Longform Animated Talking Head logo

Longform Animated Talking Head

Community
kevinbadi
longform-animated-talking-head

Edit a LANDSCAPE (16:9, YouTube long-form) talking-head clip Kevin records at his desk into a fast-paced animated hyper-edit — 1920x1080, split layout by default (animation slot left, his head in a ringed card right) with full-bleed and hero-slot layouts as accents, motion-graphic cards, real logos, Giphy reaction GIFs, YouTube thumbnails he drops in, kinetic phrase captions on an accent line, punch/shake on the slot only. No HeyGen. Use when the user hands over a landscape/long-form/YouTube talking-head video (intro, full episode, tutorial) and asks to edit it; vertical shorts stay with split-animated-talking-head.

Overview

Publisherkevinbadi
Repositoryopen-edits
Skill namelongform-animated-talking-head
Stars
53
Forks
10
Bundled files
15
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.

  • 15 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Longform Animated Talking Head 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/kevinbadi/open-edits/tree/main/skills/longform-animated-talking-head
  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/kevinbadi/open-edits.git /tmp/open-edits
mkdir -p .claude/skills
cp -r /tmp/open-edits/skills/longform-animated-talking-head .claude/skills/longform-animated-talking-head
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Longform Animated Talking Head 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 Longform Animated Talking Head 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 Longform Animated Talking Head 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.

Long-form animated talking head (landscape 1920x1080)

Built reverse-first on the JEV AI x HyperEdit intro (2026-09-21, Kevin: "ya really good. solid long form skill start"). Reference cut = clone-projects/jev-intro-longform-animated/render.py (historical, not bundled) on top of templates/longform_engine.py. Same visual language as the short-form flagship (Oswald + Luckiest Guy, graphite wash, neon/orange/red + brand accents, real marks, GIFs, light fx, rare punches) but the canvas, layouts and hook rules are different.

Locked rules for long-form (Kevin 2026-09-21)

  1. SPLIT is the default layout for talking-to-camera (Kevin, screenshot of the CapCut/Premiere frame: "when i am talking full screen i like this editing layout you did the most"). Animation slot 1140x950 left, his head in the ringed 680x950 card right. FULL (face full-bleed + side overlays) only for the hook, one dramatic claim, the CTA. HERO (full-width slot + head PIP) only when a grid / thumbnails / b-roll IS the content.
  2. No mystery cold open. Short-form gets the dark wash + "?"; long-form does NOT. Frame 0 is a bright billboard on the ACTUAL topic (brand badges + wordmark + "the biggest update yet" chip beside the untouched face). Never darken the hook. No "?" badges later either.
  3. No bottom strip. Progress bar / label / timecode / chapter name were "truly pointless". Accent line at y=1038, captions rest on it, nothing under it.
  4. Head never moves. No punch_zoom / shake on the head in any layout. Slot layer only.
  5. Captions are phrase groups (<=4 words, Luckiest Guy 60px, white, 7px black stroke, active word in KEY_COL, unspoken words dim STEEL), baseline resting on the accent line.
  6. Consistent palette + type across a series. Pick brand accents once (PINK = Jev, VIOLET = HyperEdit; CYAN / NEON / RED per section) and reuse them every episode.

Layouts (engine constants)

SPLIT  (default) slot 1140x950 at (40,46) LEFT + head card 680x950 at x=1200 RIGHT (1:1 HEAD_CROP from
       meta, r=44, accent ring). Slot-local px: title cy≈70, left cx≈330, right cx≈850, chips y≈700, lane y≈800.
FULL   head full-bleed 1920x1080 + transparent overlay 1920x1080, bottom gradient under the captions.
       Author overlays on the RIGHT (x 1300..1900) or a top wordmark (cy≈84); the face stays clean.
HERO   slot 1840x950 full width + head PIP 520x292 at (1330,690) with ring. Keep hero pieces out of
       the bottom-right 520x292; lanes may run behind the PIP.

Switch layouts on spoken anchors (every ~4–8 s keeps it action packed). Layout change = 0.24 s zoom-dissolve (outgoing view rendered live at the same t, incoming eases 1.035→1.0 scale); same-layout section cuts whip. No shake and no hyperframe flash on any section cut (Kevin 2026-09-30: "I don't like the video shake transition from a different view"). Shake only fires on extra_hits impact beats.

Pipeline

  1. Workdir + prep (clip must exist on disk; landscape 1920x1080 expected, others cover-crop):
bash
bash "${CLAUDE_SKILL_DIR}/scripts/setup_workdir.sh" <slug>        # projects/<slug>-longform/
cd projects/<slug>-longform
python3 "${CLAUDE_SKILL_DIR}/scripts/prep_longform_head.py" --clip "/abs/clip.mov" --whisper

Writes source/talking_head.mp4 (h264 30fps), audio/narration.{wav,m4a}, audio/words.json, source/meta.json (DUR, HEAD_CROP = 680x950 face-anchored crop for the SPLIT card) and renders/head_sheet.jpg. View the sheet: crown and chin inside the card, face ~42% down; override --crop-x/--crop-top if he drifts. Fix Whisper mishears in words.json first ("Jev"/"Jeff", product names) — captions, anchors and number beats all read it.

  1. Read the whole transcript with timestamps and plan sections at spoken anchors (tabs("word", after) / tabs_any([...], after)). SPLIT by default; one FULL for the hook, HERO for the grid moments. "Action packed and fast paced": 6–10 live pieces in the slot, a beat on every named product / number / punchline, a GIF on every joke or flex.

  2. Source assets (before choreography; scripts live in the short skill, call by path):

    • logos: split-animated-talking-head/scripts/fetch_brand_asset.py → assets/logos/. Proven marks (jev, typesafe, hyperedit, obsidian, github, capcut, premiere, youtube, ffmpeg, python, openai, gemini, deepseek) sit in clone-projects/jev-intro-longform-animated/assets/logos (historical, not bundled) — copy them. Claude family = bundled invader/sphinx. Premiere = Wikimedia SVG → qlmanage -t -s 512. CapCut = s2 favicon (tool_badge skips its white punch).
    • GIFs: fetch_giphy.py --query ... --list, pick by title, --id <id> --slug <slug>; ≤4 s; eyeball frame 1. One per beat.
    • thumbnails Kevin drops in → assets/thumbs/thumb_<i>.png → thumb_card(i, w) (crops the YouTube card to the 16:9 image, adds a play badge). Screenshots he pastes live in /var/folders/.../TemporaryItems/NSIRD_screencaptureui_*/ — copy with find -exec cp (a plain cp on those paths fails in the sandbox).
    • figures: fetch_figure.py whenever a company / niche with famous faces is spoken. Build a contact sheet of everything and view it before authoring.
  3. Author render.py from templates/render_template.py: SECT, T_*, KEYWORDS, KEY_COL, bespoke cards, scene(), then run(SECT, scene, punch_words, extra_hits, ...). Engine cards: chip / strike_chip / big_text_card / stamp_card / wordmark / ghost_img / ticker / draw_code_rain / sparkle_badge / round_logo / tool_badge + x_mark / terminal_card (typed lines) / timeline_card (fake NLE, playhead + agent cursor) / workflow_card (nodes light up) / thumb_card / demo_card / whisper_line; plus media_marks (gif_card, logo_badge, topic_marks, figure_*, counter/odometer), hyper_edits (entrance(k), lanes / guarded_conveyor, punch_beats, whip, hyperframe), light_fx (light_hit, light_rays, bloom_orb, speed_streaks) and claude_marks (invader / sphinx). Beat t0 = spoken word (max 0.08 s lead via r(T)); cold-open atmosphere with place(..., -0.35, dur=0.01) so each section's first frame is packed; exit_at the previous big piece ≤0.06 s before the next hero; rotate entrance(k); punches = ONE hero word per section (>=8 s apart); extra_hits = the 3–5 biggest claims.

  4. Preview, then render:

bash
PREVIEW="0.0,2.2,5.5,12.6,21.5,29.5,44.6,52.2" python3 render.py      # frames/out/f<n>.jpg
bash "${CLAUDE_SKILL_DIR}/scripts/render_parallel.sh" "Readable Name" 4
# → renders/final.mp4 (1920x1080, the clip's audio, crf 16) + ~/Downloads/Readable Name.mp4

Tile the preview frames (3 columns at 960x540) and LOOK for: pieces under the head card / PIP, empty halves (add a terminal, lane or badge), a FULL-layout title over the cap, stamps over GIFs. Kevin reviews from ~/Downloads; keep the project for re-renders.

Learnings

  • 2026-09-21 v1 → v2 (JEV intro): dark hook + "?" = wrong for long-form ("it just needs to hook the user into the actual topic"); bottom timeline strip = "truly pointless". Both removed; slot/head card grew to 950 tall. Approved: layout switching, phrase captions on the line, X-marks over CapCut/Premiere on the spoken word, GitHub fork grid on "hundreds", thumbnails on "three video updates", Jev slam + rays, fake timeline, workflow nodes, code tree, demo card + "INSANE" stamp. Then: SPLIT named the favourite layout for talking.
  • HEVC .mov from Kevin's recorder: transcode to h264 30fps first (cv2 reads sequentially, seeks only at a RANGE start). 1572 frames render in ~3 min across 4 chunks.
  • Long-form Whisper: small.en (base mishears more on a long clip).
  • 10-minute clips: ~60–90 sections; ~0.15 s/frame/process → 18k frames ≈ 12 min on 4 chunks.
  • 2026-09-25 VIKTOR intro (examples/viktor-intro-longform/render.py): Kevin's raw clip had logo SLATES baked in (black frame + product logo, no face) at 35.4–37.9 s and 75.5–78.0 s → the head card rendered black. Scan every source for dark runs first (frame[::8,::8].mean() < 40) and put those spans on "SOLO" (any non-SPLIT/FULL/HERO name = full-width slot, no head, no PIP); boundaries must land on the first real frame. Name-drop reveal + five-fields board went there. Also: wordmark() clips tall Oswald digits / its sub overlaps at big sizes → use a textbbox-sized big_num() + a chip; Oswald has no ✓ glyph (Menlo does, so terminals are fine); white-bg Wikimedia logos need a white-trim crop before logo_badge(punch=False, fill=WHITE).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Longform Animated Talking Head AI skill do?

Edit a LANDSCAPE (16:9, YouTube long-form) talking-head clip Kevin records at his desk into a fast-paced animated hyper-edit — 1920x1080, split layout by default (animation slot left, his head in a ringed card right) with full-bleed and hero-slot layouts as accents, motion-graphic cards, real logos, Giphy reaction GIFs, YouTube thumbnails he drops in, kinetic phrase captions on an accent line, punch/shake on the slot only. No HeyGen. Use when the user hands over a landscape/long-form/YouTube talking-head video (intro, full episode, tutorial) and asks to edit it; vertical shorts stay with sp...

Why use Longform Animated Talking Head on TypingMind?

Because you install it once and use it with any model. Longform Animated Talking Head 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 Longform Animated Talking Head in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kevinbadi/open-edits/tree/main/skills/longform-animated-talking-head. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Longform Animated Talking Head?

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 Longform Animated Talking Head?

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

Is the Longform Animated Talking Head AI skill free?

Yes. It is published on GitHub by kevinbadi 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇