Skill UI

Guides how to stretch limited LLM context windows by compressing, masking, caching, and partitioning information so agents process longer conversations or documents without enlarging models; includes when to trigger optimizations and concrete patterns for summaries, masking, KV-cache reuse, and budget tracking.

Extends transformer context windows using RoPE, YaRN, ALiBi, and position interpolation so long documents (32k-128k+ tokens) can be processed, pre-trained models lengthened, and efficient positional encodings or extrapolation strategies deployed.

Optimize Perplexity Sonar API calls by selecting appropriate models, caching query hashes, streaming long research, and batching deduplicated requests for faster, cost-effective research or fact-checking workflows.

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.