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-interviewgit 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-interview)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/ulw-interview"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-interview/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-interview"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/ulw-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 24 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.
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 | $0.00059 | $0.02186 |
| Opus 5 | $0.00030 | $0.01093 |
| Sonnet 5 | $0.00012 | $0.00437 |
| Haiku 4.5 | $0.00006 | $0.00219 |
Grade A, and why
ulw-interview 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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Interview
This is a Hermes-native deep-interview workflow skill.
Why This Exists
deep-interview exists to stop Hermes from guessing through ambiguous product, workflow, or implementation intent; it converts uncertainty into a clarified brief before planning or handoff.
Do Not Use When
- The request already has concrete scope, acceptance criteria, and verification commands.
- The missing information is discoverable from the repository or local artifacts without asking the user.
- The user asked for immediate read-only analysis and the ambiguity does not change the answer.
- The ambiguity is specifically repository terminology or project-language alignment; use
contextand its direct-lookup/frontier boundary. - The open question is answerable by a small reversible experiment rather than another interview round; use
decision-prototype.
Examples
Good example:
- Prompt: $deep-interview before planning Discord and Slack routing, ask what each channel owns and what evidence counts.
- Expected behavior: Ask one decision-changing question at a time, then produce goals, non-goals, and acceptance criteria.
- Why: The request explicitly rejects assumptions and needs product boundaries before implementation.
Bad example:
- Prompt: $deep-interview fix this failing test; the traceback and expected behavior are attached.
- Expected behavior: Proceed to diagnosis or implementation instead of interviewing.
- Why: The required facts are already available, so more questions would slow the workflow.
Completion Checklist
- The clarified brief names goals, non-goals, constraints, and one next planning or handoff path.
- Remaining ambiguity is listed only when it changes the plan, risk, or stop condition.
- No implementation handoff is prepared until the blocking decision is resolved.
Recovery Notes
- If an answer surfaces new ambiguity, file it under one of the three clarity dimensions and keep asking only while the round budget allows; once round 6 is reached, record the rest as assumptions and plan.
- If repo evidence can answer the question, inspect it before asking the user.
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 · +1 lines c3a9d80a041a
- 7d ago First seen · 193 lines · 59 tokens per session scan A 4eb7a2cdf48b
ulw-interview is a skill published in the GitHub repository rlaope/oh-my-hermes (1,648 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 2,186 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.
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