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Avenox Graphics

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avenoxai
avenox-graphics

Avenox Studio — produce brand-locked motion graphics with HyperFrames and composite them onto a cut video. Use when making cutaway/overlay graphics: title cards, diagrams, data-viz, loops, lower-thirds, outro. Proven cutaway + composite recipe with gotchas. Triggers: "make graphics", "add an animation", "motion graphic", "intro/outro card", "data-viz". Part of avenox-video step 3.

Overview

Publisheravenoxai
Repositoryavenoxskills
Skill nameavenox-graphics
Stars
105
Forks
6
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 avenoxai on GitHub. Read the source before you install it.

Installation

Install the Avenox Graphics 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/avenoxai/avenoxskills/tree/main/skills/avenox-graphics
  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/avenoxai/avenoxskills.git /tmp/avenoxskills
mkdir -p .claude/skills
cp -r /tmp/avenoxskills/skills/avenox-graphics .claude/skills/avenox-graphics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Avenox Graphics 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 Avenox Graphics 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 Avenox Graphics 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.

Graphics — HyperFrames cutaways → composite

Brand source of truth: brand/frame.md. Define it once, then never deviate inside a job. (Reference implementation: warm paper #f3efe4, ink #1b1712, one golden accent #e8b23a, a serif display face + a grotesk UI face, plus a mascot. Deliberately anti-AI-slop — no neon, no gradient mesh, no glassmorphism, no 3D gloss.)

Pipeline

  1. Project: cd "$STUDIO_JOBS/<job>/graphics" && npx hyperframes init gfx (one-time; installs a headless chromium — slow first run, background it).
  2. One composition per beat = a standalone <name>.html. Reuse the brand template below. Each is full-screen 1920×1080 — a cutaway (opaque, replaces footage) or a transparent overlay.
  3. Render serially (render reads index.html):
    bash
    for f in *.html; do
      n="${f%.html}"; [ "$n" = index ] && continue
      cp "$f" index.html
      npx hyperframes render -f 60 --resolution landscape -o "renders/$n.mp4"
    done
  4. Composite all cutaways onto the cut via ffmpeg, each at its timecode.

Composition contract (HyperFrames)

  • #root with data-composition-id="main" data-start data-duration data-width data-height.
  • Every timed element: class="clip" + data-start data-duration data-track-index.
  • ONE paused GSAP timeline registered: window.__timelines["main"] = gsap.timeline({paused:true}). HyperFrames seeks it per frame, so everything must be seek-safe: use fromTo/set, and position every tween on tl.
  • Deterministic only — no Date.now(), no Math.random(), no runtime fetch. Web fonts via <link> are fine.
  • Render flags: -f 60, --resolution landscape (1920×1080), --format mp4 (use mov/webm for alpha overlays).

Brand template

  • Fonts via a single <link> to your webfont provider — renders fine in headless chrome.
  • html,body{background:<paper>} + a paper div, radial vignette, and an SVG feTurbulence grain at opacity .05 / mix-blend-mode: multiply. Fix the turbulence seed or the grain is nondeterministic and frames will flicker.
  • Kicker (grotesk, tracked caps) → title (serif, heavy) → accent underline (scaleX wipe) → mascot (assets/mascot.png).

Mascot transparency (keying a mascot off an ivory plate)

HSV key — saturated body colors and black ink survive, low-saturation cream becomes alpha:

python
import numpy as np
from PIL import Image
im = Image.open(src).convert('RGB')
a  = np.asarray(im) / 255.
mx = a.max(2)
s  = np.where(mx > 1e-6, (mx - a.min(2)) / np.maximum(mx, 1e-6), 0)
bg = (s < 0.22) & (mx > 0.60)
out = np.dstack([np.asarray(im), np.where(bg, 0, 255).astype('uint8')])
img = Image.fromarray(out, 'RGBA')
img.crop(img.getbbox()).save(dst)

Tune 0.22 / 0.60 to your plate. Too aggressive and you eat highlights on the subject.

Compositing cutaways onto the cut

Per cutaway input: [i:v]setpts=PTS+{START}/TB,format=yuv420p[ovi], then [prev][ovi]overlay=enable='between(t,{START},{END})':eof_action=pass[vi].

Keep the base audio (-map 0:a). Hardware encode with h264_videotoolbox -b:v 18M.

Gotchas

  • npx/hyperframes need the SSL cert fix: export SSL_CERT_FILE=$(python3 -c 'import certifi;print(certifi.where())') (and REQUESTS_CA_BUNDLE) or downloads fail.
  • hyperframes init first run is slow (chromium download) — background it.
  • Blended is the better default. Prefer animated overlays on the live screen (lower-thirds, kinetic titles, callouts — rendered transparent as .mov/.webm with --format mov) for most beats. Reserve full-screen cutaways for the intro, outro, and genuine dead zones where the screen shows nothing useful. Replacing a screen that has useful content loses information — overlay it instead.
  • Placement: grab the frame at the target timecode (ffmpeg -ss), look at it, and place graphics in empty safe zones — never over a face or important UI. Optional: HyperFrames remove-background matting to sit graphics behind the subject.
  • Don't over-animate. Restraint reads as premium; constant motion reads as a template.
  • Render time scales with frame count — keep cutaways short (5–26s). Long sustained pieces get expensive fast.

Frequently asked questions

What does the Avenox Graphics AI skill do?

Avenox Studio — produce brand-locked motion graphics with HyperFrames and composite them onto a cut video. Use when making cutaway/overlay graphics: title cards, diagrams, data-viz, loops, lower-thirds, outro. Proven cutaway + composite recipe with gotchas. Triggers: "make graphics", "add an animation", "motion graphic", "intro/outro card", "data-viz". Part of avenox-video step 3.

Why use Avenox Graphics on TypingMind?

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

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

Which AI models can use Avenox Graphics?

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 Avenox Graphics?

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

Is the Avenox Graphics AI skill free?

Yes. It is published on GitHub by avenoxai 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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