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Found 270 Skills
Workflow to build a sourced, corroborated profile of a named individual from public records, social platforms, professional networks, court and property filings, licensing boards, patents, papers, and obituaries. Use for due diligence, background and fraud investigation, journalism, skip tracing, missing persons, hiring integrity checks, or auditing your own exposure.
Setup Sentry Tracing (Performance Monitoring) in any project. Use when asked to enable tracing, track transactions/spans, measure latency, or add performance monitoring. Supports JavaScript, Python, and Ruby.
Generate EventSettings metadata to enable or disable Platform Tracing (TraceSpanEvent publishing) in Event Monitoring. Use this skill for any EventSettings enablePlatformTracing metadata work. TRIGGER when: user mentions Platform Tracing, TraceSpanEvent, enable tracing in Event Monitoring Settings, event monitoring tracing toggle, enablePlatformTracing, .settings-meta.xml for Event settings tracing, turn on trace span events, or stop publishing trace spans. DO NOT TRIGGER when: user wants Agentforce agent tracing to Data Cloud (use platform-tracing-agentforce-configure), wants Event Log Files or ELF generation, wants Change Data Capture (use integration-eventing-cdc-configure), or wants ManagedEventSubscription (use integration-eventing-subscription-configure).
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Expert in streamlining and enhancing the development of AI Agent Applications, including AI app / agent / workflow code generation, AI model comparison and recommendation, tracing setup, and evaluation planning / setup / execution.
Instruments Python and TypeScript code with MLflow Tracing for observability. Triggers on questions about adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, or tracing specific frameworks (LangGraph, LangChain, OpenAI, DSPy, CrewAI, AutoGen). Examples - "How do I add tracing?", "How to instrument my agent?", "How to trace my LangChain app?", "Getting started with MLflow tracing", "Trace my TypeScript app"
Full Sentry SDK setup for Ruby. Use when asked to add Sentry to Ruby, install sentry-ruby, setup Sentry in Rails/Sinatra/Rack, or configure error monitoring, tracing, logging, metrics, profiling, or crons for Ruby applications. Also handles migration from AppSignal or Honeybadger. Supports Rails, Sinatra, Rack, Sidekiq, and Resque.
Setup Sentry Tracing (Performance Monitoring) in any project. Use this when asked to add performance monitoring, enable tracing, track transactions/spans, or instrument application performance. Supports JavaScript, TypeScript, Python, Ruby, React, Next.js, and Node.js.
Full Sentry SDK setup for Python. Use when asked to "add Sentry to Python", "install sentry-sdk", "setup Sentry in Python", or configure error monitoring, tracing, profiling, logging, metrics, crons, or AI monitoring for Python applications. Supports Django, Flask, FastAPI, Celery, Starlette, AIOHTTP, Tornado, and more.
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
Use when an existing agent already works without Prefactor and you need to add tracing for runs, llm calls, tool calls, and failures with minimal behavior changes.
Expert skill for using Future AGI — the open-source end-to-end platform for evaluating, observing, and improving LLM and AI agent applications with tracing, evals, simulations, datasets, gateway, and guardrails.