by code-yeongyu
The one and only agent harness for complex codebases. Project memory, planning, execution, and verified completion inside Codex.
# Add to your Claude Code skills
git clone https://github.com/code-yeongyu/lazycodexLast scanned: 6/1/2026
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}lazycodex is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by code-yeongyu. The one and only agent harness for complex codebases. Project memory, planning, execution, and verified completion inside Codex. It has 3,346 GitHub stars.
Yes. lazycodex passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/code-yeongyu/lazycodex" and add it to your Claude Code skills directory (see the Installation section above).
lazycodex is primarily written in TypeScript. It is open-source under code-yeongyu on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh lazycodex against similar tools.
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
[!NOTE] [OmO] 60K Stars: the terrifying token burner has arrived in LazyCodex.
Sisyphus Labs' OmO is the quality-obsessed agent harness whose public lore says it loved Anthropic models hard enough to get third-party clients blocked. Now that same OmO quality bar is available for Codex through LazyCodex.
If you wanted OmO but did not want the setup ceremony, start here:
npx lazycodex-ai installContext: OmO 60K Stars on X
One line. No global install, no npm i -g. Always use npx:
npx lazycodex-ai install
This is shorthand for npx --yes --package oh-my-openagent omo install --platform=codex. For a fully autonomous, no-TUI setup:
npx lazycodex-ai install --no-tui --codex-autonomous
The npx installer above stays the primary path. As an additive, experimental
alternative you can install from inside Codex itself: type /plugins, open the
Add Marketplace tab ("Add a marketplace from a Git repo or local root."),
and enter https://github.com/code-yeongyu/lazycodex, then install omo from
the sisyphuslabs marketplace. Or from the CLI:
codex plugin marketplace add https://github.com/code-yeongyu/lazycodex
codex plugin add omo@sisyphuslabs
On the next launch, approve the omo hooks in Codex's startup review — hooks
never run before approval. The first approved session prints
LazyCodex bootstrap running in background — restart the session when it completes
while a background worker finishes the setup (config blocks, agent roles, bin
links, a pinned sg binary for the ast_grep MCP); restart when it is done.
The marketplace path never touches Codex permission settings — autonomous mode
remains the explicit npx lazycodex-ai install --no-tui --codex-autonomous
choice.
Upgrade with codex plugin marketplace upgrade sisyphuslabs. The next startup
review shows the hooks as Modified — expected after every upgrade —
re-approve them and the following session re-runs bootstrap on the new version.
If anything looks pending or degraded, npx lazycodex-ai doctor explains what
and why. Full details: lazycodex.ai/docs.
npx lazycodex-ai doctor
doctor prints the installation health report: plugin cache, hooks, MCP
servers, agents, and config state. Inside Codex, type $ in the composer to
browse every installed skill — init-deep, ulw-loop, ulw-plan,
start-work, and the rest — and hooks announce themselves with
LazyCodex(<version>): ... status messages during a session.
npx lazycodex-ai uninstall
Removes the installed plugin cache, bin links, agent roles, and the managed
sections of ~/.codex/config.toml.
LazyCodex installs these as OmO commands for Codex. Invoke them with the
$command syntax shown by the installer.
| Command | Type this | What it does |
|---|---|---|
$ulw-loop |
$ulw-loop "task" [--completion-promise=TEXT] [--strategy=reset|continue] |
Self-referential loop that runs until Oracle-verified completion. Caps at 500 iterations in ultrawork mode, 100 in normal mode. |
$ulw-plan |
$ulw-plan "what to build" |
Prometheus strategic planner. Writes a plan to plans/<slug>.md. Never writes product code. |
$start-work |
$start-work [plan-name] [--worktree <path>] |
Executes a plan until every checkbox is done. Prints ORCHESTRATION COMPLETE. |
Full documentation lives at lazycodex.ai/docs.
LazyCodex should be judged by the features it actually installs. It is the Codex distribution for OmO's agent harness: project memory, planning, execution, verified completion, skills, hooks, model routing, and diagnostics.
$init-deep creates project memory$init-deep generates hierarchical AGENTS.md context. It scores complex
directories, writes local guidance near the code that needs it, and gives future
agents landmarks before they edit. Type $init-deep in the Codex composer —
the $ prefix is how every installed skill is invoked.
Use it when the repository is too large to explain from memory. Run it again when the shape of the codebase changes.
Use $ulw-plan when the work needs decisions before implementation. It writes a
plan to plans/<slug>.md and does not touch product code.
Use $start-work when a plan is ready. It executes the checklist with durable
Boulder progress and stops only when the plan is complete.
Use $ulw-loop when the task should keep moving until the result is verified by
evidence instead of a hopeful status update.
The command layer stays simple. The skill layer adds specialist judgment for the actual work:
| Feature | Use it for |
|---|---|
$init-deep |
Hierarchical project memory through AGENTS.md |
$ulw-plan |
Decision-complete planning before code changes |
$start-work |
Durable plan execution with Boulder progress |
$ulw-loop |
Verified completion for open-ended tasks |
review-work |
Multi-angle post-implementation review |
remove-ai-slops |
Behavior-preserving cleanup of AI-looking code |
frontend-ui-ux |
Polished UI surfaces |
programming |
Strict TypeScript, Rust, Python, or Go discipline |
LSP |
Diagnostics, definitions, references, symbols, and renames |
AST-grep |
Structural search and rewrite across code |
rules |
Project instructions from AGENTS, rules, and instruction files |
comment-checker |
Feedback after edit-like operations |
LazyCodex installs selectable agent roles into ~/.codex/agents/: explorer,
librarian, plan, momus, metis, and codex-ultrawork-reviewer. Pick one
by passing agent_type to Codex's spawn_agent tool — the child agent runs
with that role's model and instructions:
spawn_agent({"message": "TASK: map the auth flow end to end.", "agent_type": "explorer"})
The installer exposes agent_type on multi_agent_v2 sessions (Codex hides it
by default). If your Codex build's spawn tool has no agent_type parameter,
describe the role inside message instead — the skills are written to fall
back to that form automatically.
Start at https://lazycodex.ai.
LazyCodex packages OmO (oh-my-openagent) as the Codex agent harness for complex codebases.
Think LazyVim for lazy.nvim, but for Codex.
OmO is the agent harness: discipline agents, parallel orchestration, multi-model routing, skills, hooks, and verified completion. LazyCodex packages that harness for Codex.
"LazyVim made Neovim usable for the rest of us. LazyCodex does the same for Codex."
Credit: The LazyCodex name idea is inspired by LazyVim. The Ultragoal and UltraQA ideas are inspired by oh-my-codex, reimplemented from concept for this Codex setup.
| Feature | Description |
|---|---|
| 🤖 Discipline Agents | Sisyphus orchestrates Hephaestus, Oracle, Librarian. A full AI dev team |
| 🔀 Parallel Execution | Multiple agents working simultaneously on subtasks |
| 🎯 Multi-Model Routing | Automatic model selection per task category |
| 🛠️ Skills System | Extensible skill library for specialized tasks |
| 📋 Hooks & Lifecycle | Pre/post hooks for every agent action |
| 🔧 Zero Config | Sensible defaults, override when you want |
Do not be surprised if an OmO/LazyCodex run shows models like gpt-5.2
with xhigh, gpt-5.4-mini, gpt-5.3-codex, or newer equivalents like
gpt-5.5 with xhigh. That is intentional.
OmO does not blindly spend your best model on every subtask. Its source
defines task categories and fallback chains so the agent can pick the most
appropriate model for the job: quick routes to gpt-5.4-mini for small
edits, ultrabrain uses a high-reasoning GPT model for hard logic, and
agentic coding paths can use Codex-tuned GPT models when available. See
openai-categories.ts
and model-requirements.ts.
The point is quota discipline: use the strongest model when the task needs deep reasoning, use a cheaper/faster model when that is enough, and keep parallel agent work efficient instead of burning premium quota on routine steps. This is benchmark-driven routing, not random model churn:
xhigh.