生物神经元参数学习SNN方法论。联合优化突触权重和神经元内在参数，结合Lempel-Ziv复杂度实现可解释的时空脉冲数据分类。适用于脉冲神经网络、神经形态计算、可解释AI。触发词：生物神经元、SNN参数学习、Lempel-Ziv复杂度、神经形态计算、biological neuron、LZC、spiking neural network。
操作并扩展NanoClaw v2，这是ECC基于claude -p构建的零依赖会话感知REPL。
Gateway to 400+ bioinformatics skills from bioSkills and ClawBio. Covers genomics, transcriptomics, single-cell, variant calling, pharmacogenomics, metagenomics, structural biology, and more. Fetches domain-specific reference material on demand.
Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
Analyze skin health data, identify skin problem patterns, assess skin health status. Supports correlation analysis with nutrition, chronic diseases, and medication data.
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.