Dev & Engineering multi-agent-orchestrationagent-skillscode-reviewworkflow-automationcodex-cliserena

oh-my-agent

A portable multi-agent harness that keeps project skills, workflows, and engineering roles consistent across AI coding runtimes.

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What does this agent do, and when should you use it?

oh-my-agent is a project-scoped multi-agent harness that treats `.agents/` as the single source of truth for skills, workflows, and rules. It ships role-oriented agents for architecture, backend, frontend, databases, QA, security, infrastructure, project management, and other specialist work. Its slash-command workflows include `/plan`, `/work`, `/orchestrate`, `/review`, and `/debug`, while keyword detection can activate workflows from ordinary chat. `oma emit` projects the shared source into Agent Skills-conformant folders, `.claude-plugin/marketplace.json`, and `AGENTS.md`, alongside runtime-native or adapter layouts. The documented quality model includes `oma verify <agent>`, session quota caps, and the iterative `ralph` workflow, making it a fit for teams that want reusable engineering guidance to travel with a repository across supported coding tools.

oh-my-agent keeps skills, workflows, and rules in .agents/, then projects that source into detected runtime layouts. Users can invoke /deepinit, /brainstorm, /architecture, /plan, /work, /orchestrate, /ultrawork, /review, /debug, /docs, and /scm; the workflows coordinate roles such as oma-pm, oma-orchestrator, oma-backend, oma-frontend, oma-db, and oma-qa. oma emit produces Agent Skills-conformant skill folders, .claude-plugin/marketplace.json, and AGENTS.md; oma doctor --profile prints the resolved per-role model matrix; and oma verify <agent> runs shared and agent-type-specific checks. In .agents/oma-config.yaml, model_preset selects a model configuration and agents can override an individual role, for example backend: { model: openai/gpt-5.5, effort: high }.

  1. A full-stack team using Claude Code, Codex CLI, or Cursor that wants frontend, backend, database, and QA guidance maintained once in `.agents/`.
  2. An engineering lead planning a feature who needs `/architecture` to assess module boundaries before using `/plan` to create prioritized tasks.
  3. A team implementing independent workstreams that wants `/orchestrate` to run multiple agents while enforcing documented session quota limits.
  4. Maintainers of an existing repository who want `/deepinit` to map the codebase, then `/review`, `/debug`, and `/docs` for quality, regressions, and documentation work.
  5. A monorepo team using pnpm, Nx, Turbo, or Lerna that needs agents routed to the appropriate workspace.

What are this agent's strengths and limitations?

Pros
  • Uses `.agents/` as a single source of truth and `oma emit` to produce open-standard skill folders, a Claude plugin marketplace manifest, and `AGENTS.md`.
  • Provides named specialist roles across architecture, implementation, databases, mobile, QA, security, observability, infrastructure, and project management, with matching workflows.
  • Documents concrete control mechanisms: `oma verify <agent>`, shared and type-specific checks, `session.quota_cap`, independent JUDGE verification in `ralph`, and hypothesis exploration after repeated retries.
  • Explicitly supports several coding runtimes, including Codex CLI and Claude Code, and allows role-level model selection in configuration.
Limitations
  • It requires Node.js 26+; manual setup also requires bun, uv, and serena, which can add environment setup work.
  • The multi-runtime approach uses generated native or adapter layouts, and the repository advises choosing either APM or CLI distribution per project to avoid drift.
  • The number of agents, workflows, presets, and quality controls creates upfront adoption choices around scope, configuration, and budget caps.
  • The supplied material does not document direct integration paths for ChatGPT, the OpenAI API, or the Claude API.

How do you install or deploy this agent?

Node.js 26+ is required. On macOS or Linux, run:
curl -fsSL https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.sh | bash
The script auto-installs bun, uv, and serena when missing. On Windows PowerShell, run:
irm https://raw.githubusercontent.com/first-fluke/oh-my-agent/main/cli/install.ps1 | iex
With bun, uv, and serena already available, run:
bunx oh-my-agent@latest
Microsoft APM can install all skills with apm install first-fluke/oh-my-agent, but APM distributes skills only; it does not include workflows, rules, oma-config.yaml, keyword-detection hooks, or the oma agent:spawn CLI.

How do you use this agent?

After installation, maintain shared project content in .agents/. Start with /deepinit to map an existing codebase, then use /plan followed by /work or /orchestrate for implementation. Run oma verify <agent> for that agent type's deterministic checks, and run oma doctor --profile to inspect the resolved role-to-model matrix. Configure .agents/oma-config.yaml with a preset such as model_preset: mixed, then add per-role model and effort overrides under agents when needed.

How does this agent compare with similar options?

Compared with installing through Microsoft's APM, bunx oh-my-agent@latest includes workflows, rules, oma-config.yaml, keyword-detection hooks, and the oma agent:spawn CLI; APM distributes skills only. The project names Claude Code, Codex CLI, Cursor, OpenCode, and Qwen Code among runtimes that can share the .agents/ source rather than requiring one IDE.

FAQ

Am I locked into one model provider or IDE?
No. The repository describes a multi-vendor setup and explicitly names Claude Code, Codex CLI, Cursor, Kiro, and Qwen among supported runtimes. Model presets and per-agent overrides are configured in `.agents/oma-config.yaml`.
Can I install it through both APM and the CLI?
The repository advises selecting one distribution per project to avoid drift. APM installs skills only, while the CLI path also provides workflows, rules, configuration, hooks, and `oma agent:spawn`.
How are parallel-agent costs or usage constrained?
The documented `session.quota_cap` in `oma-config.yaml` can cap per-session tokens, spawns, and per-vendor budgets; `orchestrate` blocks the next spawn when a cap is exceeded.
Does it guarantee that review findings are fixed?
It provides `/review`, `/deepsec`, `oma-qa`, and `oma verify <agent>` workflows and checks. The supplied material does not guarantee that every issue in a repository will be found or corrected automatically.

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