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Found 7 Skills
Use when analyzing repositories, conducting deep research on codebases, performing architecture reviews, or exploring large projects. Use when the user wants to research or analyze a git repo, a GitHub link, or a repository URL.
LangGraph state-machine design and debugging for `StateGraph`, node/edge routing, checkpoints, `interrupt`, and HITL flows. Use when building or troubleshooting graph-based agents with conditional edges and thread state.
Advanced RAG with Self-RAG, Corrective-RAG, and knowledge graphs. Use when building agentic RAG pipelines, adaptive retrieval, or query rewriting.
LangGraph workflow patterns including basic graphs, conditional routing, parallel execution, and subgraphs. Use when designing graph structure or workflow flow.
Graph engineering for parallel task execution: convert a task, PRD, SPEC, or issue set into a dependency graph (DAG), layer it into supersteps, then implement each independent node concurrently with subagents — each node runs /goal → /review-it → /ship-it in an isolated git worktree, with a fan-in barrier between waves. Triggers on: graph, graph engineering, build a graph, task graph, dependency graph, DAG, parallel implement, 并发实现, 并行实现, 任务图, 把任务变成图, fan-out fan-in, superstep, dynamic workflow.
Run deterministic, resumable end-to-end development across one or more repositories through a durable LangGraph control plane, risk-proportional approval and challenge gates, reusable validation evidence, single-pass review and remediation limits, isolated worktrees, auto-detected headless workers, and validated artifact handoffs.
LangGraph workflow patterns for state management, routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming, subgraphs, and functional API. Use when building LangGraph pipelines, multi-agent systems, or AI workflows.