oh-my-hermes is an operating layer for Hermes Agent that organizes requests into workflows for planning, research, creation, coding handoffs, operations, and project memory. Hermes users run these workflows through the desktop app, CLI, or messenger app, while the catalogue add-ons extend its native capabilities.
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 rlaope/oh-my-hermes --skill ulw-qagit clone --depth 1 https://github.com/rlaope/oh-my-hermesWrote 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/rlaope/oh-my-hermes/ulw-qa)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/ulw-qa"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-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/rlaope/oh-my-hermes/ulw-qa"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-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 | $0.00057 | $0.01766 |
| Opus 5.5 | $0.00023 | $0.00706 |
| Sonnet 5.5 | $0.00011 | $0.00353 |
| Haiku 4.5 | $0.00006 | $0.00177 |
Grade A, and why
ulw-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 3d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ultraqa
This is an OMH ultraqa workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
ultraqa exists because behavior that passes its own tests can still fail under inputs and conditions nobody wrote down: it turns the changed behavior and known risks into hostile scenarios, records each one's observed pass or fail apart from any proposed fix, and hands fixes to the selected coding executor.
Do Not Use When
- Judging whether a rendered page, image, or terminal frame looks right across viewports and states; use
omh-visual-qa. - Confirming that a finished change meets its acceptance criteria with the smallest proving check, when no hostile scenarios are wanted; use
omh-verification-gate.
Examples
Good example:
- Prompt: $ultraqa test the setup wizard with hostile install paths, stale config, and missing PATH cases.
- Expected behavior: Generate adversarial QA scenarios, expected signals, observed results, and fix-or-retry routing.
- Why: The request asks for verification pressure and hostile scenarios.
Bad example:
- Prompt: ultraqa: treat casual chat or unaccepted work as if this workflow already produced verified results.
- Expected behavior: Ask a clarification question or route to a narrower workflow instead of forcing
ultraqa. - Why: The request lacks the required inputs or would overclaim work that Hermes did not observe.
Completion Checklist
- The scenario, expected behavior, observed result, and pass/fail basis are named.
- Proposed fixes are separated from observed QA evidence.
- Missing or failed verification routes back to plan, fix, or a narrower test.
Recovery Notes
- If the expected behavior is unclear, route back to plan before running adversarial checks.
- If verification fails, return to fix or research with the failed signal instead of advancing.
When to Use
Use when the task needs adversarial test scenarios, verification, and fix loops.
Strong routing signals: `ultraqa`, `$ultraqa`, `adversarial qa`, `hostile scenarios`, `e2e qa`, `real-world qa`, `qa scenario`, `release qa`, `敵対的QA`, `リリース前QA`, `障害シナリオ`, `장애 상황`, `쿠버네티스 장애`, `적절히 진단`, `검증 체크리스트`, `릴리즈 전 gate`, `对抗式测试`, `发布前测试`, `故障场景`, `uji skenario ekstrem`, `lakukan QA adversarial`, `uji skenario kegagalan sebelum rilis`, `bantu cari celah lewat pengujian end to end`, `uji aplikasi dengan kondisi pengguna nyata`, `jalankan siklus pengujian dan perbaikan berulang`, `kiểm thử các tình huống cực đoan`, `tiến hành QA phản biện`, `kiểm thử các tình huống thất bại trước khi phát hành`, `giúp tìm lỗ hổng qua kiểm thử end to end`, `kiểm thử ứng dụng với điều kiện người dùng thực tế`, `chạy các chu kỳ kiểm thử và sửa lỗi lặp lại`
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.
- 3d ago Changed · +1 lines 2ac2031ad8ac
- 4d ago Changed 2f08224f1532
- 6d ago Changed 4bc8e879aec5
- 12d ago Changed · +3 lines 3e9bcd0ed171
- 25d ago Changed · +3 lines 11049ec3a49f
- 1mo ago Changed 814b17457cb3
- 1mo ago Changed · +6 lines 1a384bbfcfbb
- 1mo ago Changed · -11 lines 6a9649053365
- 1mo ago Changed · +2 lines ded6ba8d7de9
- 1mo ago First seen · 122 lines · 57 tokens per session scan A 52735fdcc25b
ulw-qa is a skill published in the GitHub repository rlaope/oh-my-hermes (4,206 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,766 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
reproduce-bug
Reproduce a reported bug in googleapis/mcp-toolbox and decide whether it is real, delivering an evidence-backed verdict: confirmed, already fixed, misconfiguration, client-side, works as intended, not reproducible, or blocked. Use whenever a maintainer asks you to reproduce, verify, confirm, or investigate a bug…
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
verification
Prove that a coding task is actually complete. Use this after meaningful code changes, when tests/builds fail or are skipped, before marking a plan or goal complete, and whenever acceptance depends on runtime, security, recovery, performance, or cross-module evidence.
ai-discover
Read-only discovery step of the auto-improvement loop. One bounded agent turn per cycle reads the focus files (Read/Grep/Glob only, no shell, no subagents) and replies with a JSON array of testable surfaces {file, line, symbol, rule, message, hypothesis}; the spine authors fixes and verifies them. Discovery only — no…
evidence-audit
Evidence-led audit/fix rounds for any codebase in any language. Finds only defects that a runnable proof reproduces on current code, applies the narrowest in-scope patch, verifies with a second proof, promotes high-risk proofs to permanent regression tests, and reports exact validation results. Use whenever the user…