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-long-document-readinggit 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-long-document-reading)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-long-document-reading"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-long-document-reading/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-long-document-reading"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-long-document-reading.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00081 | $0.03628 |
| Opus 5.5 | $0.00032 | $0.01451 |
| Sonnet 5.5 | $0.00016 | $0.00726 |
| Haiku 4.5 | $0.00008 | $0.00363 |
Grade A, and why
omh-long-document-reading 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long Document Reading
This is a Hermes-native long-document-reading workflow skill.
Why This Exists
long-document-reading exists because a 300-page PDF is about 500,000 characters and Hermes' read_file returns 100,000 per call with no page numbers, re-converting the whole file each time; five unanchored reads then sit in the conversation until the ratio-based compressor summarizes them without a page number, so without a ledger the session either truncates, loses the early ranges to compaction, or claims a summary of pages it never read.
Do Not Use When
- The document is a research paper and the user wants it explained by level; use
omh-paper-learning. - The request asks to convert, export, split into a new file, compare two PDFs, or extract tables into CSV; use
omh-materials-package. - The input is an image, screenshot, receipt, audio, or video rather than a document; use
omh-media-input. - The user is still looking for the document or its download link; use
omh-source-finder. - The document fits one read (under about 60 pages of prose); read it directly and answer.
Examples
Good example:
- Prompt: summarize this 300-page vendor contract pdf and list every obligation with a deadline
- Expected behavior: Prepare long_document_card/v1: record the page count and scanned flags, plan five 60-page ranges, delegate them with the per-range brief, merge obligations with page anchors, and close with covered / next / missing.
- Why: The document is far past one read budget and the goal needs page-anchored claims from every range.
Bad example:
- Prompt: turn this 300-page pdf into a slide deck
- Expected behavior: Route to
materials-package: the user wants a produced file, not a page-anchored reading of the document; the page count alone does not make it a reading request. - Why: Reading and producing are different lanes; a deck request is file output work.
Completion Checklist
- The page count is observed or the card says it is not.
- Every ledger range is covered, or the missing ranges are listed with a reason.
- Every claim in the merged answer carries a page anchor.
- Scanned ranges are read, declined with a reason, or listed as missing.
- Not-observed boundaries remain visible: page_count, text_extraction, scanned_page_ocr, range_delegation, hosted_ocr, cross_range_consistency.
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.
- 2d ago Changed · +3 lines 95c821b52fdd
- 15d ago Changed 879dce22c576
- 17d ago Changed c2b54677fca8
- 20d ago Changed · +4 tokens per session b17b40ef1812
- 22d ago Changed · +1 lines ea2036e92fe1
- 26d ago Changed e2c8b5adcb87
- 1mo ago First seen · 158 lines · 77 tokens per session scan A 7a1f16859cbe
omh-long-document-reading is a skill published in the GitHub repository rlaope/oh-my-hermes (3,383 stars, last pushed today), licensed MIT. It adds 81 tokens to every session and 3,628 once invoked, about $0.0003 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 2026-09-15.
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