Imported from akillness/jeo-skills (.agent-skills/unirig/SKILL.md). Install upstream with npx skills add akillness/jeo-skills --skill unirig. Copyright stays with the author (MIT).

unirig — automatic 3D rigging (skeleton → skin → merge)

Keyword: unirig · auto-rigging · skeleton prediction · skinning weights

Only rig assets the user is licensed to modify. UniRig is MIT-licensed, but its checkpoints, the Rig-XL/VRoid/Objaverse data, and the user's own models each carry their own terms.

UniRig is a two-stage autoregressive rigging framework: a GPT-like transformer predicts a topologically valid skeleton from mesh geometry, then a bone-point cross-attention model predicts per-vertex skinning weights. A third step merges the predicted rig back onto the original (full-resolution, textured) asset.

The single most common failure is skipping the merge stage or merging the wrong file — see Step 5. The second most common failure is an environment that silently lacks CUDA extensions.

When to use this skill

• The user wants a static 3D character/creature/prop turned into a rigged asset
• The user needs a skeleton only, or skin weights for a skeleton they already edited
• The user needs UniRig installed and verified on a CUDA machine, or wants to know up front that their machine cannot run it
• The user wants to batch-rig a directory of models
• The user is choosing between UniRig, its successor SkinTokens, or classical/hosted riggers
• The user has a rigged output and wants to verify that joints and skin weights actually landed

Instructions

Step 1: Capture the intake packet and pick the stage

Collect four facts before running anything:

• Asset: file format (.obj, .fbx/.FBX, .dae, .glb, .gltf, .vrm), poly count, single model or directory, textured or not
• Goal: skeleton only, skin only, or a fully rigged deliverable
• Hardware: NVIDIA GPU + CUDA version, VRAM, OS (no CUDA ⇒ no inference; route out)
• Constraint: install budget (UniRig needs spconv, flash_attn, torch_scatter, torch_cluster, bpy), and whether hand-authored control over the skeleton is required

Routing rules:

1. Static mesh, no skeleton yet → skeleton stage (--stage skeleton)
2. Skeleton exists (predicted or hand-edited) → skin stage (--stage skin)
3. Deliverable must keep the original geometry/materials → merge stage (--stage merge)
4. All three in one shot → --stage all (the default of scripts/rig.sh)
5. No CUDA GPU, or the user needs artist-controlled naming/IK/constraints → route out (see references/route-outs-and-troubleshooting.md)

Step 2: Check the environment before installing anything

bash scripts/doctor.sh            # human-readable readiness report
bash scripts/doctor.sh --json     # machine-readable, for agent branching

doctor.sh reports Python 3.11, torch + CUDA availability, spconv, torch_scatter, torch_cluster, flash_attn, bpy, trimesh, the UniRig checkout, its launch/inference scripts, and Hugging Face reachability. It exits 1 when a blocking item is missing, so an agent can stop before promising a rig that cannot run. Use --unirig-home <path> (or UNIRIG_HOME) when the checkout is not at ~/.cache/unirig/UniRig.

Step 3: Install the skill and, when the machine qualifies, the upstream repo

npx skills add https://github.com/akillness/jeo-skills --skill unirig

bash scripts/install.sh --repo-only          # clone/update UniRig only
bash scripts/install.sh --cuda cu121         # clone + create venv + install deps
bash scripts/install.sh --cuda cu121 --torch 2.4.0 --vrm

The installer is deliberately conservative:

• it never installs CUDA-only wheels on a machine without nvidia-smi unless --force is passed;
• spconv-<cuda> and the PyG wheel index are derived from --cuda/--torch, matching the upstream README instead of guessing a single pinned wheel;
• flash_attn is attempted last and a failure is reported, not swallowed — see the troubleshooting reference for the source-build path;
• --vrm additionally registers the bundled Blender VRM add-on.

Full dependency detail lives in references/environment-and-install.md.

Step 4: Plan the run with a dry run, then execute

# print the exact upstream commands without executing them
bash scripts/rig.sh --input examples/giraffe.glb --output results/giraffe_rigged.glb --dry-run

# run the whole pipeline (skeleton → skin → merge)
bash scripts/rig.sh --input examples/giraffe.glb --output results/giraffe_rigged.glb

# one stage at a time
bash scripts/rig.sh --stage skeleton --input model.glb --output results/model_skeleton.fbx
bash scripts/rig.sh --stage skin --input results/model_skeleton.fbx --output results/model_skin.fbx
bash scripts/rig.sh --stage merge --source results/model_skin.fbx --target model.glb \
  --output results/model_rigged.glb

# whole directory (skeleton and skin stages only — upstream merge takes one file pair)
bash scripts/rig.sh --stage skeleton --input-dir assets/ --output-dir results/skeletons/

rig.sh is a thin, honest wrapper over launch/inference/generate_skeleton.sh, generate_skin.sh, and merge.sh: it validates the input suffix, derives intermediate <input>_skeleton.fbx / <input>_skin.fbx paths next to the final output (override with --skeleton-out / --skin-out), runs the stages in order from UNIRIG_HOME, and fails when an expected artifact is missing instead of reporting a rig that was never written. --seed, --faces-target-count, --num-runs, --add-root, --force-override, --skeleton-task, and --skin-task are passed straight through to upstream with upstream's own defaults.

Step 5: Respect the two merge rules

1. Merge the skin file, not the skeleton file. merge.sh --source <skeleton>.fbx produces an armature with no skinning weights. Use the *_skin.fbx output for a deliverable rig.
2. Fix the skeleton before skinning. Skin quality collapses when bones are missing (tails, wings, extra limbs). Hand-edit the predicted skeleton in Blender, then re-run --stage skin on the edited FBX. Different --seed values produce different skeleton proposals — cheap to sample a few before committing.

Stage flags, defaults, config files, and the tmp/ npz cache are documented in references/inference-pipeline.md.

Step 6: Verify the deliverable, do not assume it

python3 scripts/inspect_glb.py results/model_rigged.glb
python3 scripts/inspect_glb.py results/model_rigged.glb --json

inspect_glb.py is stdlib-only (no torch, no Blender): it parses the GLB/glTF JSON chunk and reports meshes, nodes, skins, joint counts, animations, and whether any mesh primitive carries JOINTS_0/WEIGHTS_0 attributes. It exits 1 when the file has no skin, which is exactly the "merged the skeleton file by mistake" case. For FBX outputs, verify in Blender or with bpy (see the troubleshooting reference) — FBX is binary and not parseable stdlib-only.

Step 7: Training and datasets (only when asked)

Training, Rig-XL/VRoid data layout, the raw_data.npz key schema, and the Rignet validation task live in references/training-and-datasets.md. Do not start a training run for a request that only needs inference — the published checkpoint is downloaded automatically on first inference.

Examples

Example 1: "Rig this GLB character for me"

doctor.sh → rig.sh --dry-run to show the plan → rig.sh → inspect_glb.py to prove the output has skins and joints.

Example 2: "The tail has no bones"

Do not re-run skinning on the bad skeleton. Re-sample with another --seed, or edit the skeleton FBX in Blender, then run --stage skin on the edited file and re-merge.

Example 3: "I'm on a MacBook"

doctor.sh exits blocking. Say so plainly and route out to a CUDA machine/cloud GPU, the hosted Tripo rigging service, or classical Mixamo/AccuRig/Rigify — do not pretend a CPU fallback exists.

Example 4: "Which is better, UniRig or SkinTokens?"

SkinTokens is the same lab's successor (unified autoregressive skin tokens, RL-trained, reported 98–133% skinning and 17–22% bone-prediction gains). Recommend it for new work; keep UniRig when the user needs its released checkpoint, its Rig-XL tooling, or an already-working environment.

Checklist

1. Capture the asset/goal/hardware/constraint packet before touching a shell.
2. Run doctor.sh first; report a blocking environment instead of installing blindly.
3. Never install CUDA-only wheels on a machine without an NVIDIA GPU.
4. Dry-run the pipeline and show the exact upstream commands before a long GPU run.
5. Fix the skeleton before skinning; sample seeds when the topology looks wrong.
6. Merge the *_skin.fbx, never the *_skeleton.fbx, into the original asset.
7. Verify the deliverable with inspect_glb.py (GLB) or Blender (FBX) — never claim success from a command exit code alone.
8. Route out honestly to SkinTokens, hosted services, or classical riggers when UniRig is the wrong tool.

References

• references/environment-and-install.md — Python 3.11, torch/CUDA matrix, spconv/PyG/flash_attn pitfalls, checkpoints, VRM add-on
• references/inference-pipeline.md — every upstream flag and default for extract/skeleton/skin/merge, configs, tmp/ npz cache
• references/training-and-datasets.md — Rig-XL/VRoid data, raw_data.npz schema, config layering, training and Rignet validation tasks
• references/route-outs-and-troubleshooting.md — when not to use UniRig, and the recurring install/runtime errors
• scripts/doctor.sh — readiness report (--json, exits 1 when blocked)
• scripts/install.sh — skill plugin + upstream clone/venv/deps
• scripts/rig.sh — stage-aware wrapper with --dry-run
• scripts/inspect_glb.py — stdlib GLB/glTF rig verifier
• UniRig repository · paper · checkpoint · SkinTokens successor