LangGraph Tutor
v1.0.0Architect and deploy advanced LangGraph AI pipelines with stateful graphs, conditional routing, human-in-the-loop, persistence, and streaming execution featu...
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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OpenClaw
Benign
high confidencePurpose & Capability
The name/description match the SKILL.md: it teaches how to design stateful LangGraph pipelines, routing, human-in-the-loop, persistence, and streaming. The instructions reference LangGraph/LangChain concepts that are appropriate for this purpose.
Instruction Scope
The runtime instructions focus on state schema, graph/node construction, routing, compilation, and production patterns (interrupts, checkpointers, streaming). They do not direct the agent to read unrelated system files, exfiltrate secrets, or call unexpected external endpoints. They do, however, instruct the implementer to integrate persistence (databases/checkpointers) and tools/LLM bindings — which is expected for the stated goal.
Install Mechanism
There is no install spec (instruction-only). The SKILL.md references Python modules (e.g., langgraph.*, langchain_core.*, MemorySaver) but the skill does not declare dependencies or an install step. This is coherent for a tutorial, but users should be aware they will need to install those libraries in their environment before following the instructions.
Credentials
The skill requests no environment variables or credentials. The instructions do suggest using persistence (databases/checkpointers) and human-in-the-loop and dynamic tool bindings; those features will typically require credentials and external services when implemented, but the skill itself does not request them. This is proportionate to a tutorial but means real deployments will need careful credential management.
Persistence & Privilege
always is false and model invocation is allowed (default). The skill does not request permanent presence or attempt to modify other skills or system-wide settings. It documents using persistent storage for the pipelines, which is normal given the goal.
Assessment
This is an instruction-only skill (no code or install spec) that teaches how to build stateful LangGraph pipelines. It is internally consistent with its stated purpose, but before you use it or implement the examples: 1) Verify and install the referenced Python packages (langgraph, langchain_core, any checkpointer libraries) from trusted sources and pin versions. 2) Be cautious when implementing persistence or dynamic tool bindings — those will require database credentials and tool access; follow least-privilege practices and do not reuse high-privilege keys. 3) Test new pipelines in an isolated environment (sandbox) before running them in production, since agent nodes can invoke tools/LLMs and thereby execute actions with whatever permissions you grant. 4) If you plan to enable autonomous invocation or connect to external services (webhooks, human approval UIs), review those integrations and audit logs. If you want a stronger assurance, ask the publisher for an explicit dependency list and example repository (requirements.txt/pyproject) so you can review the exact packages and versions.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.