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Found 338 Skills
Use when building a custom provider integration on top of @prefactor/core so your app can instrument agent, llm, and tool workflows without relying on a prebuilt adapter package.
Python OpenTelemetry style: module-scope tracers/meters, decorators for bounded work, error spans, logs, and no wrappers.
This guide provides step-by-step instructions for recording Lynx performance traces. Use this guide when the user asks how to record a trace.
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
Reference for all GrepAI MCP tools. Use this skill to understand available MCP tools and their parameters.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for CTF web, API, SSR, frontend, queue-backed app, and routing challenges. Use when the user asks to inspect a site or API, follow real browser requests, debug auth or session flow, trace uploads or workers, find hidden routes, or explain why frontend and backend behavior diverge under sandbox-internal routing. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
[Fix & Debug] Investigate and explain how existing features or logic work. READ-ONLY exploration with no code changes.
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.
Analyze implementation details, trace data flow, and explain technical workings with precise file:line references. Use when you need to understand HOW code works.
Expo / React Native OpenTelemetry style: bootstrap guards, init ordering, inline endpoint + ingest key, mobile-compatible exporters, and product action spans.
Guide developers through capturing diagnostic artifacts to diagnose production .NET performance issues. Use when the user needs help choosing diagnostic tools, collecting performance data, or understanding tool trade-offs across different environments (Windows/Linux, .NET Framework/modern .NET, container/non-container).
Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".