executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
llm-providersConfigure and troubleshoot LightRAG LLM, embedding, VLM, role-specific LLM, rerank, asymmetric embedding, provider binding, and cache-identity behavior.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when work involves LightRAG model providers, embedding wrappers, VLM image input support, role-specific LLM routing, reranking, provider options, credentials, service endpoints, asymmetric embeddings, or LLM cache identity.
python scripts/check_llm_symbols.py, or use --json for machine-readable output.lightrag.llm provider bindings, EmbeddingFunc, wrap_embedding_func_with_attrs, role LLM configuration, API binding options, rerank bindings, asymmetric embedding configuration, VLM image inputs, response-format compatibility, and LLM cache identity.../storage-backends/SKILL.md.../document-pipeline/SKILL.md.../api-server/SKILL.md.LightRAG initialization, QueryParam, and core RAG application flow, route to ../core-rag/SKILL.md..func when wrapping an already-decorated embedding function inside a new EmbeddingFunc to avoid nested wrapper conflicts.model_name, or asymmetric behavior changes vector semantics; plan vector rebuild or re-index work with the storage skill area.