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 omh-tech-debt-auditgit 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/omh-tech-debt-audit)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-tech-debt-audit"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-tech-debt-audit/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/omh-tech-debt-audit"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-tech-debt-audit.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 | $0.00103 | $0.01755 |
| Opus 5 | $0.00051 | $0.00877 |
| Sonnet 5 | $0.00021 | $0.00351 |
| Haiku 4.5 | $0.00010 | $0.00176 |
Grade A, and why
omh-tech-debt-audit 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 9d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Debt Audit
This is a Hermes-native tech-debt-audit workflow skill.
Why This Exists
tech-debt-audit exists so accumulated debt becomes a ranked, reconcilable ledger instead of a one-off complaint: findings cite file:line, severity and effort make the trade-off explicit, quick wins are separated from big fixes, and reruns mark what was resolved instead of rediscovering it.
Do Not Use When
- The target is one diff, PR, or claim rather than the codebase's accumulated state; use
code-review. - The user wants the debt removed now, behavior preserved; use
ai-slop-cleanerfor deletion-first cleanup. - A boundary-changing fix from the ledger needs its execution shaped into phases; use
refactor-plan. - The question is release risk for a specific deploy rather than source quality; use
production-audit.
Examples
Good example:
- Prompt: Audit our tech debt and tell me what to fix first - we have maybe two weeks of cleanup budget.
- Expected behavior: Orientation from manifests and churn, dimension-by-dimension findings with file:line citations, the severity-by-effort ledger with top fixes and quick wins sized to the budget, and the looks-bad-but-fine list.
- Why: A budgeted what-to-fix-first question is exactly the ranked ledger this workflow produces.
Bad example:
- Prompt: This module is a mess, rewrite it properly.
- Expected behavior: Refuse the rewrite framing: audit the module into ledger findings with bounded fixes, or route a decided restructure to
refactor-plan. - Why: A rewrite recommendation is the failure mode the ledger exists to replace with bounded, ranked fixes.
Completion Checklist
- Orientation evidence is observed: manifests, churn ranking, and largest files are named, not assumed.
- Every finding has id, dimension, file:line, severity, effort, and a bounded recommendation.
- Quick wins and top fixes are ranked, and the looks-bad-but-is-actually-fine section is present.
- On rerun, every prior finding is reconciled RESOLVED, CARRIED, or superseded - none silently dropped.
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.
- 9d ago First seen · 128 lines · 103 tokens per session scan A c8c05aacd2be
omh-tech-debt-audit is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 1,755 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.
Other skills, from other repositories
story-long-analyze
A structured process for deeply analysing a long online novel, starting with its opening three chapters and continuing chapter by chapter.
story-review
A review process for finding problems in a novel’s structure, characters, wording, and world rules. It can use several reviewers or one reviewer when others are unavailable.
moxiangtongxiu-perspective
A Chinese-language creative-writing guide built around character-led stories, interwoven plotlines, memorable dialogue, ensemble casts, and emotional contrasts. It is presented as a perspective associated with the author Mo Xiang Tong Xiu.
tiancantudou-perspective
A creative-writing guide based on the storytelling patterns associated with Chinese web novelist Tiancan Tudou. It focuses on stories where an underestimated character grows stronger through challenges and moves into new settings.
tianya-gods-team
A decision-making system in which 20 fictional specialist viewpoints analyze one question in parallel before a coordinating AI combines them. It covers areas such as history, economics, relationships, technology, mysteries, and culture.
lijigang-skill
A Chinese-language approach to writing precise, highly structured prompts, sometimes using Lisp-like notation. It combines concise wording, philosophical questioning, and a process for defining roles, conditions, output formats, and revisions.