Skill UI

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

Basic Text Processor provides word/character statistics, simple text transformations, batch file handling, and dual JSON or human-readable outputs to demonstrate a CLAUDE skill template.

Guides production-level computer vision engineering with object detection, segmentation, model optimization, and deployment, covering YOLO/Faster R-CNN/DETR, Mask R-CNN/SAM, vision transformers, PyTorch tooling, ONNX/TensorRT pipelines, and deployment best practices.

Provides senior-level project management guidance for enterprise software and SaaS transformation initiatives, covering portfolio health, risk quantification, and executive reporting across multi-workstream, high-budget programs.

High-quality Sentence Transformers framework that ships 5000+ pretrained models for multilingual, multimodal, and domain-specific embeddings; ideal for RAG, semantic search, clustering, and production vector stores without relying on external APIs.

Automates spark job creation within data pipelines by guiding etl, transformation, orchestration, and streaming workflows; includes industry best practices, code templates, and validation to deliver production-ready jobs.

Automates Spark SQL optimizer tasks in data pipelines by guiding best practices, producing production-ready code and configurations, and validating ETL, transformation, orchestration, and streaming workflows.

Guides researchers through training and analyzing Sparse Autoencoders with SAELens to decompose neural activations into interpretable concepts, understand superposition, and steer or audit transformer models with sparse feature math.

This skill provides systematic patterns and best practices for transforming slow database queries into lightning-fast operations. It covers advanced topics such as proper indexing strategies, detailed query plan analysis (EXPLAIN), resolving common issues like N+1 problems, and designing highly performant database schemas. Use this when debugging performance bottlenecks, improving application response times, or ensuring scalability for growing datasets.

Automated guidance for SQL transform helper tasks within data pipelines, covering ETL, data transformation, workflow orchestration, and streaming, while following best practices, generating production-ready code, and validating results.

Guides mechanistic interpretability research with TransformerLens by exposing HookPoints, activation caching, activation patching, and circuit-analysis workflows to inspect and manipulate GPT-style models for reverse-engineering attention behavior and causal tracing.

Step through assessment, export, transformation, import, and verification to migrate meetings from Otter.ai, Fireflies, Rev, Zoom, Teams, and other services into TwinMind with platform-specific exporters and validation tooling.