A deterministic middleware to compress LLM prompts by 50-80%
Deterministic prompt compression cuts tokens 50-80% without extra model calls.

Modular context folders beat monolithic prompts for scaling AI agent instructions.
AI developers, Prompt engineers
Cursor Rules · Anthropic Context · LangChain
Deterministic prompt compression cuts tokens 50-80% without extra model calls.
The CPU/kernel/process analogy is more than marketing — the project actually spawns short‑lived Sub‑Agents to limit context pollution and pairs that with a GitHub 'App Store' for one‑click skill installs. It's a practical, Windows‑centric proof‑of‑concept with sandboxing and security scans, but it isn't a clear leap beyond existing agent frameworks (LangChain/AutoGPT) and the repo looks early-stage with a small community.
450+ medical research skill templates, but it's a curated collection competing with prompt libraries.
Speculative manifesto with no shipped code or working demo.
SLM classifiers compress context based on tool call intent before LLM sees it.
50 markdown prompt templates for affiliate marketing. Works with any LLM.