Token-efficiency proxy for Codex/OpenAI Responses API: recoverable storage, MCP server, opt-in visual compaction, effort routing
View on GitHub ↗TokenSkein is a local context-economy gateway for GPT clients and Codex. It combines the useful ideas behind pxpipe, Headroom, RTK, and Caveman-style output discipline without pretending that every optimization helps every request.
The name combines token with skein: schemas, recovered text, image pages, shell output, style, and reasoning are separate strands managed as one measured bundle.
- proxy OpenAI Responses API requests; - compact tool schemas and older tool outputs; - archive every compacted output locally so exact text remains retrievable; - expose compression, retrieval, and statistics over MCP; - route reasoning effort from task complexity; - inject a concise-output policy with safety exceptions; - optionally encode very dense text as PNG pages when an explicit economic gate passes; - optionally place a safe output filter after RTK for recognized read-only shell commands.
Status: working experimental MVP. Text compaction, storage, MCP, routing, proxying, and the shell adapter are implemented and tested. The vision lane is implemented but disabled by default until model-specific A/B evaluation establishes its real cost and quality.
Context cost has several different causes, so one trick is not enough:
- repeated schemas waste input tokens; - large logs and JSON consume context even when only a few lines matter; - verbose responses consume output tokens; - easy tasks do not always need high reasoning effort; - dense visual text can sometimes cost fewer model tokens than raw text, but can also damage exactness or cost more; - lossy compression becomes dangerou
From the project README.
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