{"id":"spillwavesolutions-mastering-langgraph-agent-skill","name":"mastering-langgraph-agent-skill","af_score":37.5,"security_score":26.0,"reliability_score":23.8,"what_it_does":"A Python educational skill/repository that teaches common LangGraph patterns for building stateful, tool-using, multi-step AI agents (including persistence/memory, human-in-the-loop, and multi-agent workflows), with a focus on agent “Agent Skill Standard” packaging and deployment guidance.","best_when":"You want practical guidance for implementing LangGraph agent skills in Python and integrating those graphs into your own applications.","avoid_when":"You need a network API surface (REST/GraphQL/gRPC) or managed deployment with guaranteed SLAs from this specific repository/package.","last_evaluated":"2026-03-30T15:29:13.103703+00:00","has_mcp":false,"has_api":false,"auth_methods":["None specified for this repo/package itself (relies on underlying LLM/provider credentials, e.g., OpenAI/Anthropic)"],"has_free_tier":false,"known_gotchas":["Examples rely on in-memory checkpointers (InMemorySaver) which may not provide persistence across processes; production usage requires appropriate checkpointer/storage.","Agent workflows can be sensitive to state schema design (e.g., message aggregation via operator.add) and thread_id consistency.","Tool-using agents require careful tool error handling and guardrails; the repo content shown here is conceptual and may not include robust patterns for all failure modes."],"error_quality":0.0}