计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
embeddings-and-generative【效率与协作】Select Chonkie embedding models, provider embeddings, and generative chunking workflows without hidden network assumptions.
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Use this sub-skill when a task depends on embedding vectors, semantic/late/neural/LLM chunking, embedding-based refinement, or provider-backed generation inside Chonkie.
AutoEmbeddings, BaseEmbeddings, Model2VecEmbeddings, SentenceTransformerEmbeddings, CatsuEmbeddings, provider embedding wrappers, or LiteLLMEmbeddings.EmbeddingsRefinery before retrieval, export, or vector storage.SemanticChunker, LateChunker, NeuralChunker, or SlumberChunker with explicit optional dependencies, model cache/download, credential, batching, and timeout decisions.Genie classes used by SlumberChunker: OpenAI, Azure OpenAI, Gemini, Groq, and Cerebras.Chunk/tokenizer contracts: ../chunking-and-types/.../pipelines-and-processing/.../interfaces-and-deployment/.../integrations-and-storage/.references/embeddings-reference.md for embedding/provider selection, constructor arguments, extras, credentials, and batching/timeout notes.references/model-dependent-chunking.md before using EmbeddingsRefinery, SemanticChunker, LateChunker, NeuralChunker, or SlumberChunker.scripts/optional_dependency_probe.py to inspect installed optional modules without making network calls. Use --instantiate-safe only for the script's safe no-network checks.references/troubleshooting.md and select a deterministic fallback or ask the user to authorize the required install/network/API-key path.The Chonkie skill's required environment only verified CPU-local core package behavior. Model downloads, accelerator use, third-party provider APIs, provider credentials, and live generative calls are optional and must be treated as unverified unless the current user explicitly supplies and authorizes those resources.