Open-source operating system for OpenAI Codex workflows: agents, skills, hooks, rules, memory, safety gates, and cross-harness adapters. - mturac/everything-openai-codexEOC turns raw OpenAI Codex into a maintained engineering environment: 60 agents, 230 skills, 110 rules, hook gates, session memory, and adapters for Cursor, OpenCode, Gemini, Zed, Copilot, and Trae. Not a prompt dump — a field-tested workflow system. MIT.
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Hunter
📌
Hey Hunters,
I'm Mehmet Turac, the maker. I built EOC because every serious
OpenAI Codex project I worked on kept re-discovering the same
gaps: no session memory, no quality gates, no reusable skills,
no cross-tool portability. Every team was rebuilding the same
operating layer from scratch.
EOC packages that layer once, properly:
- 60 specialized agents (planner, code-reviewer, security-reviewer,
language-specific reviewers for TS/Python/Go/Java/Kotlin/Rust/C++/Perl)
- 230 skills covering backend, frontend, security, ML, docs, ops,
release work, content, and investor materials
- 110 rules across 12 language ecosystems
- Hook gates (no-verify blocks, supply-chain scanners, MCP health checks)
- Session memory + compaction + handoff-friendly logs
- Cross-harness adapters for Cursor, OpenCode, Gemini, Zed, Copilot,
Trae, and Codex app + CLI
v2.0.0-rc.1 ships with a desktop dashboard (Tkinter), the Hermes
operator workflow, and an EOC 2.0 Rust control-plane alpha.
MIT licensed. Field-tested on daily software work, not a
prompt collection.
Would love to hear from anyone running Codex at scale — what
breaks first in your setup? That's usually where EOC's next
skill comes from.
GitHub: github.com/mturac/everything-openai-codex