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render-3d-character-explainer

Assemble an animated character-explainer video ad (~77s, 9:16, any art style: Pixar-style 3D, claymation, anime, paper-cut, felt) on an \"N types of X\" listicle spine: one recurring human protagonist plus one persona character per list item.

Install / Use

npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Category

Other

Supported Platforms

Universal

Our assessment of render-3d-character-explainer

render-3d-character-explainer scores 85/100 on our quality scale, 188th of 283 Other skills we index.

Its SKILL.md is 8.8 KB long, well organised into 8 sections and no code examples: a thorough specification that gives an agent plenty to work with.

With 1,222 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
13/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 33 days ago, so render-3d-character-explainer is actively maintained.
  • Our last check on 2026-10-12 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

render-3d-character-explainer compared with similar skills

All 4 of these similar skills score higher than render-3d-character-explainer; compare them before choosing.

SkillScoreStarsUpdatedFormat
render-3d-character-explainer (this skill)by gooseworks-ai851.2k33d agoSKILL.md
LocalAIby mudler10050.0ktodayMCP Server
algorithmic-artby anthropics100177.9k33d agoSKILL.md
pptxby anthropics100177.9k33d agoSKILL.md
designby nextlevelbuilder100137.2k11d agoSKILL.md

Frequently asked questions

How do I install render-3d-character-explainer?
Run npx skills add gooseworks-ai/goose-skills --skill render-3d-character-explainer. The install tabs above show the steps for each supported agent.
Which AI agents does render-3d-character-explainer work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is render-3d-character-explainer safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is render-3d-character-explainer still maintained?
The repository was last updated 33 days ago, so render-3d-character-explainer is actively maintained.

name: render-3d-character-explainer description: "Assemble an animated character-explainer video ad (~77s, 9:16, any art style: Pixar-style 3D, claymation, anime, paper-cut, felt) on an "N types of X" listicle spine: one recurring human protagonist plus one persona character per list item. Given per-scene clips, a scene timing table and narration, it trims, normalises, concatenates and mixes them, reusing the source ad's audio (restyle) or fresh per-scene voiceover over a music bed (original), loops a scene's keyframe when its clip failed, and burns captions last. Free, deterministic assembly (Python and ffmpeg, no paid calls); the recipe supplies the clips, keyframes, audio and captions. Use for the 3d-character-explainer format." status: active

render-3d-character-explainer

The free, deterministic renderer for the 3d-character-explainer video ad format — an animated character spot (art style is the user's choice; the demo was glossy Pixar-style 3D) built on an "N types of X" listicle spine, where a recurring human protagonist plus a locked cast of N persona characters (one per list item) carry a hook → "deeper story" → cast-reveal → one beat per list item → kicker → product test → relieved payoff. This capability is the FREE assembly stage only. All generative work (Nano-Banana cast anchors + per-scene keyframes, Kling-V3 i2v clips, ElevenLabs VO + music, or a source ad's audio reused verbatim) happens upstream in the recipe and is handed to this capability as files.

It ports the validated compose recipe from the worked example — a "six types" Pixar-style 3D listicle restyle (_render_full.sh — per-scene trim → normalize 1080×1920/fps30 → concat -c copy → mux the source audio, with a static-still fallback on any failed clip). The assembly is deterministic — iterate the cut for free, re-roll only the offending paid beat.

Choices

The recipe asks these of the user before any paid step; this capability assembles whatever comes back. None of them is fixed by the format.

  • visual_style — the art style of the anchors, keyframes and Kling motion prompts (Pixar-style 3D, claymation, anime, paper-cut, felt). Asked of the user; the demo used glossy Pixar-style 3D.
  • narrator — who tells the story and in whose POV the lines / captions are written. Asked of the user; the demo used an off-screen storyteller.
  • narrator_voice — the VO voice (ORIGINAL mode only). Asked of the user; the demo used one ElevenLabs narrator.
  • tone — cute/comedic, sincere, educational, dramatic, punchy; shapes the style prefix, the delivery and the script. Asked of the user; the demo was cute and comedic.
  • protagonist — the human's age, gender, look and outfit (locked via the world lock). Asked of the user; the demo used a young protagonist in a single tee.
  • music — the bed under the VO, or none (ORIGINAL mode → music_bed). Asked of the user; the demo used a light instrumental underscore.

Two modes

  • Restyle mode (audio_mode: "restyle", the reference run) — re-tell a finished source ad, beat for beat, as an animated character story in the chosen style and tone. The source ad's audio mix (VO + music bed) is reused VERBATIM (source_audio), and the per-scene target_sec table is inherited from the source's scene timing. No new VO or music is rendered. The trims must sum to the source audio length.
  • Original mode (audio_mode: "original") — the ad authors its own narration. Each scene carries a measured VO cue (scenes[].vo, target_sec = the ffprobe'd VO duration) which is concatenated into a VO track (loudnorm I=-14) and optionally mixed under a music_bed (loudnorm I=-26 then volume, amix normalize=0).

What it does (the deterministic recipe)

  1. Per-scene retime. Each i2v clip is trimmed to its scene target_sec and normalized to identical dims/fps/SAR (scale=W:H:force_original_aspect_ratio=decrease,pad=W:H:(ow-iw)/2:(oh-ih)/2:color=<pad>,fps=30,setsar=1). A clip shorter than its window is extended with tpad=stop_mode=clone; a longer one is -t trimmed. Decrease+pad (never crop) preserves the full 9:16 keyframe framing.
  2. Static-still fallback. For any scene whose clip is missing or failed to render, the scene's keyframe PNG is looped (-loop 1) for target_sec, so the master always assembles. Fallback scenes are printed at the end.
  3. Identical re-encode + concat. Every segment is re-encoded libx264 -crf 18 -pix_fmt yuv420p -r 30 even if already correct — a dims/framerate mismatch makes the concat demuxer silently drop frames — then concatenated via the concat demuxer (-c copy).
  4. Audio. Restyle: source_audio muxed verbatim (-map 0:v -map 1:a), clamped to the video length. Original: per-scene VO track (optional atempo, apad, -t clamp) → loudnorm → optionally mixed under the music bed.
  5. Captions last. make_captions.py emits a libass .ass (one cue per scene, start = scene_start + 0.08s, suppressed on any scene with no caption — e.g. a product/end-card beat carrying its own typeset copy). compose.py burns it as the final filter so captions sit on top. Word-level energy-pop captions (Whisper on the narration) are the recipe's upstream option — produce that .ass externally and point captions_ass at it; compose burns whatever .ass it's handed.

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

  • scripts/make_captions.py — emits the per-scene libass .ass from the SAME scene table compose reads, so caption windows stay in lockstep with the cut. Run before compose.py (or leave captions_ass unset / pointing at nothing to skip captions).
  • scripts/compose.py — the assembler: per-scene trim + identical 1080×1920/30fps re-encode (static-still fallback on missing clips) → concat → audio (restyle verbatim / original mix) → burn captions → master mp4.
  • scripts/config.example.json — the shape of the config the recipe binds (the "six types of laundry stains" restyle demo, a made-up brand, as a worked example; captions, audio and music are the demo's, never defaults).

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

config.json carries: audio_mode (restyle | original), scenes[] (each {id, clip, keyframe, target_sec, caption?, vo?, atempo?} where target_sec is the source-inherited window in restyle mode or the measured VO window in original mode, and keyframe is the static-still fallback source), source_audio (restyle), music_bed + music_volume + atempo (original), width/height (default 1080×1920), pad_color (letterbox colour), captions_ass, and caption_style. See config.example.json. pad_color defaults to neutral black; set it from the brand palette.

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

  • Restyle inherits the source timing. A restyle reuses the source ad's exact audio, scene order, and per-beat durations verbatim; only an original-mode remix authors its own VO + timing table. Merge any sub-1.5s flash scene into a neighbour upstream to avoid a dead micro-cut (the reference folded scene 7 into scene 8).
  • Normalize decrease+pad, never crop — the listicle's cast-reveal + per-persona framing must not lose edges; letterbox-pad to the canvas colour instead. Re-encode every segment to 30fps before concat, even if already correct, or the concat demuxer silently drops frames.
  • Static-still fallback is mandatory — Kling can 403 mid-run (a billing wall after a burst of successes, not a rate limit). Any failed clip loops its keyframe so the master still assembles; re-roll only the missing beat and recompose (free).
  • generate_audio was false upstream — Kling would otherwise invent its own dialog track; the real narration is muxed here separately. (This is the recipe's upstream call, not this capability.)
  • No AI-rendered brand text — the product-beat keyframe shows a BLANK-label box; the real wordmark/end-card copy is composited upstream, never AI-drawn. Suppress captions on any product/end-card beat (its typeset copy carries the message — two text layers at one spot are both unreadable).
  • Caption start = scene_start + 0.08s (avoids the caption flashing a frame before a cut).

Requires

watch (QC the final master — the human protagonist reads as the SAME person every scene (wardrobe/hair/lighting held), each persona is on-model, the cast-reveal lineup matches the N list items, the product beat shows the REAL box, narration lands beat-for-beat, and duration is within ±0.1s of the summed windows). The recipe gates the paid create-image-fal (cast anchors + keyframes), create-video-fal (Kling-V3 i2v), create-vo-elevenlabs, and create-music-elevenlabs calls to their own capabilities — this capability itself makes NO paid calls.

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryOther
Updated1mo ago
Forks208

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions
render-3d-character-explainer — Universal Skill: Install & Safety Check | SkillAgent