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Jevreel

Organization
DeepAleph
jevreel

Search and understand videos without embeddings. Use when the user asks about the content of a video file or video URL - finding moments ("when does X happen"), finding a person or object from a photo, reading what is on screen at a moment, getting what is said, tracking the same things across a whole video, or one answer about the whole video. Works through the jevreel MCP tools or the `jevreel` CLI.

Overview

PublisherDeepAleph
Repositoryjevreel
Skill namejevreel
Stars
52
Forks
5
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 DeepAleph on GitHub. Read the source before you install it.

Installation

Install the Jevreel 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.
    https://github.com/DeepAleph/jevreel/tree/main/skills/jevreel
  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/DeepAleph/jevreel.git /tmp/jevreel
mkdir -p .claude/skills
cp -r /tmp/jevreel/skills/jevreel .claude/skills/jevreel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jevreel 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 Jevreel 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 Jevreel 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.

jevreel: ask questions about videos

jevreel lays a video out on contact sheets at one frame per second and has Jev, TypeSafe's fast decision model, screen every sheet in parallel, then re-check the best windows on full frames. You (the main agent) then look at what it found. Jev gives typed answers and probabilities, never descriptions: reading, describing and concluding is your job.

Setup check

  • A TypeSafe API key is needed for anything that asks Jev (indexing with a timeline, ask, find, locate, whole, lenses): TYPESAFE_API_KEY, or a key saved with jevreel login. Zoom, transcripts and timeline queries work without it. If tools report a missing key under Codex, have the user run uvx --from "jevreel[url]==0.2.0" jevreel login themselves (it asks for the key; never ask for it in chat).
  • ffmpeg and ffprobe must be on PATH.
  • URLs need jevreel[url]; videos without captions need jevreel[asr] for a transcript.

Workflow

  1. Index once. MCP index_video(source) or jevreel index <file-or-url>. Later calls take the returned id, the path, or the title ("tears of steel"). Re-indexing an indexed video returns at once.
  2. Find moments. ask_video(video, query) / jevreel ask <id> "<query>".
    • Write the query as a plain description of what would be visible or said: "a bar chart on a slide", "someone opens a laptop", "the speaker mentions pricing".
    • One thing per query. Split "a red car and a dog" into two asks; Jev is most accurate on simple questions.
    • mode: visual (on screen only), speech (transcript only, cheapest), both, or auto.
    • Narrow with start/end when the user names a part of the video.
  3. Find from a photo. find_in_video(video, image_path, note="person") / jevreel find <id> photo.jpg --note person.
  4. Look before you answer. Hits are leads, not facts. Verified hits come with frame, a full-resolution image of the moment: look at it. zoom_video(video, start, end) / jevreel zoom <id> 12:00 12:10 gives more frames. locate_in_frame(video, at, target) boxes and crops one thing in one frame for close reading.
  5. What is said. video_transcript(video, start, end) / jevreel transcript <id> --start 12:00 --end 13:00.
  6. Whole-video questions. ask_whole_video(video, question, kind, options) / jevreel whole <id> "Is this a tutorial?". Use kind=choice with options, or kind=score with levels lowest first (at most 10).

Lenses: when the same questions matter across the whole video

When the user cares about several things throughout a video (who is on screen, safety rules, brands, weather, whether a slide is up), don't ask each one separately. Run a lens: every minute answers all of its questions in one request. Then filter the answers as often as needed, for free.

  • MCP run_lens(video, lens) with {"name": "site", "questions": [{"name": "helmet_off", "type": "noul", "ask": "Is a worker without a helmet visible?"}, {"name": "vehicle", "type": "choice", "ask": "Which vehicle is most visible?", "options": ["car", "truck", "none"]}, {"name": "crowd", "type": "score", "ask": "How crowded is it?", "levels": ["Empty", "Some", "Busy"]}]}. CLI: jevreel lens <id> lens.toml. To include a lens while indexing, pass it to index_video (or jevreel index --lens); it then costs almost nothing extra.
  • query_timeline(video, "helmet_off > 0.7") / jevreel query <id> "helmet_off > 0.7 and vehicle == truck". Conditions are column op value joined by and; quote a value that contains "and" (place == 'books and a desk'). Omit where to see every minute and its columns. Yes/no questions also give <name>_at, the second it is most visible.
  • The default timeline columns are there too: people, talking_to_camera, screen_or_slides, text_on_screen, cut, setting, activity (0-3), keyframe.

Reading results

  • probability is Jev's yes-probability; strong means at least 0.7. For verified hits it comes from the full frames, at / time is the frame that showed it best, and screened is the contact-sheet probability before. Unverified hits come from the sheet alone.
  • shown and said are the screen and transcript questions, asked separately.
  • also lists other likely moments in the same window.
  • Probabilities rank well but are not guaranteed calibrated. Look before stating anything you have not seen, and say so when you have only a weak hit.
  • No hits above the threshold is a real answer, but check weak first: windows Jev did not rule out, including ones verification overturned. Zoom into them before saying "not found".
  • Cite moments by their times (for example "at 12:04"), so the user can jump there.

Costs

Jev bills input tokens only, and repeated identical requests are answered from jevreel's cache for free (cached in results). Every ask sends each sheet in range once (one sheet per minute of video), plus one request per verified window, so narrowing with start/end makes asks cheaper and faster. Results report requests, cached, input_tokens and estimated_usd.

Frequently asked questions

What does the Jevreel AI skill do?

Search and understand videos without embeddings. Use when the user asks about the content of a video file or video URL - finding moments ("when does X happen"), finding a person or object from a photo, reading what is on screen at a moment, getting what is said, tracking the same things across a whole video, or one answer about the whole video. Works through the jevreel MCP tools or the `jevreel` CLI.

Why use Jevreel on TypingMind?

Because you install it once and use it with any model. Jevreel 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 Jevreel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DeepAleph/jevreel/tree/main/skills/jevreel. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jevreel?

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 Jevreel?

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

Is the Jevreel AI skill free?

Yes. It is published on GitHub by DeepAleph 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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