Kai Motion Spot logo

Kai Motion Spot

Community
cgallic
kai-motion-spot

Code-rendered motion video for a product or a client, built the way a motion studio would do it. Every frame comes from an HTML seek(t) page rendered by Playwright at 1080p60 with real motion blur, using real product UI and screenshots with a privacy blur, and an ElevenLabs hype score cut on bar lines with its drop verified on the reveal. Three formats — a 20-30 s hype launch spot, a polished feature walkthrough from real screenshots, and a 19 s pain-point video with a muted site cut and burned-in text. Delivered with baked posters, H.264 + VP9 web encodes at 1280 px (1.5-3 MB), a site-embed kit (muted autoplay or click-to-play), 9:16 social re-layouts and share copy, plus a service playbook for selling it. Use when "make a launch video", "make a commercial / promo / ad spot", "motion design video", "product walkthrough video from screenshots", "pain point videos for the site", "videos for the homepage", "sell video production", "price a video package", or a polished brand spot is wanted rather than a talking-head edit or a Remotion template batch (use /kai-video-production for those).

Overview

Publishercgallic
Repositorykai-cmo-harness
Skill namekai-motion-spot
Stars
61
Forks
8
Bundled files
Instructions only
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 cgallic on GitHub. Read the source before you install it.

Installation

Install the Kai Motion Spot 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/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-motion-spot
  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/cgallic/kai-cmo-harness.git /tmp/kai-cmo-harness
mkdir -p .claude/skills
cp -r /tmp/kai-cmo-harness/harness/skills-v2/kai-motion-spot .claude/skills/kai-motion-spot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kai Motion Spot 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 Kai Motion Spot 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 Kai Motion Spot 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.

Kai root note: knowledge/, harness/, and scripts/ paths in this skill live in the Kai install, not the user's project. Resolve them against the first ancestor directory of this SKILL.md that contains a knowledge/ folder (the Kai plugin root, ~/.claude/kai, or the kai-cmo-harness repo). MARKETING.md, memory/, and any output files live in the current project. If a referenced scripts/ command is not available in this install, say so, skip it, and continue with the file-based guidance — never fabricate its output.

/kai-motion-spot — product video that looks like a studio made it, from code

Objective

A delivered video package the approver signs off: a hype launch spot, a walkthrough set built from real screenshots, or a pain-point set. Each video has a 16:9 master, a real 9:16 re-layout, a baked poster, web encodes, an embed snippet and share copy. Everything on screen comes from one HTML page where every style is a pure function of time, and the score is cut so its drop lands on the reveal. When the request is about selling this, the objective is a service offer built from the playbook, with the price left to the founder.

Done when

Work type social-post for anything published (floor E5/C2/O3, harness/eco-floors.yaml). A client delivery is done at E3, when the approver signs off the final package.

  • E3: the approver chose a score variant and signed off the final. Publishing is a separate, per-post approval.
  • C2:
    • analyze.py --verify passes on every master (drop within 30 ms of the picture), loudness is -14 LUFS, and SFX are within 15 ms of the grid.
    • qa.py sheets were reviewed for the whole video at phone size in both formats, against every row of the watch-back table.
    • Privacy sheets were read with no personal data legible, and site cuts were reviewed muted.
    • ffprobe matches the meta, frame 0 is the baked poster, and web encodes are 1.5-3 MB with faststart.
  • O3 (published cuts): reach, engagement rate, profile clicks and link clicks at 7 days, against a baseline recorded before publishing.

Constraints

  • Keys come from the environment only: ELEVENLABS_API_KEY, and UPLOAD_POST_API_KEY for publishing. Never print them or write them into the project.
  • Claims: show what is live, plus roadmap features the client already claims publicly, and list those roadmap claims in the report. Never invent features, stats, customers or testimonials. Use the official logo files only; never retype a logo.
  • Real material:
    • The newest UI only, in tight readable crops, never a whole spinning page, never demo cruft.
    • Screenshots go through assets.py blur, which downsamples before blurring; a plain Gaussian can leave text readable.
  • Grid: 120 BPM, with cuts on downbeats and the drop on the reveal. Any line meant to be read stays settled for at least 0.3 s per word. Stats stack and stay.
  • Music is hype for launches, whatever the brand looks like. Calm or ambient only when asked.
  • Motion taste: one idea per shot, one accent, masked reveals, one camera language. The effects banned in the method (shake, RGB split, flares, particles, glitches, bouncy easing) stay banned.
  • Formats:
    • 9:16 is its own re-layout, never a pillarbox, with key content above the bottom ~300 px.
    • Pain-point site cuts are muted, and the burned-in text carries the story.
    • Walkthroughs are click-to-play; short loops are muted autoplay.
  • Render fixes stay on: the warm-up pass, the two-rAF paint wait, every hard cut in cuts, and fast moves in fast.
  • Publishing: posting follows harness/references/social-automation-rules.md and the platform posting rules. upload_post.py stays a dry run until the account owner approves that exact post.
  • Selling: the price is a founder decision; cite market reference points only with a URL. Internal costs (music credits, render time, operator time) never appear on a customer-facing proposal, page or email.

Context

NeedLoad / run
Formats, page contract, score, render fixes, taste rules, watch-back QA tableharness/references/motion-spot-method.md
Deliverables menu, client inputs, turnaround, QA checklist, approval, cost basis (INTERNAL)harness/references/motion-spot-service-playbook.md
Engine (run from workspace/video/<slug>/)scripts/motion_spot/: render.py, probe.py, qa.py, poster.py, web_encode.py, analyze.py, build_score.py, sfx.py, music.py (--dry-run), assets.py, walkthrough/walkthrough.py, upload_post.py
Starting pointsreference_spot.html + .meta.json, examples/walkthrough.example.json, examples/cues.example.json, examples/blur-boxes.example.json, examples/upload-spec.example.json
Product, claims, voiceMARKETING.md (project root)
Posting rulesharness/references/social-automation-rules.md, harness/references/*-organic-posting-rules.md

Requirements: Python 3.10+ with playwright numpy scipy pillow (then playwright install chromium), and ffmpeg with libx264 and libvpx-vp9. Smoke test: DUR=2 MASTER=none PAGE=index.html python "$KAI/render.py".

Output: workspace/video/<slug>/:

  • the page and meta
  • assets/, audio/
  • out/: masters, posters, share-copy.txt, render-log.txt, web/ with the encodes and snippets, and site-embed.md for sets
  • qa/: the QA sheets

Escalate when

  • The product, the promise or which features are live cannot be established from MARKETING.md or the client's materials.
  • A screen can only be shown with real personal data that the blur cannot fully cover.
  • A claim, stat or testimonial the client wants has no source.
  • analyze.py --verify keeps failing after a snap. Offer another track rather than shipping an off-beat drop.
  • Anything would be posted publicly, or a price would be quoted to a customer.

Frequently asked questions

What does the Kai Motion Spot AI skill do?

Code-rendered motion video for a product or a client, built the way a motion studio would do it. Every frame comes from an HTML seek(t) page rendered by Playwright at 1080p60 with real motion blur, using real product UI and screenshots with a privacy blur, and an ElevenLabs hype score cut on bar lines with its drop verified on the reveal. Three formats — a 20-30 s hype launch spot, a polished feature walkthrough from real screenshots, and a 19 s pain-point video with a muted site cut and burned-in text. Delivered with baked posters, H.264 + VP9 web encodes at 1280 px (1.5-3 MB), a site-embe...

Why use Kai Motion Spot on TypingMind?

Because you install it once and use it with any model. Kai Motion Spot 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 Kai Motion Spot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-motion-spot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kai Motion Spot?

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 Kai Motion Spot?

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

Is the Kai Motion Spot AI skill free?

It is published on GitHub by cgallic. Check the repository for licensing terms. 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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