---
name: animated-asset
description: Create a fully rigged, animated voxel creature from a one-line idea — fal text-to-image → Hunyuan-3D → C++ voxelizer → headless Blender rig + 13 animation clips → glTF. Use when asked to add, generate or create a new creature, animal, animated asset or voxel model (e.g. "add a corgi", "make a wolf", "generate a new animal").
title: animated-asset
canonical_url: https://skilld.dev/gh/rehan-remade/animated-asset-skill
last_updated: 2026-09-28T07:11:22.000Z
---

> Index of every page on this site: [llms.txt](https://skilld.dev/llms.txt)

> **Skill from skilld.dev.** Follow the user's requested action.
> If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it.
> Otherwise, follow the instructions below for this session. You do not need to install anything.
>
> Supporting files, fetch one when the Skill refers to it: [references/fal-generation.md](https://skilld.dev/api/skills-raw/rehan-remade/animated-asset-skill/animated-asset/references/fal-generation.md), [references/rigging-traps.md](https://skilld.dev/api/skills-raw/rehan-remade/animated-asset-skill/animated-asset/references/rigging-traps.md).
>
> If the user asked to install this Skill, run `npx skilld install rehan-remade/animated-asset-skill/animated-asset`. Install writes the Skill files into the project, so every session loads them.
>
> ## Fork workflow
>
> A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation.
> 1. Check `./skills/animated-asset`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target.
> 2. Read [source metadata](https://skilld.dev/api/v1/skills/rehan-remade/animated-asset-skill/animated-asset) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit.
> 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call:
>
> ```sh
> git init --quiet "$temporary_dir"
> git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit"
> git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD
> ```
>
> Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop.
> 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/animated-asset`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md.
> Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical.
> 5. In the project root, run `skilld install ./skills/animated-asset --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/animated-asset --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure.
> 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks.

# Animated asset pipeline

Turns a one-line idea ("a cheerful corgi") into a rigged voxel creature with 13 animation
clips, exported as glTF. Four stages, each with a QA gate. Run them in order; never skip a
gate — a bad early artifact wastes every later stage.

Exactly ONE gate is a manual user pick — present the material, then stop and wait:
  1. concept selection (stage 1): the user picks one of four generated concept images.
Every other gate is automated; ask the user only when one fails twice in a row.

## Layout

```
assets/<Name>/                      (PascalCase name, e.g. Corgi)
  concept/candidate_{1..4}.png      stage 1 candidates + concept_sheet.png + concept.json (CDN urls)
  <Name>_mesh.glb                   stage 2: Hunyuan-3D output
  <Name>.vox                        stage 3: MagicaVoxel voxels, 5 cm cells, facing -Y
  previews/<Name>_vox_{front,side,top}.png
  layout.json, <name>_rules.py      stage 4: derived body plan + voxel->bone rules
  <Name>_rigged.blend               stage 4: the rig, all clips stashed in the NLA
  <Name>_animated.glb               stage 4: EXPORT — every clip as a glTF animation
  report.json, run.log              guards, timings, everything that was run
```

Tools live in this repo: `tools/pipeline/concept.py`, `tools/pipeline/run_creature.py`,
`tools/voxelizer/` (build once with CMake), `tools/rig/` (Blender scripts + clip modules).
Requirements: Blender 5.2 LTS on PATH (or `BLENDER=/path`), a fal API key (`FAL_KEY` or
`~/.fal/key`), Python 3.10+ with Pillow for the contact sheets.

## Stage 1 — concept image (fal text-to-image)

Model: `bytedance/seedream/v5/pro/text-to-image`. Use the `fal-ai` MCP tools (`run_model` /
`submit_job` + `check_job`) when that server is connected; otherwise run

```
python tools/pipeline/concept.py <Name> --idea "a cheerful corgi" --len-m 0.9 \
       --anatomy "<2-3 sentences of BLOCKY anatomy>" --colors "<flat colour regions>"
```

Prompt rules (the template is in `concept.py` and [references/fal-generation.md](https://skilld.dev/api/skills-raw/rehan-remade/animated-asset-skill/animated-asset/references/fal-generation.md)):
depict the animal AS voxel art ("large chunky visible cubes, MagicaVoxel style, roughly N cubes
long"), side three-quarter view, legs clearly separated from each other and the ground, plain
light-grey background, whole model in frame. Thin parts (tails, whiskers, antler tines) must be
thickened into slabs or omitted — they shred at 5 cm. `--len-m` is the real-world length;
cube counts in the prompt follow from it (1 cube = 10 cm in the prompt, 5 cm in the voxels).

**Auto QA first:** view each candidate. Reject any that are not blocky axis-aligned cubes,
lack a plain background, crop the body, fuse the limbs, or grow thin appendages. Fewer than 2
survivors → regenerate before involving the user.

**USER PICK (mandatory):** show `concept_sheet.png` and STOP until they choose. Keep the picked
candidate's fal CDN URL — stage 2 takes it directly.

## Stage 2 — image to 3D (Hunyuan-3D), Stage 3 — voxelize, Stage 4 — rig + animate

One command runs all three and writes the report:

```
python tools/pipeline/run_creature.py <Name> --image-url <picked CDN url> --len-m 0.9
```

What it does, and the gates it applies:

- **Mesh** — `fal-ai/hunyuan-3d/v3.1/pro/image-to-3d`, `generate_type: Normal`, PBR on,
  `face_count: 100000` (plenty for voxels, 4x smaller download). ~2.5 min. If the rear comes out
  wrong (Hunyuan hallucinates unseen views), pass a back-view image via `back_image_url`.
- **Voxelize** — `tools/voxelizer` cuts the mesh at 5 cm cells (`--long-axis` = 2 x metres x 10).
  Facing is detected from the voxel grid (the taller end is the head) and the yaw is swept
  automatically. **Check `previews/<Name>_vox_side.png`: the head must be at the image LEFT.** The
  heuristic fails on animals whose rump is higher than the head (rhino, boar) or whose tail is
  raised (skunk); fix those by re-running the voxelizer with `--yaw` and passing `--vox`.
- **Rig** — `tools/rig/voxrig.py` derives the body plan from the grid (leg zone = bottom layers
  still split into separate blobs, tail = narrow trailing slices, head = whatever rises at the
  front) and writes `<name>_rules.py` (voxel -> bone). `generic_quad_build.py` builds a 14-bone
  quadruped rig with rigid per-bone weights, authors Idle / Walk / Gallop / Graze / Alert, then
  exec's every module in `tools/rig/clips/` (Trot, Jump, Sit, Sleep, Shake, Stretch, LookAround,
  Rear) and exports the glTF. Gaits are scaled by leg length; the same maths runs on a fox and a hippo.

**QA gate (numeric, never visual):** `report.json` → `stages.rig.guards`. `DIRECTION GUARD: OK`
means every planted foot sweeps tailward and touches the ground in the Walk; `suspension: YES`
means the Gallop has an airborne frame (legs one voxel tall can't — the clip still exports).
Every extra clip prints its own `EXTRA CLIP <Name>: N frames | guard: OK ...`. Read
[references/rigging-traps.md](https://skilld.dev/api/skills-raw/rehan-remade/animated-asset-skill/animated-asset/references/rigging-traps.md) before touching any rig code — it is the
accumulated list of things that silently produce wrong animation, including why vision-model
judges cannot be trusted to tell a forward walk from a backward one.

Optional: add `--video` to render a 3/4-view preview MP4 per clip into `previews/`.

## Stage 5 — use it

`<Name>_animated.glb` imports into Godot, Unity, Unreal and three.js with all clips as named
animations (`Idle`, `Walk`, `Gallop`, ... same names on every animal, so a state machine can key on
them). The `.blend` keeps the rig editable. (tiny-voxel additionally bakes the clips into its own
rigid-part runtime format; that baker is engine-specific and not part of this repo.)

## Wrap up

Report one line per stage: model used, voxel dims and count, clips exported, and any gate that
needed a retry. Leave the unpicked candidates in `concept/` — never delete.

## Scope

Quadrupeds only. The rig is one body plan with derived proportions; bipeds, birds, snakes and
anything with a moving tail need their own build script. Blocky voxel animals are the easy case
for both meshing and weights — nothing here claims organic characters.