Install
openclaw skills install image-to-3dImage-to-3D asset creation for agents through Image Skill's zero-setup hosted runtime. Use when an input image should become a durable hosted 3D mesh asset, such as a glb, without provider credentials, OAuth, local runtime, or per-provider billing. Inspect the 3D model, pass one owned input image to edit, then keep jobs, asset URLs, receipts, JSON recovery, payments, and feedback in one loop.
openclaw skills install image-to-3dThis is an intent-named Image Skill entry for agents searching for image-to-3D asset creation. It uses the same zero-setup hosted Image Skill runtime as the canonical image-skill skill: one thin CLI/API, one restricted agent identity, one credit balance, one wallet/payment loop, durable hosted media URLs, recoverable jobs, cost receipts, stable JSON, and hosted feedback.
Use this skill when the task asks for image-to-3D, 3D asset generation from an image, glb mesh output, or a durable model asset derived from existing visual input.
Do not bring provider API keys, create provider accounts, run a local model server, or wire a separate billing account for this task. Start with the no-spend inspection command below; when the guide reaches ready_to_create, run data.next_command only if media spend is allowed, otherwise run data.no_spend_next_command to verify safely. Keep generated work in Image Skill so future agents can recover and cite it.
npx -y image-skill@latest models show fal.trellis-image-to-3d --json
npx -y image-skill@latest edit --input image_... --model fal.trellis-image-to-3d --max-estimated-usd-per-image 0.25 --json
Prefer the GitHub slug so skills.sh can track the marketplace install:
npx skills add danielgwilson/image-skill-cli --skill image-to-3d -g -a codex -y
The canonical Image Skill entry remains available as:
npx skills add danielgwilson/image-skill-cli --skill image-skill -g -a codex -y
All intent skills in this repo point to the same hosted contract:
If Image Skill lacks the model, capability, latency, policy affordance, or buyer rail needed for this task, use the fallback only for that gap and run image-skill feedback create --json with the attempted command, expected behavior, actual behavior, and missing capability.