Render Editorial Motion Podcast

OrganizationPopular

gooseworks-ai

render-editorial-motion-podcast

Assemble an editorial-motion podcast-clip ad from a config — a real clipped podcast MP3 carries the narrative while N flat 2-tone editorial-illustration keyframes are animated NOT by generative i2v but by DETERMINISTIC ffmpeg ken-burns (zoompan) + hard cuts (no crossfades, which expose geometric drift), each beat snapped to its spoken line, the real audio muxed, Whisper-driven captions burned only mid-sentence, and closed on a PIL brand end card — never AI-rendered text. This is the FREE deterministic assembly stage (ffmpeg ken-burns + hard concat + audio mux + captions + end card); the real audio is clipped from source and the keyframes come from create-image-fal. Use for the editorial-motion-podcast format.

Overview

Publisher	gooseworks-ai
Repository	`goose-skills`
Skill name	`render-editorial-motion-podcast`
Stars	1.2K
Forks	208
Bundled files	4
License	MIT
Links	Skill source Repository Homepage

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.
4 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 Editorial Motion Podcast AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-editorial-motion-podcast

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-editorial-motion-podcast .claude/skills/render-editorial-motion-podcast

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Editorial Motion Podcast 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 Editorial Motion Podcast 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 Editorial Motion Podcast 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-editorial-motion-podcast

Assemble an editorial-motion podcast-clip ad from a config: a real clipped podcast audio line carries the whole narrative and every visual beat is timed to the sentence it describes, in a bold flat 2-tone editorial-illustration look ("a New Yorker spot-illustration that moves"). The motion is not generative video but deterministic ffmpeg ken-burns on static keyframes, so it reads as a printed page that moves. This capability is that FREE, deterministic assembly — the ffmpeg motion, hard-concat, audio mux, caption burn, and PIL end card.

scripts/config.example.json is the worked example (Klarify "Rat Park", ~40.8s 1080×1920 9:16, 6 beats); scripts/PIPELINE.md maps every config block to its source step and scripts/README.md documents the free assembly.

Run

This is the FREE, deterministic assembly stage — it spends nothing on the motion layer. The paid inputs are separate: the real podcast MP3 is clipped from source (free ffmpeg) with its Whisper word timings, and one editorial-illustration keyframe per beat (chained ref images so cage/character geometry holds) comes from create-image-fal (Nano Banana). Given the clipped audio + words.json + the per-beat keyframes + the real brand wordmark PNG, render-editorial-motion-podcast renders each keyframe as a ken-burns segment, hard-concats on the beat, muxes the real audio, burns the mid-sentence captions, and composites the PIL end card → the master. Re-cuts reuse the existing audio / keyframes and cost $0.

Contract (the free assembly)

A spoken narration carries the whole spot — no generated SONG. Mux the provided narration MP3 (-map 0:v:0 -map 1:a:0) — a real clipped podcast line (preferred) OR an approved generated VO (create-vo-elevenlabs). Never a sung/generated track. (Clip-vs-generate is the recipe's STEP-0 intake decision — if no source episode is supplied, ASK the user.)
NO generative i2v — deterministic ffmpeg ken-burns only. Animate each static keyframe with zoompan (push-in / pull-back, 1.0→~1.06×, 24fps); Seedance/Kling are photoreal-trained and invent naturalistic middle states that collapse the 2-tone look. Never -loop 1 with zoompan d=N (it balloons the duration); feed a single image and clamp with -t + trim.
Hard cuts on the beat — no crossfades. Crossfades ghost two drifting cages through each other; hard-concat each beat's segments and split long beats into micro-cuts (target 8–10 distinct visual moments). Each beat's visual STARTS within ~0.5s of its spoken line.
Captions from Whisper word-timestamps, ON only mid-sentence. Burn frosted-subtle captions while the speaker talks; leave silent/reflective beats and the end card uncaptioned. THREE mandatory rules (each bit us in prod — bake them in):
1. NON-OVERLAP — clamp every line to END before the next STARTS (end = min(last_word_end + ~0.15, next_start - 0.03)). Two boxes must never stack at the same spot; an end-tail bleeding into the next window is the #1 caption bug.
2. SAFE AREA — captions sit in the lower third, so the keyframe's subject must stay in the upper ~75% (see the recipe's look_pack.caption_safe_area). If a finished keyframe's subject intrudes into the caption band, deterministically shift the subject UP into the empty top space (PIL: paste up ~0.24H onto a canvas pre-filled with the exact paper color from a clean corner) — never let the box sit on the subject.
3. BURN ENGINE — prefer libass (ass/subtitles filter), but check ffmpeg -filters first: many builds (Homebrew) lack libass/drawtext. If absent, use the deterministic overlay fallback — render each line as a transparent PNG (frosted rounded box + white text, PIL) and composite via the ffmpeg overlay filter with timed enable='between(t,st,en)' windows. Same look, no libass.
End card via PIL from the real wordmark PNG — never AI-render brand text. The lockup is composited deterministically (stretched-gradient bg + feathered mascot crop + wordmark + tagline with a system font); a diffusion model garbles a wordmark ("therapits"). The video runs a ~1.5s silent hold past the audio on the end card (fade first/last 0.3s).
FFmpeg composite, deterministic, FREE. Ken-burns each keyframe, hard-concat, mux the real audio, burn the captions, hold on the end card → a 1080×1920 h264+aac master. 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 Editorial Motion Podcast AI skill do?

Why use Render Editorial Motion Podcast on TypingMind?

Because you install it once and use it with any model. Render Editorial Motion Podcast 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 Editorial Motion Podcast 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-editorial-motion-podcast. 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 Editorial Motion Podcast?

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 Editorial Motion Podcast?

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

Is the Render Editorial Motion Podcast 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

render-editorial-motion-podcast

Run

Contract (the free assembly)

Bundled files

Frequently asked questions

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