Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

A curated set of agent skills for Qdrant vector search, providing structured knowledge on scaling, optimization, monitoring, deployment, and SDK usage.

CLI client for installing and managing verified AI agent skillsets from the Nori Skillsets registry, supporting multiple coding agents.

中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

AI agent skills for generating high-quality unit tests — Given-When-Then test cases, JUnit 5, Mockito, AssertJ. Works with Claude Code, Cursor, and any AGENTS.md-compatible agent.

A collection of 33 spec-compliant, installable AI agent skills for Go development, encoding experienced Go engineering practices for coding agents.

Uniswap Universal Router SDK - Decode and Encode Transactions - Uniswap V2, V3 & V4

Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while `samber/cc-skills-golang@golang-performance` provides the optimization patterns.

A collection of AI agent skills for integrating Arize observability, tracing, and experiments into LLM applications.

Headless GTM: the full outbound chain as Claude Code and Codex skills on raw vendor APIs.

Claude Code skill for capturing and codifying learnings before session ends / context lost