Skim

Skim: The Most Intelligent Context Optimization Engine for Coding Agents

**Code skimming. Command rewriting. Test, build, and git output compression. Codebase heatmap. Token budget cascading.** 18 languages. 14ms for 3,000 lines. Built in Rust.

Other tools filter terminal noise. Skim understands your code. It parses ASTs across 18 languages, strips implementation while preserving architecture, then optimizes every other type of context your agent consumes: test output, build errors, git diffs, raw commands, and codebase heatmaps. 14ms for 3,000 lines. 48x faster on cache hits.

[](https://dean0x.github.io/x/skim/) [](https://github.com/dean0x/skim/actions/workflows/ci.yml) [](https://crates.io/crates/rskim) [](https://www.npmjs.com/package/rskim) [](https://crates.io/crates/rskim) [](LICENSE)

Why Skim?

**Context capacity is not the bottleneck. Attention is.** Every token you send to an LLM dilutes its focus. Research consistently shows attention dilution in long contexts -- models lose track of critical details even within their window. More tokens means higher latency, degraded recall, and weaker reasoning. Past a threshold, adding context makes outputs worse. While other tools stop at filtering command output, Skim parses your actual code structure and optimizes the full spectrum of agent context: code, test output, build errors, git diffs, and commands. Deeper, broader, and smarter than anything else available.

Take a typical 80-file TypeScript project: 63,000 tokens. That contains maybe 5,000 tokens of actual signal. The rest is implementation noise the model doesn't need for architectural reasoning.