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Found 8 Skills
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
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).
(NS) LangGraph.js agent-api — greenfield scaffold (bootstrap-agent-runtime), StateGraph, MCP tools, skill bind/inject, checkpointers, context-window trim, HITL/SSE, prompt/locale placement. Use for new agent-api from scratch, LangGraph graphs, MCP wiring, orphan layout, system-prompt compose, bind parity, graph-spec sync, or "fix my LangGraph agent" / "wire MCP tools" / "translations in the graph". Feature diffs via ns-coder; version features via ns-spec-driven; LangGraph vs CrewAI via ns-multi-agent-architect. Do NOT use for CrewAI crews, generic web apps, or SDD-only requirements with no agent-api.
(NS) Language-agnostic conceptual agent design from foundation-model adaptation (Evaluation, Prompt, RAG, Agent, Fine-Tune). Use when designing an agent, deciding agent vs RAG vs prompt vs fine-tune, tool taxonomy, plan/execute separation, agent memory, agent evaluation, guardrails, or observability for agents — even if user never says "AI engineering". Do NOT use for LangGraph/CrewAI choice (`ns-multi-agent-architect`), LangGraph runtime (`ns-langgraph-agents`), Postgres RAG schema (`ns-postgres-rag`), or app coding.
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.
Integrate PICA into a LangChain/LangGraph Python application via MCP. Use when adding PICA tools to a LangChain agent, setting up PICA MCP with LangChain, or when the user mentions PICA with LangChain or LangGraph.
Comprehensive Python engineering guidelines for writing production-quality Python code. This skill should be used when writing Python code, performing Python code reviews, working with Python tools (uv, ruff, mypy, pytest), or answering questions about Python best practices and patterns. Applies to CLI tools, AI agents (langgraph), and general Python development.
Scaffold a minimal local LangGraph agent in Python by following the official quickstart. Use when the user wants to quickly build or try a LangGraph agent locally.