AI Coding Tools Used
This repository contains config files from these AI coding tools, indicating AI-assisted development.
GitHub Topics
Language breakdown (by bytes)
Why this is classified AI-related
The AI relevance score checks four places for AI keywords and adds the weight of each one that matches. Full methodology
Total AI relevance score: 70 / 100
Position among AI repos in the same language
We track 19,111 Python repos, of which 14,885 score 40 or above on AI relevance. 3,357 have more stars than this one (top 17.6%), and 4,144 score higher (top 21.7%).
Owner
Hi, I am always sleeping.
Dates
| Created on GitHub | 2026-09-12 |
| Last push | 2026-10-03 |
| First seen here | 2026-09-12 |
| Last fetched | 2026-10-05 16:19 |
Similar repos (same language)
Experiential is the open source, zero markup gateway for BYOK, self-hosted and 1000+ marketplace models. It learns from your traffic to cut costs, recommend better models, and train a specialized model you own.
WenyuChiou/awesome-agentic-ai-zhA trilingual (繁中 / English / 简中) learning roadmap for agentic AI: from LLM basics to multi-agent systems, with 240+ curated resources and hands-on examples. 中文 AI agent 學習地圖。
AminBlg/SimpleEnglishAgent skill: make LLMs write docs in ASD-STE100 Simplified Technical
Sophomoresty/gemini-web2apiConvert Google Gemini web into OpenAI-compatible API. Zero auth, cross-platform, single file.
HarnessRouter/harnessrouterHarnessRouter Community Edition: the self-hosted, Apache-2.0 edition of the unified interface for agent harnesses. Run Codex, Claude Code, Hermes, PI, DSH, and more through one API, with sessions, streaming, files, cancellation, and failure handling. Implements the Unified Harness Protocol (UHP), an open standard. Your keys, your infrastructure.
Alisa0808/vox-directorTurn one topic into a finished Vox-style paper-collage explainer/ad video — automated end to end on Atlas Cloud + ffmpeg. An agent skill.
coji/natural-japanese仕事の日本語を、読みやすくわかりやすく書く・直すための Agent Skill です。
ray-r-ren/agent-apprenticeshipThe living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.