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Omh Image Cards

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rlaope
omh-image-cards

[omh] Image prompt cards - turn meetings, reports, PRs, issues, research, and releases into domain-aware image prompt cards. Use when the user says: img-summary, img summary, visual prompt card, image card, image generation, image edit, edit this image, remove the background.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-image-cards
Stars
2.7K
Forks
194
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Image Cards AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-image-cards .claude/skills/omh-image-cards
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Image Cards in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Image Cards on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Image Cards is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 materials-package.
  • The user wants a text-only report, leadership brief, or PPT-ready outline; use 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.

Use When

Use when Hermes should prepare a source-specific visual or supplied-image edit prompt without claiming generation or transformation.

Strong routing signals: `img-summary`, `img summary`, `visual prompt card`, `image card`, `image generation`, `image edit`, `edit this image`, `remove the background`, `background removal`, `image generation features`, `image generation support`, `image tool support`, `image feature`, `image features`, `visual generation`, `visual generation support`, `visual card support`, `image summary card`, `summary image`, `summary card`, `explainer image`, `feature explainer image`, `feature explanation image`, `product explainer image`, `product explainer card`, `infographic`, `one-page infographic`, `workflow image`, `workflow card`, `shareable image`, `explain this as an image`, `make an image explaining`, `image explaining the cron feature`, `make an image explaining the cron feature`, `make a visual summary of this PR`, `visual summary`, `picture card`, `meeting notes picture card`, `vertical card`, `vertical summary image`, `vertical image card`, `meeting image`, `meeting summary image`, `conversation summary image`, `meeting notes image`, `pr card`, `pr summary card`, `pull request card`, `review card`, `issue card`, `bug triage card`, `feedback card`, `triage card`, `research card`, `report card`, `report summary card`, `report digest card`, `news briefing card`, `competitor-news briefing card`, `briefing card`, `release announcement image`, `release notes image`, `release notes thumbnail`, `announcement card`, `multilingual img-summary`, `이미지 편집`, `배경 제거`, `회의록 세로 요약 이미지`, `회의 요약 이미지`, `회의록을 보기 좋은 세로 이미지로 요약`, `회의록을 보기 좋은 세로 이미지로 요약해줘`, `세로 이미지로 요약`, `세로 이미지로 요약해줘`, `보기 좋은 세로 이미지`, `PR 요약 카드`, `PR 내용을 리뷰어에게 공유할 이미지 카드`, `PR 내용을 리뷰어에게 공유할 이미지 카드로 만들어줘`, `이슈 트리아지 카드`, `버그 트리아지 카드`, `피드백 카드`, `리포트 요약 카드`, `보고서 요약 카드`, `경쟁사 뉴스 브리핑 카드`, `리서치 브리핑 카드`, `릴리즈 노트 발표 이미지`, `릴리즈 노트 썸네일`, `업데이트 발표 이미지`, `세로 이미지 카드`, `이미지 카드`, `회의록 이미지 카드`, `회의록을 세로 이미지 카드`, `설명 이미지`, `설명하는 인포그래픽`, `기능 설명 이미지`, `기능 소개 이미지`, `인포그래픽`, `인포그래픽 만들어줘`, `이미지 요약 카드`, `요약 이미지`, `요약 카드`, `썸네일`, `썸네일 만들어줘`, `썸네일로 만들어줘`, `카드 이미지`, `이미지로 요약`, `이미지로 요약해줘`, `이미지 생성`, `이미지 생성해줘`, `이미지 만들어줘`, `크론 기능 설명 이미지`, `크론 기능 설명 사진`, `크론 기능 설명 사진 하나 만들어줘`, `사진 카드`, `사진처럼 만들어줘`, `PR 요약 사진`, `공유용 이미지`, `안내 이미지`, `워크플로우 이미지`, `이미지로 설명`, `이미지 하나 만들어줘`

Catalog Metadata

Category: materials Phase: visual-prompt-card Quality tier: visual-card-gated Reasoning demand: standard

Quality bar:

  • Pick one canonical source kind: meeting, github_pr, issue_feedback, research_briefing, report_summary, or release_announcement.
  • Use the source-specific format profile instead of forcing every visual into the same grid.
  • Expose the detected domain_key so wrappers and users can explain why a domain-specific scene and poster archetype were selected.
  • Adapt scene, texture, depth, lighting, camera, motifs, palette, and composition to domains such as security, commerce, sports, fashion, finance, developer work, or research.
  • Resolve a poster archetype such as Swiss grid, cinematic key-art, editorial magazine, constructivist photomontage, data infographic, product ad, technical brutalist, museum exhibition, sports event, or luxury lookbook.
  • Ask image tools to render the domain-specific environment first, then place readable card modules on top; reject flat vector clipart, plain gradients, generic glass cards, color-swapped templates, and low-detail wallpaper.
  • Preserve a stable OMH img-summary format contract: source badge, headline, source-kind subtitle, content modules, evidence footer, and small OMH generated mark.
  • Use long_scroll or extended rows when the card needs a document-style vertical canvas with more sections or denser text.
  • Keep visible card text readable and faithful to supplied source or structured sections; do not shrink paragraphs into tiny poster copy.
  • Separate prompt prepared, image generated, visual QA passed, and delivered states.
  • Bind an image result to the card digest, action, attempt, and content digest, then warn on route mismatch, unknown route, stale card, digest drift, and response reuse rather than resolving any of them.
  • For transformations, preserve requested identity, composition, text, and protected regions; verify the observed result against the edit brief before a PASS claim.
  • Prefer img-summary over materials-package only when the request asks for an image, visual card, or summary card.
  • Use materials/report workflows only after an observed generated file needs packaging.

Required inputs:

  • source/image
  • create/edit
  • format
  • ratio
  • headline or source text
  • audience
  • language mode
  • card sections, source excerpts, or preserve/remove constraints

Expected outputs:

  • visual_prompt_card/v1
  • image_generation_setup/v1 when generator capability is missing
  • source-specific visual format
  • detected domain_key
  • domain-aware visual theme
  • poster_archetype/v1
  • poster archetype visual grammar
  • background, texture, camera, and lighting direction
  • image-safe card copy
  • generation prompt
  • image transformation brief when editing a supplied image
  • negative prompt
  • quality checks
  • visual evidence boundary
  • visual_generation_receipt/v1 when a producer reports an image attempt
  • requested route separate from observed route

Artifact expectations:

  • visual_prompt_card/v1 prompt card when prepared
  • image_generation_setup/v1 fallback when image_generation_capability/v1 is unknown or prompt_only
  • visual_observation/v1 only when a wrapper or user records generated image, visual QA, or delivery evidence
  • visual_generation_receipt/v1 only when a producer reports one image attempt, with unattested route fields left unknown

Safety rules:

  • Do not call image providers, LLMs, APIs, or network services from OMH core.
  • Do not claim image generation, visual QA, posting, sharing, attachment, or delivery from a prepared prompt card.
  • Require visual_observation/v1 before claiming generated image, visual QA, or delivery evidence.
  • Report requested route apart from observed route; leave provider, model, quality, operation, dimensions, and credential class unknown unless visual_generation_receipt/v1 attests them.
  • Do not infer an observed provider, model, or quality from configuration, capability state, or a returned file.
  • A failed or partial visual_generation_receipt/v1 keeps its failure stage and is not generated-image evidence.
  • Raw source text may become only an extractive draft; do not fabricate summaries, owners, decisions, test results, or conclusions.
  • Show generate_visual_image only when wrapper context reports image_generation_capability/v1 as connected, and still treat it as wrapper-owned action rather than evidence.
  • When image_generation_capability/v1 is unknown or prompt_only, ask which image tool to use and route to image_generation_setup/v1 instead of pretending generation can start.
  • For image edits, require a supplied image reference and state preserve, remove, replace, crop, and output constraints without claiming the source image was loaded.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

Frequently asked questions

What does the Omh Image Cards AI skill do?

[omh] Image prompt cards - turn meetings, reports, PRs, issues, research, and releases into domain-aware image prompt cards. Use when the user says: img-summary, img summary, visual prompt card, image card, image generation, image edit, edit this image, remove the background.

Why use Omh Image Cards on TypingMind?

Because you install it once and use it with any model. Omh Image Cards is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Image Cards in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-image-cards. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Omh Image Cards?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Image Cards?

As many as you like. As long as a model supports skills, you can use Omh Image Cards with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Image Cards AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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