Pydantic

Route broad requests to set up Pydantic Logfire across application instrumentation, infrastructure, evals, frontend observability, Gateway, or coding agents. Use for "set up Logfire", "add Logfire to my project", "get me set up properly with Logfire", "send as much data as would be useful", mentions of Logfire without specific scope, or multi-surface requests; use the matching specialist directly for an already scoped request.

Set up and troubleshoot Pydantic Logfire frontend observability and session replays in JavaScript/TypeScript browser applications. Use for browser errors, RUM, Web Vitals, replay recording, privacy controls, or reducing browser telemetry volume. Supports browser entrypoints, React/Vite, and the client side of Next.js; use logfire-instrumentation for server-side telemetry.

Configure an application's model calls to use the Pydantic AI Gateway. Use when the user asks to route application inference through Gateway, connect a Gateway key, or add a Gateway provider snippet. This does not configure or instrument coding agents.

Monitor hosts, Docker containers, Kubernetes clusters, database/queue/cache servers, and cloud-provider metrics with Pydantic Logfire — no application code required. Use this skill whenever the user asks to "monitor my host/server/VM", "monitor my Docker containers", "monitor my Kubernetes cluster", "send infrastructure metrics to Logfire", "watch my database/Postgres/Redis/MongoDB/Kafka", "collect cloud metrics" (AWS/GCP), or mentions the OpenTelemetry Collector in the context of Logfire. This is infrastructure only — for instrumenting APPLICATION CODE (traces, logs, AI/agent spans) use the logfire-instrumentation skill instead.

Add Pydantic Logfire observability to application code — traces, logs, metrics, and AI/agent spans. Use when the user asks to add or configure Logfire, observability, tracing, logging, or monitoring; maximize useful telemetry; or understand what an app is doing. Supports Python, JavaScript/TypeScript, Rust, and major AI agent frameworks including Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, LangChain, LangGraph, CrewAI, AutoGen, and Google ADK. For infrastructure-only monitoring (hosts, Docker, Kubernetes, databases, or cloud metrics with no app-code changes), use `logfire-infrastructure`. For evaluating AI/agent behavior against test datasets, use `logfire-evals`.

Query and analyze Logfire telemetry data — traces, logs, spans, metrics, summaries, and SQL results. Use this skill when the user asks to "query logfire", "search traces", "find logs", "query data", "search spans", "look up errors in logfire", "get metrics from logfire", "analyze telemetry", "summarize errors", "find root cause", or add Logfire querying capabilities to code. Do not use this skill for direct Logfire UI, browser, live-view, Explore-page, or link-opening requests; use logfire-ui instead. If "show" or "view" wording is ambiguous, ask whether the user wants a UI view or query analysis.

Migrate Python LangChain Deep Agents applications to Pydantic AI and Pydantic AI Harness. Use when the source uses the upstream `deepagents` package or demonstrably reproduces its middleware, backends, skills, memory, subagents, or sandbox contracts. Use `migrating-langchain-to-pydantic-ai` for plain LangChain, LangGraph, or LCEL migrations and `pydantic-ai-harness` for greenfield Harness usage.

Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

OpenAPPA

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Allganize Inc.

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Iterate.ai

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LiteLLM documentation

docs.litellm.ai

Docs for LiteLLM, the most widely used open-source AI gateway: a Python SDK and a self-hosted gateway that…