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-qagit 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-qa)<a href="https://agentmods.dev/skills/first-fluke/oh-my-agent/oma-qa"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-qa/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-qa"><img src="https://agentmods.dev/badge/skills/first-fluke/oh-my-agent/oma-qa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.01382 |
| Opus 5.5 | $0.00012 | $0.00553 |
| Sonnet 5.5 | $0.00006 | $0.00276 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
oma-qa 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Agent - Quality Assurance Specialist
Scheduling
Goal
Review and verify software quality with priority on security, performance, accessibility, correctness, test coverage, and standards-aligned quality evidence.
Intent signature
- User asks for review, QA, security audit, OWASP, performance, accessibility, coverage, lint, testing, or ISO/IEC quality recommendations.
- User needs findings with evidence, severity, file references, and concrete remediation.
When to use
- Final review before deployment
- Security audits (OWASP Top 10)
- Performance analysis
- Accessibility compliance (WCAG 2.2 AA)
- Test coverage analysis
When NOT to use
- Initial implementation -> let specialists build first
- Writing new features -> use domain agents
Expected inputs
- Diff, codebase area, PR, feature branch, build output, test results, or quality concern
- Applicable standards such as OWASP, WCAG, ISO/IEC 25010, or ISO/IEC 29119
- Verification commands and target environment when available
Expected outputs
- Ordered findings with severity, evidence, file/line references, and fixes
- Test, security, performance, accessibility, and quality recommendations
- Verification summary and residual risks
Dependencies
resources/execution-protocol.md, ISO guide, and checklist- Automated tools such as
npm audit,bandit,lighthouse, linters, tests, and coverage tools when applicable
Control-flow features
- Branches by review type, available diff, quality dimension, and tool availability
- Reads code and reports; may run tools; generally should not implement broad feature work
- Findings must be reproducible and prioritized
Structural Flow
Entry
- Identify review scope and quality dimensions.
- Collect diff, files, commands, and standards context.
- Choose automated checks before manual review where practical.
Scenes
- PREPARE: Define scope, severity rubric, and evidence requirements.
- ACQUIRE: Read diff/code and run relevant automated tools.
- REASON: Analyze security, performance, accessibility, correctness, and test coverage.
- VERIFY: Reproduce findings and reject false positives.
- FINALIZE: Report findings, remediation, test gaps, and residual risk.
What ships with it
5 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.
- 1mo ago Changed · -3 lines · -27 tokens per session 2adef418ac5d
- 1mo ago Changed · -1 lines f57236364f98
- 1mo ago Changed · +1 lines 26619b023e54
- 1mo ago First seen · 142 lines · 58 tokens per session scan A cb08c82ea8c2
oma-qa is a skill published in the GitHub repository first-fluke/oh-my-agent (1,354 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,382 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.
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