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-image-cardsgit 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-image-cards)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-image-cards"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-image-cards/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-image-cards"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-image-cards.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 | $0.00079 | $0.02781 |
| Opus 5.5 | $0.00032 | $0.01112 |
| Sonnet 5.5 | $0.00016 | $0.00556 |
| Haiku 4.5 | $0.00008 | $0.00278 |
Grade A, and why
omh-image-cards 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 5d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Img Summary
This is an OMH img-summary workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
Why This Exists
img-summary exists so Hermes can turn common communication work into provider-neutral image-card prompts while adapting format, domain mood, background, texture, lighting, camera, and poster grammar, and keeping generation, QA, and delivery as observed-only evidence.
Do Not Use When
- The user needs a deck, PDF, spreadsheet, HWP, Markdown package, or binary file export plan; use
omh-materials-package. - The user wants a text-only report, leadership brief, or PPT-ready outline; use
omh-report-package. - The user asks OMH to directly generate, inspect, upload, or post an image without a wrapper-supplied observed evidence path.
Examples
Good example:
- Prompt: img-summary make a PR summary card for reviewers.
- Expected behavior: Prepare visual_prompt_card/v1 with the PR review infographic format, copy mode, generation prompt, negative prompt, and not-evidence boundaries.
- Why: The request asks for an image-card communication artifact, not a PDF/deck package or hidden image generation.
Bad example:
- Prompt: img-summary prove this generated card was posted to Slack.
- Expected behavior: Ask for visual_observation/v1 delivery evidence or report delivery as not_observed.
- Why: A prompt card cannot prove generated image, QA, or delivery evidence.
Completion Checklist
- The material source, target format, audience, structure, and QA expectation are named.
- Binary export, rendering, formula recalculation, attachment, and delivery stay observed-only.
- The next action identifies whether the package is planned, generated, QA-ready, or blocked.
Recovery Notes
- If a renderer or file tool is missing, keep the package prepared and expose the generation handoff.
- If render QA is unavailable, mark the artifact unverified and request the smallest visual/file check.
When to Use
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.
- 5d ago Changed · +1 lines b5a1540dc950
- 14d ago Changed · +3 lines 02658e65e2af
- 27d ago Changed · +3 lines 2b548c538eff
- 1mo ago Changed · +6 tokens per session 0cde86181dc4
- 1mo ago Changed · +7 tokens per session fd398d5d8a8a
- 1mo ago Changed · +6 lines 47aba256922c
- 1mo ago Changed · -11 lines 2e6be617f685
- 1mo ago Changed · +7 lines 7e09aca60464
- 1mo ago Changed 427ef8f53044
- 1mo ago First seen · 152 lines · 66 tokens per session scan A bfcac11cf914
omh-image-cards is a skill published in the GitHub repository rlaope/oh-my-hermes (4,314 stars, last pushed today), licensed MIT. It adds 79 tokens to every session and 2,781 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 2026-08-30.
Other skills, from other repositories
nlpm-audit
Audit SKILL.md, AGENTS.md, prompts, hooks, and plugin manifests for instruction conflicts, quality, broken references, and manifest-to-disk drift.
api-design-reviewer
Use when reviewing API designs for consistency, usability, versioning, error semantics, security, backward compatibility, and developer experience before implementation or release.
pr-review-expert
Review GitHub PRs or GitLab MRs for correctness, security, compatibility, and affected test coverage, with actionable evidence tied to the diff.
improve-codebase-architecture
Review module boundaries, interface depth and high-leverage refactors with evidence.
gh-address-comments
Use when addressing GitHub PR review comments or issue comments on the current branch with gh CLI, including auth checks, comment triage, edits, verification, and replies.
doubt-driven-development
Review high-risk or uncertain engineering decisions adversarially when independent verification can expose costly assumptions before implementation.