oh-my-agent is a harness for checking whether coding agents actually completed their work by verifying tests, required artifacts, independent reviews, and recorded decisions. It is used across multiple agent runtimes to make workflow results auditable instead of relying on an agent's own report. The catalogue add-ons provide parts of its skills, agents, hooks, MCP integrations, instructions, and plugins.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add first-fluke/oh-my-agent --skill oma-refactorgit clone --depth 1 https://github.com/first-fluke/oh-my-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-refactor)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-refactor"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-refactor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-refactor"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-refactor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 102 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 122 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
- medium MCP Rug Pull · line 122 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.03025 |
| Opus 5.5 | $0.00010 | $0.01210 |
| Sonnet 5.5 | $0.00005 | $0.00605 |
| Haiku 4.5 | $0.00003 | $0.00302 |
Grade A, and why
oma-refactor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- oma-refactor — 100% identical, 21 lines differ
- oma-refactor — 92% identical, 54 lines differ
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Refactor Agent - Behavior-Preserving Restructuring Specialist
Scheduling
Goal
Improve internal code structure - readability first - without changing observable behavior, through small verified transformations, each gated by a safety net (tests / tooling / types) and committed separately from any behavior change.
Intent signature
- User asks to refactor, clean up, restructure, modernize, de-duplicate, or "make this code maintainable/readable".
- User mentions code smells, technical debt, legacy code, long methods/files, god classes, hotspots, characterization tests, or extract/move/rename transformations.
- User asks "where should we refactor first?" or wants a refactoring plan/priority for a codebase.
When to use
- Executing a refactoring on specific files/modules (extract, move, rename, decompose, pattern/idiom alignment)
- Preparatory refactoring before a feature ("make the change easy, then make the easy change")
- Legacy (brownfield) rescue: seam discovery + characterization tests, then restructuring
- Refactoring target selection and prioritization (smells + SATD + hotspot = churn x complexity)
- Auditing whether code is safe to refactor now (coverage breadth x mutation strength x flakiness)
When NOT to use
- Adding or completing behavior already requested by the user -> use the appropriate existing implementation skill; classify the scope under Entry first.
- Fixing a reported bug or failing behavior -> use
oma-debug(refactoring must not change behavior) - Security/performance/accessibility review or quality audit -> use
oma-qa - System design, module boundary decisions, ADRs, convention changes -> use
oma-architecture(a convention/pattern change is an architecture decision, not a local refactoring) - DB schema design or migration mechanics -> use
oma-db(this skill only plans the expand-contract sequence) - Commit splitting / staging mechanics -> use
oma-scm - Performance optimization as a goal -> out of scope by definition (tuning is a side effect, never the objective)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +11 lines 74a8244d3935
- 17d ago Changed · +2 lines 20882075b0cf
- 20d ago Changed · -1 lines 056d33938e2f
- 1mo ago Changed · +1 lines 14564a8381cb
- 1mo ago Changed · -5 lines · -46 tokens per session feac82492b2a
- 1mo ago Changed · +6 lines 7b23ae54f9e5
- 1mo ago First seen · 170 lines · 71 tokens per session scan A cb6b7cee28ca
oma-refactor is a skill published in the GitHub repository first-fluke/oh-my-agent (1,354 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 3,025 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
code-review
name: code-review description: Used when the user wants to review code, check quality, verify feature completeness, or needs to validate code implementation against Spec, DESIGN.md, and design mockups. Outputs a structured review report with evidence for each conclusion. version: 2.3.0 updated: 2026-07-16 requires: [].
nobrainer-review
Use when the owner says nb-review, deep-audit, deep-code-review, or deep-autoreview; explicitly requests an evidence-gated CLOSEOUT, adversarial BUGHUNT or RELEASEGATE; or needs final findings filtered to verified actionable defects. Use nobrainer-build for ordinary implementation and correction work.
code-review
Use when the user asks to review a pull request or diff with a structured Codex CLI workflow using parallel review passes and filtered actionable findings.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.
agent-harness-fault-injection
Use when an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.