Omh Media Input

CommunityPopular

rlaope

omh-media-input

[omh] User-sent media - audio, video, YouTube links, screenshots, receipts, OCR, meeting recordings, transcripts, timestamps, and clip summaries, gated for source, permission, and hallucination risk. Use when the user says: media-input-operator, media input operator, media input, audio transcription, audio transcript, transcribe audio, transcribe this audio, meeting recording.

Overview

Publisher	rlaope
Repository	`oh-my-hermes`
Skill name	`omh-media-input`
Stars	2.7K
Forks	194
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 Media Input AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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-media-input .claude/skills/omh-media-input

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Media Input 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 Media Input 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 Media Input 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.

Media Input Operator

This is an OMH media-input-operator workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

media-input-operator 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: media-input-operator transcribe this audio meeting and summarize action items with evidence and timestamp boundaries.
Expected behavior: Produce prepare_media_input_card with 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: media-input-operator invent a YouTube transcript and claim the timestamps are verified without media evidence.
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

Media type, source location, permission boundary, transcript availability, language, requested output, timestamp requirement, and stop condition are explicit.
Downloads, uploads, ASR, transcript extraction, speaker labels, copyrighted media access, and provider setup are gated or marked missing.
Transcript text, OCR output, screenshot text, receipt fields, timestamps, quotes, action items, and media-summary claims are reported only from observed media or supplied transcript/extraction evidence.

Recovery Notes

If the media or transcript is missing, ask for the smallest source, file, transcript, or provider result needed.
If the request is broad current-source research about a video topic, route to research or source-finder before summary.
If the user wants a PPT/PDF/report generated from the media summary, route to materials-package after media input evidence is clear.
If the request is about whether a live duplex voice connector keeps whole spoken turns, route to external-connector-readiness for a realtime_voice_trial_receipt/v1 rather than treating a supplied recording as that evidence.

Use When

Use when Hermes should prepare or supervise audio/video transcript, YouTube/video summary, OCR, screenshot text extraction, receipt image parsing, or timestamped media extraction work without claiming media access, download, transcription, OCR output, or factual summary evidence.


Strong routing signals: `media-input-operator`, `media input operator`, `media input`, `audio transcription`, `audio transcript`, `transcribe audio`, `transcribe this audio`, `meeting recording`, `recording transcript`, `video transcript`, `youtube summary`, `youtube video`, `summarize youtube`, `summarize this youtube`, `video summary`, `summarize this video`, `ocr image`, `image ocr`, `photo ocr`, `picture ocr`, `graphic ocr`, `screenshot ocr`, `ocr this image`, `ocr receipt image`, `ocr this receipt image`, `receipt ocr`, `receipt image ocr`, `receipt text`, `receipt text from image`, `receipt fields`, `receipt fields from image`, `receipt image extraction`, `receipt image text`, `receipt image fields`, `parse receipt image`, `receipt image parse`, `receipt image into fields`, `image text extraction`, `extract text from image`, `extract text from this image`, `screenshot text extraction`, `extract text from screenshot`, `extract text from this screenshot`, `screenshot to text`, `timestamps`, `with timestamps`, `clip summary`, `podcast summary`, `webinar summary`, `오디오 전사`, `음성 전사`, `회의 녹음`, `녹음 요약`, `영상 요약`, `유튜브 요약`, `youtube 요약`, `이미지 ocr`, `이미지 OCR`, `이미지 텍스트 추출`, `이미지에서 텍스트 추출`, `영수증 ocr`, `영수증 OCR`, `영수증 이미지 ocr`, `영수증 이미지 OCR`, `스크린샷 텍스트 추출`, `스크린샷에서 텍스트 추출`, `타임스탬프`, `타임라인 요약`

Catalog Metadata

Category: media Phase: media-input-task Quality tier: workflow-surface-gated Reasoning demand: standard

Quality bar:

Name the user-facing workflow objective, required context, next action, and stop condition.
Separate prepared guidance from observed platform, runtime, connector, file, memory, or delivery evidence.
Expose missing tools, credentials, targets, or observations as user-visible gaps.

Required inputs:

user request
target context
delivery or status expectation
known missing evidence

Expected outputs:

media_input_task_card/v1
media_source_scope/v1
transcript_boundary/v1
media_summary_plan/v1
media_result_manifest/v1 when observed
next action
prepared-vs-observed boundary

Artifact expectations:

media_input_task_card/v1 metadata-only wrapper card when prepared
media_source_scope/v1 with media type, source location, permission boundary, requested time range, and stop condition
transcript_boundary/v1 separating supplied transcript, missing transcript, ASR/extraction requirement, language, speaker labels, and confidence gaps
media_summary_plan/v1 naming action-item, timestamped, clip, chapter, quote, or evidence-linked summary method
media_result_manifest/v1 only when supplied transcript, media file metadata, provider response, or observed transcript output exists

Safety rules:

A media input card is not media access, file upload, download, transcript extraction, OCR output, screenshot text extraction, receipt fields, speech-to-text output, timestamp accuracy, copyright clearance, source retrieval, or summary correctness evidence unless observed media-result evidence records it.
Do not claim connector, gateway, runtime, file generation, memory mutation, or host automation evidence from prepared guidance.
A media_result_manifest/v1 describes a supplied recording or transcript. It is never a live duplex session, one-input-to-one-dispatch integrity, audible response behavior, or realtime voice readiness, and it cannot be promoted into one.

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 Media Input AI skill do?

Why use Omh Media Input on TypingMind?

Because you install it once and use it with any model. Omh Media Input 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 Media Input in TypingMind?

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

Which AI models can use Omh Media Input?

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 Media Input?

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

Is the Omh Media Input 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Media Input Operator

Why This Exists

Do Not Use When

Examples

Completion Checklist

Recovery Notes

Use When

Catalog Metadata

Runtime Evidence

Frequently asked questions

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