skills

Use when adding neatlogs observability to a Python project that uses LangChain or LangGraph (imports `langchain*` / `langgraph`, builds chains, runnables, or a graph).

Use when adding neatlogs observability to a TypeScript/Node.js project that uses LangChain or LangGraph (depends on `@langchain/*` / `@langchain/langgraph`).

Use when adding neatlogs observability to a Python project that uses CrewAI (imports `crewai`, builds a Crew/Flow with agents and tasks).

Use when adding neatlogs observability to a Go project — Google Gemini (genai), Google ADK agents, or custom code. Covers Init, WrapGenAI, ADK passthrough, Trace, and Identify (sessions & end-users).

Use when adding neatlogs observability to a Python project that uses the Agno agent framework (imports `agno`, constructs Agno `Agent`s).

Use when adding neatlogs observability to a Python project that uses DSPy (imports `dspy`, defines `dspy.Module`s / signatures).

Use when adding neatlogs observability to a Python project that uses the Google Agent Development Kit (imports `google.adk`, builds an ADK agent/runner).

Use when adding neatlogs observability to a Python project that uses the Hermes agent (NousResearch/hermes-agent) as a LIBRARY — i.e. it imports `run_agent` / constructs `AIAgent(...)` in its own code.

Use when adding neatlogs observability to a Python project that uses the OpenAI Agents SDK (imports `agents` / `openai-agents`, defines `Agent`s and calls `Runner.run`).

Use when adding neatlogs observability to a Python project that calls LLM provider SDKs directly (OpenAI, Anthropic, Google GenAI, Groq, etc.) and uses no agent framework.

Use when adding neatlogs observability to a Python project that uses Pydantic AI (imports `pydantic_ai`, constructs an `Agent`).

Use when adding neatlogs observability to a Python LLM/agent project and no framework-specific neatlogs skill matches the stack — i.e. the generic fallback for instrumenting Python apps that call LLMs or run agents.

NeatLogs is an AI agent debugging and observability platform. Use this skill when instrumenting Python LLM applications with neatlogs for tracing, monitoring, debugging, observability, decorators, spans, prompt template tracking, or auto-instrumentation of LLM providers and agent frameworks.

Use when adding neatlogs observability to a Python project that uses Strands Agents (imports `strands`, builds a Strands `Agent`).

Use when adding neatlogs observability to a TypeScript/Node.js project that uses the Mastra framework (depends on `@mastra/core`, builds a Mastra agent/workflow).

Use when adding neatlogs observability to a TypeScript/Node.js project that calls LLM provider SDKs directly (OpenAI, Anthropic, Google GenAI, Bedrock) and uses no agent framework.

NeatLogs is an AI agent debugging and observability platform. Use this skill when instrumenting TypeScript/Node.js LLM applications with neatlogs for tracing, monitoring, debugging, observability, spans, prompt template tracking, or auto-instrumentation of LLM providers and agent frameworks.

Use when adding neatlogs observability to a TypeScript/Node.js project that uses the Vercel AI SDK (depends on the `ai` package, calls `generateText`/`streamText`).