oh-my-openagent
OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering.
- 70.3k estrellas
- 5.8k forks
- 1735 issues abiertos
- Lenguaje: TypeScript
- Licencia: NOASSERTION
- actualizado
Instalación
git clone https://github.com/code-yeongyu/oh-my-openagent ~/.claude/skills/oh-my-openagent- Clone the repository into your ~/.claude/skills directory (or copy the skill folder containing SKILL.md).
- Start a new Claude Code session so the skill registry reloads.
- Invoke it by name, or let Claude trigger it automatically when the task matches.
If the repo bundles several skills, copy only the folders you need.
Nota editorial
Oh My OpenAgent (OmO) is a TypeScript-based agent harness and terminal UI designed to orchestrate multiple AI coding models simultaneously against complex codebases, functioning as an alternative layer on top of tools like OpenCode and Codex rather than locking users into a single provider. Its headline feature is Team Mode, which coordinates multiple models (the README demonstrates Kimi K2.6 and GPT-5.5 working together) on the same codebase in parallel. While the project began as a Claude Code skill targeting Anthropic models, the maintainers have since pivoted toward multi-harness support spanning OpenCode, Codex, and other runtimes; a companion tool called LazyCodex installs via `npx lazycodex-ai install` for users who want Codex-focused functionality with less configuration overhead. One documented use case shows a contributor eliminating 8,000 ESLint warnings in a single day. The project targets developers working on large or long-running refactors who want to route tasks across whichever models offer the best cost-to-capability ratio at a given time, rather than paying fixed subscription rates to one provider.
24 items en este repositorio
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
Use ast-grep (sg) for AST-aware code search and rewrite across 25 languages. Trigger for structural code matching or deterministic codemods: find every function/call/class/import shaped like X, rewrite console.log to logger.info, strip `as any`, migrate require() to import, find empty catch blocks or missing await, and scan/apply YAML rules. Prefer this over rg/grep when the target is syntax shape rather than text; use rg for string contents, comments, filenames, or regex-style byte searches.
QA the omo Codex Light edition (lazycodex / packages/omo-codex) itself, in strict isolation so ONLY our plugin is exercised, never the user's real ~/.codex. The first-party method drives the real `codex app-server` against an isolated CODEX_HOME plus a LOCAL mock model (no real API call), and proves a plugin hook fired by asserting hook/started + hook/completed notifications. Also: isolated install verification, per-component hook probes, a tmux TUI smoke, and runtime log observation (RUST_LOG / logs SQLite / /debug-config). Ships tested helper scripts each with a --self-test. Use whenever someone changes anything under packages/omo-codex or wants to QA, smoke-test, verify, or debug the Codex plugin, its hooks/components, the installer/config.toml, the app-server flow, or the Codex TUI. Triggers: codex qa, qa codex, codex-qa, test codex plugin, verify codex hook, codex app-server, lazycodex qa, isolated CODEX_HOME, prove codex hook fired, codex tui test.
MUST USE when asked to find, read, list, search, inspect, fetch, export, or reconstruct coding-agent sessions across Codex, Claude Code/Desktop, OpenCode, OMO/Senpi/pi, oh-my-pi (omp), gajae-code (gjc), OpenClaw, Factory Droid, Amp, Gemini/Kimi/Qwen CLIs, Codebuff, Roo/Kilo/Cline, Kodu, Cursor CLI, Aider, Aside browser-agent sessions, or unknown local agent logs. Covers transcripts, session IDs, rollout JSONL, state SQLite, Claude projects/pre-compact histories, OpenCode messages/parts, child/subagent linkage, cwd/model/time/token filters, archives, and cost clues. Expands fuzzy recall into parallel query lanes and first probes known stores so absent platforms are skipped cheaply. Triggers: coding agent sessions, Codex/Claude/OpenCode/OMO/Senpi/pi/oh-my-pi/omp/gajae-code/gjc/OpenClaw/Droid/Amp/Kodu/Cursor/Aider/Aside sessions, transcript search, session history, session ID, read transcript, token usage, subagent sessions, what did I do yesterday, did we already do this.
Use when Codex needs to understand or respond to automatic comment-checker feedback emitted after an edit-like PostToolUse hook.
Store a dag definition once and re-run it in one or two lines, instead of pasting the full definition JSON into every eval cell. MUST USE whenever the user wants to save a DAG for reuse, run a previously saved/named DAG, schedule the same graph repeatedly (nightly/weekly audits, recurring multi-agent pipelines), or asks where to put a dag definition file. Triggers: dag library, save this dag, reuse a dag, run the saved dag, stored dag definition, recurring dag, nightly dag, dag 정의 저장, 저장된 dag 실행, dag 반복 실행, DAG 만들어두고 여러 번.
Expert data processing with a hybrid engine strategy: resident-kernel engines first - DuckDB plus a resident Python stack (Polars/numpy/matplotlib) in persistent js/py eval kernels where the harness has them, bun/uv one-shots elsewhere - and per-action placement judgment (in-memory vs streaming vs remote-in-place). Triggers: 'analyze the data', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this data', DuckDB vs Polars selection, quick data exploration CLI. NOT for plain text/code inspection, configs, or tiny inline math.
MUST USE for any real runtime debugging across ANY language or binary — crashes, silent failures, wrong responses, stuck processes, memory leaks, async misbehavior, unexplained timing, reverse engineering. Runs a hypothesis-driven loop: form ≥3 hypotheses, investigate in parallel, after 2 failed rounds spawn Oracles from orthogonal angles, confirm root cause, lock with a failing test, fix minimally, QA by actually USING the system, scrub artifacts. The actual HOW lives in `references/` — READ THEM. Triggers: 'debug this', 'why is X not working', 'hanging', 'attach a debugger', 'reverse engineer', 'pwndbg', 'gdb', 'lldb', 'node inspect', 'pdb', 'dlv', 'delve', 'rust-gdb', 'set a breakpoint', 'context window exploded', 'why is the response empty', 'why is this happening', 'trace this bug', 'reproduce and fix', 'silent failure', 'HTTP 200 but empty', 'why did it stop', 'inspect the binary', 'playwright', 'flaky test', 'fails intermittently', 'passes in isolation', 'only fails in CI'.
Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", or any browser interaction request.
MUST USE for frontend/web UI/UX/visual work: building, styling, redesigning pages/components, React setup, performance audits, visual QA, taste, and polish. Routes four rulesets: design taste router and brand references; perfection for Playwright/Chromium Lighthouse/Core Web Vitals; ui-ux-db palettes/fonts/guidelines; designpowers personas/accessibility/critique/handoff; plus curl-only lazyweb real-app-screen research and the beui.dev interaction catalog. Triggers: frontend, UI, UX, design, redesign, styling, layout, animation, motion, interaction, micro-interaction, make it feel alive, premium, luxury, minimal, brutalist, Awwwards, DESIGN.md, mockup, React, Lighthouse, accessibility, WCAG, Core Web Vitals, looks generic, make it pretty, like X brand, lazyweb, design research.
Designer-turned-developer who crafts stunning UI/UX even without design mockups
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
Compare HEAD with the latest published npm versions and list all unpublished changes by release layer. Triggers: unpublished changes, changelog, what changed, whats new.
MUST USE for ANY git operations. Atomic commits, rebase/squash, history search (blame, bisect, log -S). STRONGLY RECOMMENDED: Use with task(category='quick', load_skills=['git-master'], ...) to save context. Triggers: 'commit', 'rebase', 'squash', 'who wrote', 'when was X added', 'find the commit that'.
Read-only GitHub triage for issues AND PRs. 1 item = 1 background task (category: quick). Analyzes all open items and writes evidence-backed reports to /tmp/{datetime}/. Every claim requires a GitHub permalink as proof. NEVER takes any action on GitHub - no comments, no merges, no closes, no labels. Reports only. Triggers: 'triage', 'triage issues', 'triage PRs', 'github triage'.
Explains any senpi tip in depth - startup tips, working tips, and any Tip: line shown in the TUI (including the Fable-5-refusal fallback tip). Use when the user asks about a Tip: line, says give-me-tips, asks what a tip means, how a tipped feature works, or which tips they can see. Queries the live tip list (senpi --list-tips) first, checks what THIS user can actually see, then verifies the real feature code before explaining.
Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
Adversarial multi-agent planning skill for omo-senpi. Self-orchestrates a 5-member hostile team (categories unspecified-low, unspecified-high, deep, ultrabrain, artistry) via the native lead team tools for ruthless cross-critique debate, distills only the insights that survive the attacks, then MANDATORILY hands the distilled bundle to a planner task (load_skills ulw-plan) for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', 'adversarial plan', 'hostile planning', 'cross-critique plan', '하이퍼플랜', '적대적 계획', '교차 비평'.
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
Raw mechanical interfaces fusing Swiss typographic print with military terminal aesthetics. Rigid grids, extreme type scale contrast, utilitarian color, analog degradation effects. For data-heavy dashboards, portfolios, or editorial sites that need to feel like declassified blueprints.
Resumen de Skills
English | 简体中文 | 日本語 | Русский | 한국어 | Español | Français | Deutsch | Português (Brasil) | Italiano
Note
OmO 5.0: OmO ❤️ Pi Install it with
curl -fsSL https://get.omo.dev/install.sh | bash. Memory system, CodeMode, Anthropic subscriptions, all covered.Just type "mass ulw" with your prompt - now you are the master of graph engineering. Multi-model ultracode, together with a better memory system. (The right panel is omo-herdr-dag.)
Sponsors These are our sponsors. They help keep OmO going as a personal side project.
OpenGateway sponsors OmO. An OpenAI-compatible gateway that puts many model providers behind one API. Thank you for backing open source.
Note
OmO is maintained by Jobdori, the AI assistant shown above. Meet your own Jobdori, Dori. Join the waitlist here.
Tip
Be with us!
Join our Discord community to connect with contributors and fellow OmO users. Updates for OmO used to be posted on my X account. Since it was mistakenly suspended, @justsisyphus now posts updates on my behalf. Follow @code-yeongyu on GitHub for more projects.
OmO
Your tool for real work. But it's an agent.
OmO is one omo command that turns your tokens into finished work: research across ten thousand sources, a deck people actually get, backends, frontends, code. It runs on senpi, our fork of pi, with everything below built in.
Install
curl -fsSL https://get.omo.dev/install.sh | bash
omoOn Windows, in PowerShell: irm https://get.omo.dev/install.ps1 | iex. The script installs the native omo binary for your OS and CPU into ~/.local/bin, checks it against the release checksums, and falls back to GitHub Releases when the mirror is unreachable. Add -s -- beta (or a version such as -s -- 5.1.1) after bash to pick another channel. Windows CMD, fixing your PATH, updating and uninstalling are covered at omo.dev/docs/install.
Prefer a package manager? bun add -g omo-ai (or npm i -g omo-ai) installs the same OmO. The package is omo-ai; the unrelated omo package on npm is someone else's.
Open your project, run omo, describe the job. That's the whole setup.
In a session, /thinking sets how hard the model thinks (/thinking alone opens a picker), Shift+Tab cycles through the levels, and /efforts sets the reasoning effort directly on models with effort grades. To pick up earlier work, /resume (or /sessions) opens your past sessions; from the shell, omo -c continues the last one and omo -r opens the picker. /help lists everything else.
Coming from the OpenCode edition or LazyCodex? Run omo setup once. It carries your provider keys, custom providers, MCP servers, skills and model picks across. omo doctor then shows which task categories your providers can run and what the old install left behind, and omo doctor --models shows which model each category and agent runs and in what order (keep one out everywhere with exclude_models). The full walkthrough is Migrating from OpenCode.
To update, run the install line again (a package-manager install updates with omo update). rm ~/.local/bin/omo removes the binary install; bun remove -g omo-ai (or npm uninstall -g omo-ai) removes the package install.
Why OmO
ultrawork. Skip the study. If a task feels hard, add ultrawork (or ulw) to your prompt. The agent reads the codebase before touching a line, plans, proves each step before moving on, checks the result on the real surface, and stops when it's done.
Hundreds of agents, one keyword. Type mass ulw and the whole job becomes a graph of agents: deep research over thousands of sources, a full machine-learning pipeline, a migration nobody wants to do. Each part runs on the model that fits it, in parallel, and gets checked before anything is called done.
Kibitzer. Your agent doesn't forget you, or your work. Memory lives in a git repository of markdown files. A small, cheap model sits beside the expensive main agent, reads what you've done before, and nudges the main agent before it repeats a mistake. You stop re-explaining yourself.
Mix models without thinking about it. Use the frontier models you love together with the cheap ones. Just sign in: /login covers Claude, ChatGPT, Kimi and GLM subscriptions. Every model ships with a system prompt tuned for it, so you never pick the wrong one.
High-quality skills, only the ones you need. Your model is smart, but it still doesn't know a lot about solving real-world problems. OmO ships the missing knowledge as skills, browser use included, and loads only what the task needs.
Everything else is crafted. Independent tool calls run together in one small program, so twenty reads are one round trip. Malformed tool arguments are repaired before they run. Waiting on a build or a deploy is a subscription, not polling. Long work keeps a record of what was checked, so a restarted session picks up where it stopped. Config, skills and prompts reload live.
Configuration
Opinionated defaults, adjustable if you insist. Your settings live in ~/.omo/omo.jsonc, and a project can add its own .omo/omo.jsonc; the closest file wins. Every key is in the configuration reference.
Docs
Computer use (experimental)
Reviews
"It made me cancel my Cursor subscription. Unbelievable things are happening in the open source community." - Arthur Guiot
"If Claude Code does in 7 days what a human does in 3 months, OmO does it in 1 hour." - B, Quant Researcher
"You guys should pull this into core and recruit him. Seriously. It's really, really, really good." - Henning Kilset
Author's Note
When I have to take the wheel back from an agent, that isn't teamwork. The system failed. OmO is built on that idea. You say what you want; the agent researches, plans, writes code you couldn't tell from a senior engineer's, verifies it, and keeps going until it's finished. It spends tokens when they buy real speed and routes the easy parts to cheap models.
I'm this project's most obsessive user. Which model has the sharpest logic? Who writes the best prose? What did everyone else ship this week? OmO is where those answers land. Have a better way? PRs welcome.
The goal is an agent you stop noticing. You flip the switch, the light turns on. The long version is the Ultrawork Manifesto.
No affiliation with any project or model mentioned. The Ultragoal and UltraQA ideas come from oh-my-codex, reimplemented from concept for OmO.
Loved by professionals at
Lo que la gente pregunta sobre oh-my-openagent
¿Qué es code-yeongyu/oh-my-openagent?
code-yeongyu/oh-my-openagent es skills para el ecosistema de Claude AI. OmO: Just type "mass ulw" keyword with your prompt. Now you are the master of graph engineering. Tiene 70.3k estrellas en GitHub y su última actualización registrada es del 2026-10-23.
¿Cómo se instala oh-my-openagent?
Puedes instalar oh-my-openagent clonando el repositorio (https://github.com/code-yeongyu/oh-my-openagent) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar code-yeongyu/oh-my-openagent?
Nuestro agente de seguridad ha analizado code-yeongyu/oh-my-openagent y le ha asignado un Trust Score de 82/100 (tier: Trusted). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene code-yeongyu/oh-my-openagent?
code-yeongyu/oh-my-openagent es mantenido por code-yeongyu. La última actividad registrada en GitHub es del 2026-10-23, con 1735 issues abiertos.
¿Hay alternativas a oh-my-openagent?
Sí. En ClaudeWave puedes explorar skills similares en /categories/skills, ordenados por popularidad o actividad reciente.
¿Mantienes este repo? Muestra su Trust Score en tu README
La insignia muestra el Trust Score y el veredicto actuales de este repositorio y enlaza a esta página. Sigue los datos del catálogo: nadie puede comprar ni editar la puntuación que enseña.
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