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-researchgit 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-research)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/ulw-research"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-research/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-research"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-research.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 | $0.00102 | $0.03084 |
| Opus 5 | $0.00051 | $0.01542 |
| Sonnet 5 | $0.00020 | $0.00617 |
| Haiku 4.5 | $0.00010 | $0.00308 |
Grade A, and why
ulw-research 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 today.
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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
This is a Hermes-native research workflow skill.
Why This Exists
research exists to make Hermes a careful research engine: it routes research demands to source-backed evidence gathering - from live web citations to studied reference implementations - verifies contested claims, and distills decision-grounding output so planning starts from evidence instead of guesses.
Do Not Use When
- The user asks for a full plan-to-PR delivery cycle; use
ultrawork(itsdelivery_boundarycapability) or a planning workflow after research instead. - The request is purely local repo inspection with no external, current, citation, or source-comparison need.
- The study target is this repository itself rather than external references; use
codebase-onboarding. - The user needs coding execution, review, CI, or merge evidence rather than research synthesis.
- The requested output is a typed candidate list or acquisition status without factual synthesis; use
source-finder. - The user needs a market, customer, or pricing decision brief with evidence-versus-inference treatment; use
research-brief. - The user asks for recurring monitoring, a source inbox, or Scout/Analyst/Briefer operations; use
research-department. - Correctness is a bounded, versioned official or upstream guidance question; use
best-practice-research. - One cited retrieval round settles the question and no reference implementation needs reading; use
web-research.
Examples
Good example:
- Prompt: 딥리서치로 다른 오픈소스 구현들을 깊게 보고 스펙 잡기 전에 근거를 만들어줘.
- Expected behavior: Run the Hermes research lane at depth: decompose axes, study the most relevant reference implementations with pinned refs, verify contested claims, then distill a decision-grounding dossier for the planning step.
- Why: The user explicitly asked for deep pre-spec grounding built on other open-source implementations.
Bad example:
- Prompt: 이 레포 코드 구조만 파악해줘.
- Expected behavior: Route to
codebase-onboardingbecause the study target is this repository, not external sources or reference implementations. - Why: Local repo orientation needs no external evidence gathering or claim verification.
What ships with it
1 file 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.
- today Changed · +2 lines 95186ac5e6ec
- 4d ago Changed · +5 lines 0466b5b3905a
- 6d ago Changed 7187b5d98ab8
- 7d ago Changed c3aac109540d
- 9d ago First seen · 169 lines · 102 tokens per session scan A 4e9a4e466be4
ulw-research is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 3,084 once invoked, about $0.0005 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.
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