Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
npx skills add https://github.com/arize-ai/arize-skills/tree/main/skills/arize-instrumentationAdds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
This repo contains 2 individual skills — each has its own dedicated page.
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
Specialized Agent Skills to automate GitHub workflows using the GitHub CLI
Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.
Starting point of the PyAuto workflow: prompt registry and prompt-coupled Claude Code skills.
A2A (Agent-to-Agent) Protocol skill for OpenClaw — enables AI agents to discover peers, send tasks, and exchange structured data via the Google A2A protocol
Automate Chmeetings tasks via Rube MCP (Composio). Always search tools first for current schemas.
🚀 Enhance your machine learning workflow with essential MLX skills from this ready-to-use repository.