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Found 17 Skills
Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI. Detects installed AI SDKs and configures appropriate integrations.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
List Langfuse sessions. Use when checking user sessions, analyzing conversation flows, or monitoring session activity.
Integrate Portkey AI Gateway into TypeScript/JavaScript applications. Use when building LLM apps with observability, caching, fallbacks, load balancing, or routing across 200+ LLM providers.
Instrument LLM applications with Langfuse tracing. Use when setting up Langfuse, adding observability to LLM calls, or auditing existing instrumentation.
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.
AI 도입 전략, Build vs Buy, 우선순위 설정, 거버넌스/보안, 6개월 확장 로드맵을 다루는 모듈.
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"
Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Multi-agent systems with LangGraph - supervisor/swarm/handoff/router patterns, state coordination, Deep Agents, guardrails, testing, observability, deployment. Use when building multi-agent workflows, coordinating agents, or need cost-optimized orchestration. Uses Claude, DeepSeek, Gemini (no OpenAI).