Render Myth Vs Fact logo

Render Myth Vs Fact

OrganizationPopular
gooseworks-ai
render-myth-vs-fact

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namerender-myth-vs-fact
Stars
1.2K
Forks
208
Bundled files
14
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 14 bundled files

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

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Render Myth Vs Fact 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.
  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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/capabilities/render-myth-vs-fact .claude/skills/render-myth-vs-fact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Myth Vs Fact 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 Render Myth Vs Fact 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 Render Myth Vs Fact 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.

render-myth-vs-fact

The free, deterministic renderer for the myth-vs-fact video ad format — the calm, sound-off-safe kinetic-typography explainer that busts N common myths and hands the viewer a credible resolution. Red-strike MYTH cards flip to teal-check FACT cards over a calm-authority VO, then a "what actually works" turn + an optional proof reveal + a punch line + a static brand end card.

Every on-screen word is a deterministic HTML hyperframe — no AI image/video gen, no b-roll, no character. Visuals cost $0. The only metered spend is upstream (VO + Whisper word-timestamps + an optional music bed), gated to its own capabilities. This capability OWNS the whole FREE assembly: beat-snap → render → captions → mix → burn → master. Iterate the cut for free; re-roll only the offending paid audio beat.

It ports the validated build from the Clinikally "acne myths" run — brand-neutralised, config-driven, and portable (no /Users, no clients/; everything via --config + --work-dir).

SOUND-OFF SAFE is the whole point: every claim is legible on-screen and the VO only reinforces it. VO-FIRST: render the VO, extract Whisper word onsets on the RENDERED audio, then snap every beat boundary + strike wipe + reveal to those onsets.

The 8-beat spine (roles)

hook → 3× myth-fact (the flip triad — identical grammar so it reads as a pattern) → turn (the "what actually works" pivot) → proof (optional actives/proof reveal, omit if empty) → punch (full-frame closer) → end-card (the static brand PNG). Each beat carries its role, duration, and its copy; ONE role template renders any pair.

Scripts (free — Python + Playwright + ffmpeg, no paid calls)

  • scripts/beat_snap.py — VO-first alignment. FAL Whisper word-timestamps on the RENDERED VO → re-snap every beat boundary to the nearest word onset. Writes beat-manifest.json + whisper/words-flat.json into the work dir. --no-whisper keeps the config durations un-snapped for a fully offline run.
  • scripts/render_beats.py — the deterministic renderer. Per mg beat: pick the role template under hyperframes/, inject the beat's copy + palette + fonts as window.BEAT, drive window.renderAt(t) frame-by-frame via Playwright, screenshot each frame → ffmpeg at EXACTLY the configured fps (default 25/1). The end-card beat is built from the pre-supplied brand PNG (scale/crop + a ~0.35s fade-up) — never generated per run.
  • scripts/make_captions.py — karaoke .ass from the manifest + Whisper words. ≤3 words per cue; close on a >0.4s gap / beat-window edge / sentence-ending punctuation. Captions are burned ONLY in caption-allowed windows; the proof + end-card beats are suppressed.
  • scripts/compose.py — the assembler: concat the beats → mix VO + optional music (music −20 dB, amix normalize=0, ~0.8s tail fade) → burn the .ass LAST → master mp4.
  • scripts/config.example.json — the shape of the brand config the recipe binds (the brand-neutralised Clinikally values as a worked reference).
  • scripts/hyperframes/ — the bundled hyperframe scaffold: _shared.css (palette-tokened tokens + card/tag/fact/pill/chain type), _shared.js (the initRenderer / springScale / buildLineStrikes + strikeLines per-line-strike / popIn / revealWords helpers + config injection), and one template per role (beat-hook.html, beat-myth-fact.html, beat-turn.html, beat-proof.html, beat-punch.html).

Inputs (all via --config + a runtime work dir — NO hardcoded paths)

config.json carries: fps (25) / width / height; vo + optional music + mix{music_db:-20, tail_fade:0.8}; palette (the five CSS-var tokens bg, myth_strike, fact_accent, headline_ink, accent); brand_name; display_font; end_card_png + end_card_fade; caption_style; suppress_beats; and beats[] — each {n, role, duration, captions, cues{...}} plus the role's copy:

  • hook: eyebrow, hook_line, emphasis, strike_word
  • myth-fact: myth_index, myth_line, and either fact_line (a [bracketed] phrase becomes the accented payload) OR fact_clauses[] (a staggered clause chain)
  • turn: turn_slate, turn_sub ([brackets] → emphasis)
  • proof: proof_eyebrow, proof_items[] ({name, badge}), proof_footnote
  • punch: punch_line
  • end-card: none (built from end_card_png)

The recipe's myth_fact_pairs, hook_line, turn_slate, punch_line, palette, end_card_png, and optional actives_or_proof map onto these beats 1:1. See config.example.json.

Craft rules (load-bearing — faithful to the source molecule)

  • MYTH strikethrough is PER-LINE. Measure each wrapped line box (Range.getClientRects, deduped to one rect per visual line) and lay one red bar at each line's vertical MIDDLE, driven as ONE continuous L→R sweep. A single fixed-Y rule reads as an underline the moment the headline wraps.
  • The end card is the pre-built brand PNG — NEVER generated per run. Scale/crop to the canvas with a short fade-up; it carries its own baked logo + claim + CTA.
  • Animation is a pure function of beat-local time — no setTimeout, no CSS keyframes — so Playwright seeks frame-exact and the render is fully reproducible.
  • Every beat mp4 is exactly the configured fps (25/1). render_beats.py enforces + warns; a mismatch makes the concat demuxer silently drop frames.
  • All text fits the 88% safe area at the ~15% spring-overshoot PEAK, not at rest.
  • Captions burned LAST, ≤3 words/cue, closing on >0.4s gap / window edge / sentence end. The ASS Events Format: line MUST carry the Name field or every cue gets a leading-comma artifact. Suppress the proof/footnote + end-card beats (two text layers at one spot both go unreadable).
  • Mix constants are validated — music −20 dB under the VO, amix normalize=0 (with normalize on the bed pumps), ~0.8s tail fade. Sound-off must still work without the bed.
  • Keep the MYTH triad's flip grammar + internal timing identical so it reads as a pattern (anaphora).

Requires

  • Python 3 + Playwright chromium (pip install playwright && playwright install chromium) for the frame-exact hyperframe render, and ffmpeg/ffprobe on PATH. If Playwright is unavailable, compose.py + make_captions.py (the concat / mix / caption path) still run; only render_beats.py needs the browser.
  • watch (QC the final master — the red strike crosses the vertical MIDDLE of EVERY wrapped myth line, the VO is intelligible, captions are legible with no card collision, suppression is correct on the proof + end-card beats, framerate is uniform 25/1, no clipping, every claim is legible sound-off). The recipe gates the paid create-vo-eleven (VO), create-music-elevenlabs (bed), and FAL Whisper calls — this capability itself makes NO paid calls.

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 Render Myth Vs Fact AI skill do?

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word on...

Why use Render Myth Vs Fact on TypingMind?

Because you install it once and use it with any model. Render Myth Vs Fact 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 Render Myth Vs Fact in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-myth-vs-fact. 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 Render Myth Vs Fact?

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 Render Myth Vs Fact?

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

Is the Render Myth Vs Fact AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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