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。
retrieval-and-utilities【效率与协作】Dense semantic search utilities, retrieve-and-rerank orchestration, hard-negative mining, embedding quantization, similarity helpers, corpus chunking, and vector DB/service cautions for sentence-transformers.
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Use this sub-skill when a task asks for dense retrieval, semantic search over precomputed embeddings, a retrieve-then-rerank handoff, hard-negative mining, embedding quantization for retrieval, paraphrase/community utilities, or similarity helper selection.
sentence_transformers.util.semantic_search; see references/api-reference.md.corpus_id from dense hits, then hand the selected texts and IDs to a CrossEncoder; see references/retrieval-reference.md.sentence_transformers.util.mine_hard_negatives with explicit dataset column names, output format, and score/margin filters.query_chunk_size, corpus_chunk_size, top_k, embedding precision, and optional FAISS/USearch/vector DB integration.SentenceTransformer.encode choices belong in ../embeddings-and-similarity/SKILL.md.../reranking-cross-encoder/SKILL.md.../sparse-encoder-search/SKILL.md.../evaluation-and-training/SKILL.md.python scripts/semantic_search_demo.py --help to inspect the safe demo CLI.python scripts/semantic_search_demo.py --toy-tensors for a network-free semantic search example.python scripts/semantic_search_demo.py --model MODEL_NAME_OR_PATH --local-files-only only when the model is already available locally.references/api-reference.mdreferences/retrieval-reference.mdreferences/troubleshooting.mdscripts/semantic_search_demo.py