ariaxhan

34 mods in 1 plugin, 4 repositories, 16 stars between them.

kernel

01

ariaxhan/kernel-claude

Plugin Claude Code

Bundles 27 skills, 10 agents, 4 hooks · 1,528 tokens together

KERNEL is a Claude Code and Codex runtime for direct, decomposed execution with local memory and minimal hooks. Version 9.11.0 deploys the simplify deletion doctrine, preflight and verifier contract. Skills invoke as /kernel: on Claude Code and $kernel: on Codex; run /kernel:help first. Agent-readable summa.

not rated 14 +1 changed 1mo ago A tokens not measured original MIT

kernel

02

ariaxhan/kernel-claude

Plugin Claude CodeGemini CLI

KERNEL's methodology layer for Gemini CLI: 27 software-engineering agent skills plus llms.txt as ambient context. Skills cover systematic debugging (reproduce, isolate, regression-test), generating competing approaches before implementing, pre-implementation critique, code review, bounded context handoff and resume…

not rated 14 +1 changed 1mo ago A tokens not measured original MIT

agentmailkit

03

ariaxhan/agentmailkit

MCP server Claude CodeCodexCursor +2

Local-first engine for scheduled, LLM-generated email digests that read your own data and never leave your machine. Runs locally from the agentmailkit Python package.

not rated 1 1mo ago A tokens not measured original MIT

site-spec

04

ariaxhan/site-spec

MCP server Claude CodeCodexCursor +2

MCP server: audit any website for SEO, accessibility, and AI-searchability, then auto-fix it. Runs locally from the site-spec-mcp npm package.

not rated 1 2d ago A tokens not measured original Apache-2.0

site-spec

05

ariaxhan/site-spec

Plugin Gemini CLI

Audit any website or built HTML for SEO, accessibility, structured data, llms.txt and AI-searchability, then apply deterministic repairs. 40 checks, 17 auto-fixable.

not rated 1 2d ago A tokens not measured original Apache-2.0

metabrain

06

ariaxhan/metabrain

MCP server Claude CodeCodexCursor +2

A zero-dependency SQLite memory layer for AI agents that learns what works: patterns graduate to proven preferences via a built-in experiment loop. Runs locally from the metabrain Python package.

not rated 0 1mo ago A tokens not measured original MIT