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-data-analysisgit 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-data-analysis)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-data-analysis"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-data-analysis/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-data-analysis"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-data-analysis.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 | $0.01418 |
| Opus 5 | $0.00028 | $0.00709 |
| Sonnet 5 | $0.00011 | $0.00284 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
omh-data-analysis 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis
This is a Hermes-native data-analysis workflow skill.
Why This Exists
data-analysis exists so Hermes users can ask for this workflow in chat and receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.
Do Not Use When
- The request is already handled by a narrower explicit skill with stronger evidence.
- The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
- The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.
Examples
Good example:
- Prompt: data-analysis analyze this CSV and summarize anomalies by segment.
- Expected behavior: Produce
prepare_data_analysis_cardwith required context, wrapper actions, and not-evidence boundaries. - Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.
Bad example:
- Prompt: data-analysis invent trends from an unavailable spreadsheet.
- Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
- Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.
Completion Checklist
- Dataset or corpus source, record scope, schema or extraction method, join assumptions, analysis question, method, and stop condition are explicit.
- Numeric claims, anomalies, trends, segments, and log patterns are reported only from observed data or supplied evidence.
- Causal claims require observed identification evidence.
- Source acquisition, file conversion, report generation, and code fixes are routed to the narrower workflow when stronger.
Recovery Notes
- If the data itself is missing, ask for the smallest dataset sample, schema, or query output needed.
- If the user wants datasets found online, route to source-finder before analysis.
- If the user wants a PPT/PDF/XLSX report generated from data, route to materials-package or deliverable-package after analysis scope is clear.
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 76eed8f2f483
- 5d ago Changed c980ef83bb5d
- 6d ago Changed 215bcb5b7346
- 10d ago First seen · 132 lines · 57 tokens per session scan A 81af4c1034cb
omh-data-analysis is a skill published in the GitHub repository rlaope/oh-my-hermes (1,605 stars, last pushed today), licensed MIT. It adds 57 tokens to every session and 1,418 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 2026-08-30.
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