Madhan S

Compresses top retrieval chunks into citation-rich summary packets that preserve intent while cutting token usage.

Runs hybrid retrieval with strict FTS-first policy, BM25 lexical ranking, vector merge, and top 3-5 reranking for token-efficient context selection.

Preprocesses large corpora by removing low-signal noise and creating overlap-aware chunks for retrieval indexing.

Delegates software engineering operations to focused subagents using compressed context packets to keep the main conversation lean.

Use this skill whenever the user needs to reduce context bloat, lower token usage, summarize large code/docs corpora, run FTS plus embeddings retrieval, rerank top chunks, and produce compact context packets before implementation work. Also trigger when users say context is too long, reduce tokens, optimize prompt size, retrieve top chunks, compress context, or ask for cost-saving prompt workflows.