by undead-undead · Codex Skill · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
AIMAXXING Windows-first, local-model-friendly AgentOS for personal AI workers 简体中文文档 Development Standards What AIMAXXING Is AIMAXXING is a local-first autonomous agent runtime built around one public front agent and many narrow worker agents. The product direction is Windows native first. WSL/Linux paths are used for development, compatibility checks, and bridge-based testing, but they are not the default product deployment story. The current architecture optimizes for: one clear user-facing agent: is the public conversation entry point specialist workers: worker agents equip narrow tools or skills instead of dumping every tool into the front prompt local model ownership: local GGUF/ONNX/Windows ML style model flows are treated as first-class citizens governed memory: short-term continuity, long-term facts, RAG, and sensory writeback are separated but coordinated safe execution: tools, skills, browsers, shell commands, and external runtimes run behind explicit security and policy boundaries observable runtime behavior: traces, witness records, task state, resource pressure, and failures are meant to be diagnosable Current Runtime Shape text User / Panel / Gateway | v aimaxxing...
| Stars | 51 |
| Forks | 1 |
| Language | Rust |
| Category | Codex Skill |
| Quality Score | 27.75/100 |
| Last Updated | 2026-05-14 |
| Created | 2026-03-03 |
| Platforms | rust |
| Est. Tokens | ~5520k |
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AIMAXXING is Your Very Own Agent: The Ultimate, Complete Edition. It is categorized as a Codex Skill with 51 GitHub stars.
AIMAXXING is primarily written in Rust. It covers topics such as agent, ai, aimaxxing.
You can find installation instructions and usage details in the AIMAXXING GitHub repository at github.com/undead-undead/AIMAXXING. The project has 51 stars and 1 forks, indicating an active community.
The top alternatives to AIMAXXING on Agent Skills Hub include starpod, OpenAEON, MMClaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.