Sentinel – LLM browser automation using 10x fewer tokens
Token efficiency beats Stagehand — 2-5k vs 29-51k per action with cached selectors.

MAKO compresses what matters into a HEAD-friendly payload — frontmatter, declared actions and semantic links — so agents can find relevance without downloading 181KB of navigation, ads and scripts. The project ships a spec plus real tooling (typed SDK, Express middleware, an analyzer/score and edge-friendly /md conversion), which is a rare combo of protocol thinking and usable developer ergonomics. Whether it becomes a standard depends on buy-in from CMS/plugin authors and agent platforms, but technically it's a smart, practical swing at an obvious pain point.
Website owners & marketers, backend/frontend developers and platform teams, AI agent and search tool developers
Token efficiency beats Stagehand — 2-5k vs 29-51k per action with cached selectors.
Language purpose-built for token costs: 55 tokens vs 120 in JavaScript. Real compiler, 1291 tests.
Tree-sitter extraction cuts LLM context 50-tokens-to-8 tokens. Cursor and Cody ignore this.
MCP-as-CLI cuts tool schema tokens 96-99%, discovered on demand not injected.
Replaces UUIDs with space-separated words to slash token costs in LLM prompts and tool calls.
Multilingual tokenization comparison across Arabic, Chinese, French that LangSmith ignores.