dispatch target by example…

Use when the user wants to configure LLM integration for a DataRobot agent application. This skill helps to change LLM model, switch between the LLM integrations the project supports (LLM Gateway, a DataRobot-deployed…

Call Large Language Models through Domino LLM Gateway 2.0, the centrally governed gateway extension an administrator installs in front of external providers and Domino-hosted models. Covers finding and detecting the…

…and implement how code running in Domino reaches a Large Language Model, LLM Gateway 2.0 first where deployed (routed to domino-llm-gateway); otherwise Option A, an external provider (OpenAI, Anthropic, AWS Bedrock,…

Trace and evaluate GenAI applications (LLM calls, agents, RAG pipelines, multi-step AI systems) in Domino with the Domino agents SDK, using the @add_tracing decorator, span and trace evaluators, DominoAgentContext /…

Set up GenAI tracing for an LLM or agent application in Domino by checking the Domino SDK and MLflow requirements (the DSE needs nothing installed), then adding a tracing_setup.py helper, span evaluators, a config.yaml,…

Migrate a GenAI/LLM app off Traceloop/OpenLLMetry onto OpenInference with its OTel gen_ai.* semconv option enabled (`enable_genai_semconv` / `OPENINFERENCE_ENABLE_GENAI_SEMCONV`). Covers detecting the existing Traceloop…

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate…

…decision process for instrumenting a GenAI/LLM app (Python or TypeScript, any provider/agent SDK) so it emits OTel GenAI semantic-convention (gen_ai.*) spans queryable in Dynatrace. Four steps: detect,…

Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.

Checklist for code that calls a language model or exposes a system to one. Use when writing or reviewing code that calls a model API (Anthropic, OpenAI, Bedrock, Gemini, Perplexity), a prompt or prompt template,…

Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.

…evaluations — `hog` (deterministic code-based), `llm_judge` (LLM-prompt-based), and `sentiment` (user-message sentiment). Find existing evaluations, inspect their configuration, run them against specific…

Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions. Use when the user asks "how much are we spending…

Add PostHog AI Observability 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…

Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually…

Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out…

One API and one credential for frontier and open-source LLMs and embedding models, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, generate embeddings, add AI/chat/an agent…