Agent skill

Scenario Meshy

by scenario-labs in scenario-labs/skills

A skill your agent uses when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB…

MITAuto-check passedGame Development

Install Scenario Meshy

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scenario-meshy .claude/skills/scenario-meshy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .claude/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/scenario-meshy .agents/skills/scenario-meshy && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .agents/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/scenario-meshy .cursor/skills/scenario-meshy && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .cursor/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/scenario-labs/skills.git --path skills/scenario-meshy

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/scenario-meshy .gemini/skills/scenario-meshy && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .gemini/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/scenario-meshy .github/skills/scenario-meshy && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .github/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add scenario-labs/skills --skill scenario-meshy -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install scenario-labs/skills scenario-meshy --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/scenario-labs/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/scenario-meshy .opencode/skills/scenario-meshy && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "scenario-meshy" agent skill from https://github.com/scenario-labs/skills/tree/main/skills/scenario-meshy into .opencode/skills/scenario-meshy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scenario-meshy", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: scenario-meshy
GitHub stars: 946
Token cost: ~1.6k tokens
SKILL.md length: 784 words
Files: 1
Skills in repo: 146
Repo updated: Oct 10, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB…

Works in 6 steps: search with target="models",… → model_schema_get with that id, then… → model_run with dry_run=true and… → …
Refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo
SKILL.md covers Overview, Quick reference, Generate raw, refine on purpose and Rig or animate, not both, plus 2 more sections
Calls npx

What it does

Scenario Meshy is an agent skill from scenario-labs/skills. Use when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB pipeline.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Game Development, covering Game assets and audio. It works with Model Context Protocol. The repository describes itself as: Get production-ready images, video, audio, and 3D from any AI agent: skills that pick the right model, price before spending, and keep characters and brands consistent through… The licence is MIT.

When your agent uses it

Refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo
1-4 multi-view angles
Retexturing an existing GLB
Remeshing to a target polycount

Example prompts

“/scenario-meshy”

Requirements

Node.js

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

search with target="models", query="meshy", public=true. Single-image route: model_meshy-7-img23d (a live hit at authoring time…
model_schema_get with that id, then upload_asset the character photo (see the scenario skill).
model_run with dry_run=true and parameters={"image": ["asset_photo"], "poseMode": "t-pose", "texturePrompt": "worn leather armor, muted…
asset_display the GLB; check silhouette and texture before spending further.
search for the animation member, model_schema_get, then model_run with parameters={"model": "", "heightMeters": 1.8, "actionId": }.
jobs_wait, asset_display, then asset_download for engine import.

What it can do on your machine

Read from SKILL.md and the folder at commit f6f8ab7. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- npx
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scenario Meshy loads about 1.6k tokens when it runs. Until then it costs about 117 tokens; SKILL.md has 784 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~117
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from scenario-labs/skills at commit f6f8ab7, republished under its MIT licence (© scenario-labs). 784 words, ~1,649 tokens.

Download SKILL.mdSave it as .claude/skills/scenario-meshy/SKILL.md (or your agent's skills folder).

name: scenario-meshy
description: Use when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB pipeline.
license: MIT

Scenario Meshy 3D

Overview

Meshy on Scenario is a toolchain rather than one model: image-to-3D generators (Meshy 7 Image to 3D, Meshy 7 Multi Image to 3D, Meshy T2 Smart Topology), Meshy 6 Text-to-3D, and GLB-in, GLB-out utilities (Retexture, Remesh, UV Unwrap, Rigging, Animation). Work runs as a pipeline: generate a mesh, refine it, then rig or animate, each stage its own model_run whose model parameter takes the 3D asset id the previous stage returned. Discover members with search and treat model_schema_get as the contract: members disagree on defaults as basic as enablePbr (true on Image to 3D, false on Multi Image and Retexture at authoring time).

Connection and the core loop: see the scenario skill in this repo; model-agnostic 3D work (viewer, capture, engine import): the scenario-3d skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

Members and their traps (names from the live schema, caps at authoring time):

Member	Core input	Watch for
7 Image to 3D	`image` (1-4)	`ultraMode` takes a single image only; raw mesh by default
7 Multi Image to 3D	`image` (1-4, first = front view)	remeshes in-run; `savePreRemeshedModel` keeps the raw GLB
T2 Smart Topology	`image` (1-4)	animation-ready topology; `targetPolycount` caps at 15,000
6 Text-to-3D	`prompt` (600 chars)	`shouldRemesh` off by default
Retexture	`model` + one style input	geometry untouched; keep inputs near 30K polys; `enableOriginalUv`
Remesh	`model`	`targetPolycount` 100 to 300,000, `resizeHeight`, `originAt`
UV Unwrap	`model`	rejects meshes above 44,000 faces; Remesh down first
Rigging	`model`, `heightMeters`	humanoid skeleton and skin weights
Animation	`model`, `actionId`	auto-rigs, then applies the clip

One silent rule runs through every texture control: an image beats text. On the generators textureImage overrides texturePrompt (the text is ignored, not blended), and both need shouldTexture on (Text-to-3D has no such toggle: it always textures). Retexture is stricter: exactly one of textStylePrompt, imageStyle, or multiviewImage (1 to 4 views, first is the front). textureResolution runs 2k, 4k, or 8k; at 8k the PBR maps come back at 4K with no emission map (Meshy 7 never produces one), and 8k pairs only with triangle topology when remeshing. poseMode takes lowercase "a-pose" or "t-pose": set one on any character headed for rigging.

Generate raw, refine on purpose

Keep shouldRemesh off on Meshy 7 Image to 3D (the schema recommends it): take the raw mesh at generation, then control polycount with Remesh, which owns targetPolycount, topology, resizeHeight in meters (0 keeps scale), and origin placement. On the generator, targetPolycount does nothing while shouldRemesh is off. ultraMode buys finer geometry for extra cost but accepts one input image; when hidden sides matter more than micro detail, spend the budget on up to four views instead. Costs spread widely (at authoring time the single-image Meshy 7 p50 ran three times the multi-image one), so dry_run before batches.

Show full SKILL.md (297 more words)Show less

Rig or animate, not both

Rigging and Animation are alternatives, not stages. Animation auto-rigs internally, then applies the clip named by actionId, a number from Meshy's animation library (ids listed at docs.meshy.ai/en/api/animation-library). Run Rigging alone when you want a rigged GLB for your own clips; go straight to Animation for a moving character, optionally setting postProcessOperation to change_fps (postProcessFps 24, 25, 30, or 60) or extract_armature. Both scale the rig from heightMeters, so pass the character's real height.

Worked example: photo to animated character

search with target="models", query="meshy", public=true. Single-image route: model_meshy-7-img23d (a live hit at authoring time: re-discover each session).
model_schema_get with that id, then upload_asset the character photo (see the scenario skill).
model_run with dry_run=true and parameters={"image": ["asset_photo"], "poseMode": "t-pose", "texturePrompt": "worn leather armor, muted palette", "enablePbr": true} for the estimate; then re-run with wait=false and jobs_wait with the job id, re-called with pending_job_ids on timeout, never a second model_run.
asset_display the GLB; check silhouette and texture before spending further.
search for the animation member, model_schema_get, then model_run with parameters={"model": "<GLB asset id from step 4>", "heightMeters": 1.8, "actionId": <id from the library>}.
jobs_wait, asset_display, then asset_download for engine import.

Common mistakes

texturePrompt next to textureImage expecting a blend: the image wins, the text is ignored.
Two style inputs on Retexture: it takes exactly one of prompt, style image, or multi-view images.
ultraMode: true with several images: Ultra requires a single input image.
UV Unwrap on a dense mesh: above 44,000 faces the job is rejected; Remesh down first.
Rigging before Animation: Animation rigs by itself, so the chain wastes a job.
targetPolycount on Image to 3D or Text-to-3D with shouldRemesh off: it does not apply.
A bare string where the schema says array: one photo goes as ["asset_x"].

Files

Just SKILL.md in skills/scenario-meshy of scenario-labs/skills.

Open the folder on GitHubat commit f6f8ab7

Compare with similar skills

Scenario Meshy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scenario Meshy compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Scenario Meshy this skillscenario-labs/skills	946	—	~1.6k	Automated safety check: Pass	MIT	yesterday
Unreal Material and VFX Workflowflopperam/unreal-engine-mcp	1.1k	—	~927	Automated safety check: Pass	None	3 mo ago
Kiln Refine Assetinstruktlabs/kiln	255	—	~3.6k	Automated safety check: Pass	MIT	today
Fmodel Unpackpa001024/dna-builder	137	—	~2.6k	Automated safety check: Pass	MIT	yesterday
Kiln Author Assetinstruktlabs/kiln	255	—	~4k	Automated safety check: Pass	MIT	today
Kiln QA Assetinstruktlabs/kiln	255	—	~2.7k	Automated safety check: Pass	MIT	today

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Works with

Model Context Protocol

Questions about Scenario Meshy

What does Scenario Meshy do?

A skill your agent uses when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB…. Scenario Meshy is an agent skill from scenario-labs/skills. Use when creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip.

When should I use Scenario Meshy?

Scenario Meshy fits situations like: refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo; 1-4 multi-view angles; retexturing an existing GLB; remeshing to a target polycount.

How do I install Scenario Meshy in Claude Code?

Run `npx skills add scenario-labs/skills --skill scenario-meshy -a claude-code`. Or copy the skill folder (skills/scenario-meshy in scenario-labs/skills) into .claude/skills/scenario-meshy in your project. Claude Code loads it when a task matches its description.

How do I install Scenario Meshy in Codex?

Run `npx skills add scenario-labs/skills --skill scenario-meshy -a codex`. Or copy the skill folder (skills/scenario-meshy in scenario-labs/skills) into .agents/skills/scenario-meshy in your project. Codex loads it when a task matches its description.

Can I use Scenario Meshy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add scenario-labs/skills --skill scenario-meshy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scenario-meshy, .gemini/skills/scenario-meshy, .github/skills/scenario-meshy and .opencode/skills/scenario-meshy in your project.

What does Scenario Meshy need to run?

Going by SKILL.md and its folder, Scenario Meshy needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Scenario Meshy access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Scenario Meshy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scenario Meshy use?

Scenario Meshy is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scenario Meshy use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scenario Meshy?

Skills that share tags, products or a category with Scenario Meshy: Unreal Material and VFX Workflow (flopperam/unreal-engine-mcp, 1.1k stars), Kiln Refine Asset (instruktlabs/kiln, 255 stars), Fmodel Unpack (pa001024/dna-builder, 137 stars) and Kiln Author Asset (instruktlabs/kiln, 255 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scenario Meshy?

scenario-labs (a GitHub organization) maintains it in scenario-labs/skills, which has 946 GitHub stars. The repository holds 146 skills in this directory. The repository was last updated on October 10, 2026.

Source: scenario-labs/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Scenario Meshy

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Scenario Meshy 3D

Overview

Quick reference

Generate raw, refine on purpose

Rig or animate, not both

Worked example: photo to animated character

Common mistakes

Files

Compare with similar skills

Similar skills

More from scenario-labs/skills

Related

Questions about Scenario Meshy