Portable, vendor-agnostic agent harness for project-specific skills, workflows, and agent teams aligned with your codebase, conventions, and engineering standards.
Ever wished your AI assistant had coworkers? That's what oh-my-agent does.
Instead of one AI doing everything (and getting confused halfway through), oh-my-agent splits work across specialized agents — frontend, backend, architecture, QA, PM, DB, mobile, infra, debug, design, and more. Each one knows its domain deeply, has its own tools and checklists, and stays in its lane.
Works with all major AI IDEs: Antigravity, Claude Code, Codex, Cursor, Grok Build, Kimi Code, OpenCode, Pi, Qwen Code, and more.
Quick Start
# macOS / Linux — auto-installs bun, uv & serena if missing
curl -fsSL https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.sh | bash
# Windows (PowerShell) — auto-installs bun, uv & serena if missing
irm https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.ps1 | iex
# Or manual (any OS, requires bun + uv + serena)
bunx oh-my-agent@latest
Not to be confused with oma-observability's APM (Application Performance Monitoring).
# All skills, deployed to every detected runtime
# (.claude, .cursor, .codex, .opencode, .github, .agents)
apm install first-fluke/oh-my-agent
# A single skill
apm install first-fluke/oh-my-agent/.agents/skills/oma-frontend
APM ships skills only. For workflows, rules, oma-config.yaml, keyword-detection hooks, and the oma agent:spawn CLI, use bunx oh-my-agent@latest. Pick one distribution per project to avoid drift.
oh-my-agent keeps .agents/ as the single source of truth and projects it into each runtime's native layout, so every supported tool shares the same skills, workflows, and rules.
Drafts, revises, and audits academic prose to publication quality.
oma-architecture
Weighs architecture tradeoffs and draws module boundaries, with ADR/ATAM/CBAM analysis.
oma-backend
Builds and secures your APIs in Python, Node.js, or Rust.
oma-brainstorm
Explores ideas with you before you commit to building.
oma-db
Designs your schema, migrations, indexes, and vector stores.
oma-debug
Finds the root cause, fixes the bug, and writes a regression test.
oma-deepsec
Scans your code for security holes and blocks risky pull requests.
oma-design
Builds design systems with tokens, accessibility, and responsive layouts.
oma-dev-workflow
Automates your CI/CD, releases, and monorepo tasks.
oma-docs
Checks your docs for broken references and flags ones a code change touched.
oma-explainer
Turns a diff, PR, or branch into a self-contained interactive HTML explainer with a quiz.
oma-frontend
Builds your UI with React/Next.js, TypeScript, Tailwind CSS v4, and shadcn/ui.
oma-hwp
Converts HWP, HWPX, and HWPML files to Markdown.
oma-image
Generates images through several AI providers at once.
oma-market
Researches your market from community signals and frames it with SWOT, 5F, and PESTEL.
oma-mobile
Builds cross-platform mobile apps with Flutter.
oma-observability
Routes observability work across metrics, logs, traces, SLOs, and incident forensics.
oma-orchestrator
Runs multiple agents in parallel from the CLI.
oma-pdf
Converts PDF files to Markdown.
oma-pm
Plans tasks, breaks down requirements, and defines API contracts.
oma-qa
Reviews your code for OWASP security, performance, and accessibility issues.
oma-recap
Recaps your conversation history into themed work summaries.
oma-refactor
Refactors code without changing its behavior, using hotspot targeting, characterization-test safety nets, and refactor-only commits.
oma-scholar
Searches academic literature and helps you run peer review.
oma-scm
Manages your branches, merges, worktrees, and Conventional Commits.
oma-search
Routes each query to the best source and scores how much you can trust the result.
oma-slide
Generates distinctive, animation-rich HTML presentation decks and exports to PDF/PNG/PPTX.
oma-tf-infra
Provisions multi-cloud infrastructure with Terraform.
oma-translator
Translates between languages so it reads like a native wrote it.
oma-video
Generates short-form, explainer, and demo videos through a key-optional Remotion pipeline.
oma-voice
Generates voiceovers and transcribes audio on-device, no cloud needed.
Internal & meta tools
Agent
What They Do
oma-coordination
Guides manual step-by-step coordination of PM, frontend, backend, mobile, and QA agents.
oma-skill-creator
Writes and audits new OMA skills in the SSL-lite format.
How It Works
Just chat. Describe what you want and oh-my-agent figures out which agents to use.
You: "Build a TODO app with user authentication"
→ PM plans the work
→ Backend builds auth API
→ Frontend builds React UI
→ DB designs schema
→ QA reviews everything
→ Done: coordinated, reviewed code
Or use slash commands for structured workflows:
Step
Command
What It Does
0
/deepinit
Maps your existing codebase into AGENTS.md, ARCHITECTURE.md, and docs
1
/brainstorm
Explores ideas with you before you commit to building
2
/architecture
Weighs your design tradeoffs and draws clean module boundaries
2
/design
Builds your design system with tokens, accessibility, and responsive layouts
2
/plan
Breaks your feature down into prioritized tasks
3
/work
Builds your feature step by step across multiple agents
3
/orchestrate
Runs multiple agents in parallel to build your feature faster
3
/ultrawork
Builds your feature through five gated quality phases; every review runs in a fresh, isolated reviewer session (cross-context review)
3
/ralph
Repeats /ultrawork until an independent verifier passes every criterion
4
/review
Reviews your code for security, performance, and accessibility issues
4
/deepsec
Runs a deep security scan and blocks risky pull requests
5
/debug
Finds the root cause, fixes the bug, and writes a regression test
5
/docs
Checks your docs for broken references and patches the ones your code changes touched
6
/scm
Manages your branches, merges, and Conventional Commits
-
/schedule
Schedules an agent job to run on a recurring interval
Auto-detection: You don't even need slash commands — keywords like "architecture", "plan", "review", and "debug" in your message (in 11 languages!) auto-activate the right workflow. Detection accuracy is measured, not assumed: oma verify triggers scores the detector against a labeled 171-prompt corpus (currently 0% missed-fire, under 10% false-fire) and gates CI on it.
Per-Agent Models
Set model_preset in .agents/oma-config.yaml to choose which AI models each agent uses:
language: en
model_preset: mixed # antigravity | claude | codex | cursor | kiro | mixed | qwen
# Optional per-agent overrides
agents:
backend: { model: openai/gpt-5.5, effort: high }
oma doctor --profile — prints the per-role resolved model matrix
Portable — .agents/ travels with your project, not trapped in one IDE. oma emit projects the same SSOT into open-standard artifacts — Agent Skills-conformant skill folders, a .claude-plugin/marketplace.json, and AGENTS.md — so oma skills work in any tool that reads the open spec, with a drift check in CI keeping the generated output honest
Role-based — Agents modeled like a real engineering team, not a pile of prompts
Token-efficient — Two-layer skill design saves ~75% of tokens (how it works)
Quality-first — Charter preflight, quality gates, and review workflows built in:
oma verify <agent> — a deterministic check battery per agent type: a shared core (scope violation, charter alignment, hardcoded secrets, TODO scan, declared outputs) plus type-specific checks (TypeScript strict, tests, raw SQL, Flutter analyze, inline styles, …)
session.quota_cap — per-session token / spawn / per-vendor budget caps in oma-config.yaml; orchestrate Step 5 blocks the next spawn when exceeded
ralph workflow — independent JUDGE re-verifies every criterion each iteration to catch silent regressions; heavy-test caching for >30s suites
Exploration Loop — after 2 retries, orchestrate spawns hypothesis variants in parallel and keeps the highest-scoring result
Monorepo auto-routing — detectWorkspace reads pnpm / nx / turbo / lerna and routes each agent to its workspace
Multi-vendor — Mix Antigravity, Claude, Codex, Cursor, Kiro, and Qwen per agent type
Observable — Terminal and web dashboards for real-time monitoring
Architecture
flowchart TD
subgraph Workflows["Workflows"]
direction TB
W0["/brainstorm"]
W1["/work"]
W1b["/ultrawork"]
W2["/orchestrate"]
W3["/architecture"]
W4["/plan"]
W5["/review"]
W6["/debug"]
W7["/deepinit"]
W8["/design"]
end
subgraph Orchestration["Orchestration"]
direction TB
PM[oma-pm]
ORC[oma-orchestrator]
end
subgraph Domain["Domain Agents"]
direction TB
ARC[oma-architecture]
FE[oma-frontend]
BE[oma-backend]
DB[oma-db]
MB[oma-mobile]
DES[oma-design]
TF[oma-tf-infra]
end
subgraph Quality["Quality"]
direction TB
QA[oma-qa]
DBG[oma-debug]
end
Workflows --> Orchestration
Orchestration --> Domain
Domain --> Quality
Quality --> SCM([oma-scm])
Li, X., Liu, Y., Chen, W., You, B., Di, Z., He, Y., Zheng, S., Choe, K. W., Sun, J., Wang, S., Tao, C., Li, B., Zhao, X., Geng, H., Wu, X., Zhou, J., Chen, X., Xing, H., Li, Y., … Song, D. (2026). SkillsBench: Benchmarking how well agent skills work across diverse tasks (Version 4) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2602.12670
Liang, Q., Wang, H., Liang, Z., & Liu, Y. (2026). From skill text to skill structure: The scheduling-structural-logical representation for agent skills (Version 4) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.24026
Chen, C., Yu, Q., Gu, Y., Huang, Z., Li, H., Liu, H., Liu, S., Liu, J., Peng, D., Wang, J., Yan, Z., Meng, F., Qin, E., Che, C., & Hu, M. (2026). The scaling laws of skills in LLM agent systems (Version 1) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.16508
Yang, Y., Gong, Z., Huang, W., Yang, Q., Zhou, Z., Huang, Z., Li, Y., Gao, X., Dai, Q., Liu, B., Qiu, K., Yang, Y., Chen, D., Yang, X., & Luo, C. (2026). SkillOpt: Executive strategy for self-evolving agent skills (Version 2) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.23904
Huang, Z., Xu, J., Yang, Y., Gong, Z., Yang, Q., Tian, M., Wang, X., Lv, C., Gao, X., Dai, Q., Liu, B., Qiu, K., Yang, X., Chen, D., Zheng, X., & Luo, C. (2026). From raw experience to skill consumption: A systematic study of model-generated agent skills [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.23899
Hong, D. B., Imani, A., & Ahmed, I. (2026). From anatomy to smells: An empirical study of SKILL.md in agent skills (Version 2) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.01456
