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 | $0.01297 |
| Opus 5 | $0.00028 | $0.00648 |
| Sonnet 5 | $0.00011 | $0.00259 |
| Haiku 4.5 | $0.00006 | $0.00130 |
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 5d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ultraqa
This is a Hermes-native ultraqa workflow skill.
Why This Exists
ultraqa exists to keep verification work explicit, evidence-backed, and inside the Hermes/executor boundary instead of relying on ad hoc chat narration.
Do Not Use When
- The request is casual chat, a status-only acknowledgement, or another workflow has stronger routing evidence.
- The user needs implementation, review, CI, merge, or external publishing evidence that has not been delegated or observed.
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.
Workflow Lane
- Current lane: Coding handoff (
idea-to-deploy,llm-app-dev,cto-loop,deploy-and-monitor,code-review,build-failure-triage,verification-gate,security-safety-review,+13 more) - coding owners, handoffs, review, CI, and merge evidence. - If intent belongs to another lane, hand back to
oh-my-hermesor name the adjacent workflow. - Shared product, routing, compatibility, and evidence rules:
omh-routing/references/skill-common-rail.md.
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.
- 5d ago Changed 52735fdcc25b
- 7d ago First seen · 122 lines · 57 tokens per session scan A bd51c95f9b9b
ulw-qa is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,297 once invoked, about $0.0003 per session on Opus 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
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…
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…
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
Parallel discovery of performance hotspots (perf track) and failure surfaces (bug track) for the auto-improvement loop. Fans out one subagent per hot-path area or failure surface; each returns ONE concrete, behavior-preserving fix candidate (perf) or a reproducing test plus fix (bug). Discovery only — no code changes…
03-assert
Assert the work behaves by iterating the project's coding assertions until they pass, plus optional architecture and frontend facets. Use to validate an implementation. Not for reviewing or writing tests.