Game Asset Generator
htdt/godogen
Generates game art from text prompts: PNG images, GLB 3D models, rigged characters, animations and sprites, with background removal.
Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API.
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agent --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/meshy-3d-agent .claude/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.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/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .claude/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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 install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agentType 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.
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agent --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/meshy-3d-agent .agents/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .agents/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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.
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agent --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/meshy-3d-agent .cursor/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .cursor/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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 https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/meshy-3d-agent--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agent --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/meshy-3d-agent .gemini/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.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/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .gemini/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agentInstalls 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).
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/meshy-3d-agent .github/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.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/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .github/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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.
$ npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills meshy-3d-agent --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/meshy-3d-agent .opencode/skills/meshy-3d-agent && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "meshy-3d-agent" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/meshy-3d-agent into .opencode/skills/meshy-3d-agent/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "meshy-3d-agent", 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.
meshy-3d-agentGenerate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API.
Meshy 3D Agent is an agent skill from LeoYeAI/openclaw-master-skills. Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Handles API key detection, task creation, polling, downloading, and full 3D print pipeline with slicer integration. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, 3D print a model, or interact with the Meshy API.
Its SKILL.md is about 6.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `_meta.json` and `reference.md`). Compatibility notes: Requires Python 3 with requests package. Compatible with OpenClaw and all Agent Skills tools.
It sits in Game Development, covering 3D graphics and WebGL and Game assets and audio. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT-0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
BashWriteFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
python3curlpipFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
api.meshy.aimeshy.aiFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
MESHY_API_KEYAPI_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires Python 3 with requests package. Compatible with OpenClaw and all Agent Skills tools.
From compatibility in the SKILL.md frontmatter.
Meshy 3D Agent loads about 6.7k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 1,384 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
ogged, never written to any file except `.env` in the current working directory when explicitly requested by the user.- Read: `.env` in the current working directory only (API key lookup)- Write: `.env` in the current working directory only (API key storage, only on user request)the current session environment and the `.env` file in the current working directory. Do NOT scan home directories or sh# 2. Check .env in current working directory onlyif [ -f ".env" ] && grep -q "MESHY_API_KEY" ".env" 2>/dev/null; thenecho "DOTENV(.env): FOUND"MESHY_API_KEY=$(grep "^MESHY_API_KEY=" ".env" | head -1 | cut -d'=' -f2- | tr -d '"'"'" )rrent session and optionally persist to `.env`:# Write to .env in current working directoryAutomated 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.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT-0 licence (© LeoYeAI). 1,384 words, ~6,686 tokens.
.claude/skills/meshy-3d-agent/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Directly communicate with the Meshy AI API to generate and print 3D assets. Covers the complete lifecycle: API key setup, task creation, exponential backoff polling, downloading, multi-step pipelines, and 3D print preparation with slicer integration.
Environment variables accessed:
MESHY_API_KEY — API authentication token sent in HTTP Authorization: Bearer header only. Never logged, never written to any file except .env in the current working directory when explicitly requested by the user.External network endpoints:
https://api.meshy.ai — Meshy AI API (task creation, status polling, model/image downloads)File system access:
.env in the current working directory only (API key lookup).env in the current working directory only (API key storage, only on user request)./meshy_output/ in the current working directory (downloaded model files, metadata)Data leaving this machine:
api.meshy.ai include the MESHY_API_KEY in the Authorization header and user-provided text prompts or image URLs. No other local data is transmitted. Downloaded model files are saved locally only.When this skill is first activated in a session, inform the user:
All generated files will be saved to
meshy_output/in the current working directory. Each project gets its own folder ({YYYYMMDD_HHmmss}_{prompt}_{id}/) with model files, textures, thumbnails, and metadata. History is tracked inmeshy_output/history.json.
This only needs to be said once per session.
All downloaded files MUST go into a structured meshy_output/ directory in the current working directory. Do NOT scatter files randomly.
meshy_output/{YYYYMMDD_HHmmss}_{prompt_slug}_{task_id_prefix}/project_dirmetadata.json per project, and global history.jsonUse only standard POSIX tools. Do NOT use rg, fd, bat, exa/eza.
Meshy generation takes 1–5 minutes. Write the entire create → poll → download flow as ONE Python script and execute in a single Bash call. Use python3 -u script.py for unbuffered output. Tasks sitting at 99% for 30–120s is normal finalization — do NOT interrupt.
Only check the current session environment and the .env file in the current working directory. Do NOT scan home directories or shell profile files.
echo "=== Meshy API Key Detection ==="
# 1. Check current env var
if [ -n "$MESHY_API_KEY" ]; then
echo "ENV_VAR: FOUND (${MESHY_API_KEY:0:8}...)"
else
echo "ENV_VAR: NOT_FOUND"
fi
# 2. Check .env in current working directory only
if [ -f ".env" ] && grep -q "MESHY_API_KEY" ".env" 2>/dev/null; then
echo "DOTENV(.env): FOUND"
export MESHY_API_KEY=$(grep "^MESHY_API_KEY=" ".env" | head -1 | cut -d'=' -f2- | tr -d '"'"'" )
fi
# 3. Final status
if [ -n "$MESHY_API_KEY" ]; then
echo "READY: key=${MESHY_API_KEY:0:8}..."
else
echo "READY: NO_KEY_FOUND"
fi
# 4. Python requests check
python3 -c "import requests; print('PYTHON_REQUESTS: OK')" 2>/dev/null || echo "PYTHON_REQUESTS: MISSING (run: pip install requests)"
echo "=== Detection Complete ==="pip install requests.Tell the user:
To use the Meshy API, you need an API key:
- Go to https://www.meshy.ai/settings/api
- Click "Create API Key", name it, and copy the key (starts with
msy_)- The key is shown only once — save it somewhere safe
Note: API access requires a Pro plan or above. Free-tier accounts cannot create API keys.
Once the user provides the key, set it for the current session and optionally persist to .env:
# Set for current session only
export MESHY_API_KEY="msy_PASTE_KEY_HERE"
# Verify the key
STATUS=$(curl -s -o /dev/null -w "%{http_code}" \
-H "Authorization: Bearer $MESHY_API_KEY" \
https://api.meshy.ai/openapi/v1/balance)
if [ "$STATUS" = "200" ]; then
BALANCE=$(curl -s -H "Authorization: Bearer $MESHY_API_KEY" https://api.meshy.ai/openapi/v1/balance)
echo "Key valid. $BALANCE"
else
echo "Key invalid (HTTP $STATUS). Please check the key and try again."
fiTo persist the key (current project only):
# Write to .env in current working directory
echo 'MESHY_API_KEY=msy_PASTE_KEY_HERE' >> .env
echo "Saved to .env"
# IMPORTANT: add .env to .gitignore to avoid leaking the key
grep -q "^\.env" .gitignore 2>/dev/null || echo ".env" >> .gitignore
echo ".env added to .gitignore"Security reminder: The key is stored only in
.envin your current project directory. Never commit this file to version control..envhas been automatically added to.gitignore.
CRITICAL: Before creating any task, present the user with a cost summary and wait for confirmation:
I'll generate a 3D model of "<prompt>" using the following plan:
1. Preview (mesh generation) — 20 credits
2. Refine (texturing with PBR) — 10 credits
3. Download as .glb
Total cost: 30 credits
Current balance: <N> credits
Shall I proceed?For multi-step pipelines (text-to-3d → rig → animate), show the FULL pipeline cost upfront.
Note: Rigging automatically includes walking + running animations at no extra cost. Only add
Animate(3 credits) for custom animations beyond those.
| User wants to... | API | Endpoint | Credits |
|---|---|---|---|
| 3D model from text | Text to 3D | POST /openapi/v2/text-to-3d | 20 + 10 |
| 3D model from one image | Image to 3D | POST /openapi/v1/image-to-3d | 20–30 |
| 3D model from multiple images | Multi-Image to 3D | POST /openapi/v1/multi-image-to-3d | 20–30 |
| New textures on existing model | Retexture | POST /openapi/v1/retexture | 10 |
| Change mesh format/topology | Remesh | POST /openapi/v1/remesh | 5 |
| Add skeleton to character | Auto-Rigging | POST /openapi/v1/rigging | 5 |
| Animate a rigged character | Animation | POST /openapi/v1/animations | 3 |
| 2D image from text | Text to Image | POST /openapi/v1/text-to-image | 3–9 |
| Transform a 2D image | Image to Image | POST /openapi/v1/image-to-image | 3–9 |
| Check credit balance | Balance | GET /openapi/v1/balance | 0 |
| 3D print a model | → See Print Pipeline section | — | 20 |
Use this as the base for ALL workflows. It loads the API key securely from environment or .env in the current directory only:
#!/usr/bin/env python3
"""Meshy API task runner. Handles create → poll → download."""
import requests, time, os, sys, re, json
from datetime import datetime
# --- Secure API key loading ---
def load_api_key():
"""Load MESHY_API_KEY from environment, then .env in cwd only."""
key = os.environ.get("MESHY_API_KEY", "").strip()
if key:
return key
env_path = os.path.join(os.getcwd(), ".env")
if os.path.exists(env_path):
with open(env_path) as f:
for line in f:
line = line.strip()
if line.startswith("MESHY_API_KEY=") and not line.startswith("#"):
val = line.split("=", 1)[1].strip().strip('"').strip("'")
if val:
return val
return ""
API_KEY = load_api_key()
if not API_KEY:
sys.exit("ERROR: MESHY_API_KEY not set. Run Step 0a to configure it.")
# Never log the full key — only first 8 chars for traceability
print(f"API key loaded: {API_KEY[:8]}...")
BASE = "https://api.meshy.ai"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
SESSION = requests.Session()
SESSION.trust_env = False # bypass any system proxy settings
def create_task(endpoint, payload):
resp = SESSION.post(f"{BASE}{endpoint}", headers=HEADERS, json=payload, timeout=30)
if resp.status_code == 401:
sys.exit("ERROR: Invalid API key (401). Re-run Step 0a.")
if resp.status_code == 402:
try:
bal = SESSION.get(f"{BASE}/openapi/v1/balance", headers=HEADERS, timeout=10)
balance = bal.json().get("balance", "unknown")
sys.exit(f"ERROR: Insufficient credits (402). Balance: {balance}. Top up at https://www.meshy.ai/pricing")
except Exception:
sys.exit("ERROR: Insufficient credits (402). Check balance at https://www.meshy.ai/pricing")
if resp.status_code == 429:
sys.exit("ERROR: Rate limited (429). Wait and retry.")
resp.raise_for_status()
task_id = resp.json()["result"]
print(f"TASK_CREATED: {task_id}")
return task_id
def poll_task(endpoint, task_id, timeout=600):
"""Poll with exponential backoff (5s→30s, fixed 15s at 95%+)."""
elapsed, delay, max_delay, backoff, finalize_delay, poll_count = 0, 5, 30, 1.5, 15, 0
while elapsed < timeout:
poll_count += 1
resp = SESSION.get(f"{BASE}{endpoint}/{task_id}", headers=HEADERS, timeout=30)
resp.raise_for_status()
task = resp.json()
status = task["status"]
progress = task.get("progress", 0)
bar = f"[{'█' * int(progress/5)}{'░' * (20 - int(progress/5))}] {progress}%"
print(f" {bar} — {status} ({elapsed}s, poll #{poll_count})", flush=True)
if status == "SUCCEEDED":
return task
if status in ("FAILED", "CANCELED"):
msg = task.get("task_error", {}).get("message", "Unknown")
sys.exit(f"TASK_{status}: {msg}")
current_delay = finalize_delay if progress >= 95 else delay
time.sleep(current_delay)
elapsed += current_delay
if progress < 95:
delay = min(delay * backoff, max_delay)
sys.exit(f"TIMEOUT after {timeout}s ({poll_count} polls)")
def download(url, filepath):
"""Download a file into a project directory (within cwd/meshy_output/)."""
os.makedirs(os.path.dirname(filepath), exist_ok=True)
print(f"Downloading {filepath}...", flush=True)
resp = SESSION.get(url, timeout=300, stream=True)
resp.raise_for_status()
with open(filepath, "wb") as f:
for chunk in resp.iter_content(chunk_size=8192):
f.write(chunk)
print(f"DOWNLOADED: {filepath} ({os.path.getsize(filepath)/1024/1024:.1f} MB)")
# --- File organization helpers ---
OUTPUT_ROOT = os.path.join(os.getcwd(), "meshy_output")
os.makedirs(OUTPUT_ROOT, exist_ok=True)
HISTORY_FILE = os.path.join(OUTPUT_ROOT, "history.json")
def get_project_dir(task_id, prompt="", task_type="model"):
slug = re.sub(r'[^a-z0-9]+', '-', (prompt or task_type).lower())[:30].strip('-')
folder = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{slug}_{task_id[:8]}"
project_dir = os.path.join(OUTPUT_ROOT, folder)
os.makedirs(project_dir, exist_ok=True)
return project_dir
def record_task(project_dir, task_id, task_type, stage, prompt="", files=None):
meta_path = os.path.join(project_dir, "metadata.json")
meta = json.load(open(meta_path)) if os.path.exists(meta_path) else {
"project_name": prompt or task_type, "folder": os.path.basename(project_dir),
"root_task_id": task_id, "created_at": datetime.now().isoformat(), "tasks": []
}
meta["tasks"].append({"task_id": task_id, "task_type": task_type, "stage": stage,
"files": files or [], "created_at": datetime.now().isoformat()})
meta["updated_at"] = datetime.now().isoformat()
json.dump(meta, open(meta_path, "w"), indent=2)
history = json.load(open(HISTORY_FILE)) if os.path.exists(HISTORY_FILE) else {"version": 1, "projects": []}
folder = os.path.basename(project_dir)
entry = next((p for p in history["projects"] if p["folder"] == folder), None)
if entry:
entry.update({"task_count": len(meta["tasks"]), "updated_at": meta["updated_at"]})
else:
history["projects"].append({"folder": folder, "prompt": prompt, "task_type": task_type,
"root_task_id": task_id, "created_at": meta["created_at"],
"updated_at": meta["updated_at"], "task_count": len(meta["tasks"])})
json.dump(history, open(HISTORY_FILE, "w"), indent=2)
def save_thumbnail(project_dir, url):
path = os.path.join(project_dir, "thumbnail.png")
if os.path.exists(path): return
try:
r = SESSION.get(url, timeout=15); r.raise_for_status()
open(path, "wb").write(r.content)
except Exception: passAppend to the template above:
PROMPT = "USER_PROMPT"
# Preview
preview_id = create_task("/openapi/v2/text-to-3d", {
"mode": "preview",
"prompt": PROMPT,
"ai_model": "latest",
# "pose_mode": "t-pose", # Use "t-pose" if rigging/animating later
})
task = poll_task("/openapi/v2/text-to-3d", preview_id)
project_dir = get_project_dir(preview_id, prompt=PROMPT)
download(task["model_urls"]["glb"], os.path.join(project_dir, "preview.glb"))
record_task(project_dir, preview_id, "text-to-3d", "preview", prompt=PROMPT, files=["preview.glb"])
if task.get("thumbnail_url"):
save_thumbnail(project_dir, task["thumbnail_url"])
print(f"\nPREVIEW COMPLETE — Task: {preview_id} | Project: {project_dir}")
# Refine
refine_id = create_task("/openapi/v2/text-to-3d", {
"mode": "refine",
"preview_task_id": preview_id,
"enable_pbr": True,
"ai_model": "latest",
})
task = poll_task("/openapi/v2/text-to-3d", refine_id)
download(task["model_urls"]["glb"], os.path.join(project_dir, "refined.glb"))
record_task(project_dir, refine_id, "text-to-3d", "refined", prompt=PROMPT, files=["refined.glb"])
print(f"\nREFINE COMPLETE — Task: {refine_id} | Formats: {', '.join(task['model_urls'].keys())}")Note: Only previews from
meshy-5orlatestsupport refine.meshy-6previews do NOT (API returns 400).
import base64
# For local files: convert to data URI
# with open("photo.jpg", "rb") as f:
# image_url = "data:image/jpeg;base64," + base64.b64encode(f.read()).decode()
task_id = create_task("/openapi/v1/image-to-3d", {
"image_url": "IMAGE_URL_OR_DATA_URI",
"should_texture": True,
"enable_pbr": True,
"ai_model": "latest",
})
task = poll_task("/openapi/v1/image-to-3d", task_id)
project_dir = get_project_dir(task_id, task_type="image-to-3d")
download(task["model_urls"]["glb"], os.path.join(project_dir, "model.glb"))
record_task(project_dir, task_id, "image-to-3d", "complete", files=["model.glb"])task_id = create_task("/openapi/v1/multi-image-to-3d", {
"image_urls": ["URL_1", "URL_2", "URL_3"], # 1–4 images
"should_texture": True,
"enable_pbr": True,
"ai_model": "latest",
})
task = poll_task("/openapi/v1/multi-image-to-3d", task_id)
project_dir = get_project_dir(task_id, task_type="multi-image-to-3d")
download(task["model_urls"]["glb"], os.path.join(project_dir, "model.glb"))task_id = create_task("/openapi/v1/retexture", {
"input_task_id": "PREVIOUS_TASK_ID",
"text_style_prompt": "wooden texture",
"enable_pbr": True,
})
task = poll_task("/openapi/v1/retexture", task_id)
project_dir = get_project_dir(task_id, task_type="retexture")
download(task["model_urls"]["glb"], os.path.join(project_dir, "retextured.glb"))task_id = create_task("/openapi/v1/remesh", {
"input_task_id": "TASK_ID",
"target_formats": ["glb", "fbx", "obj"],
"topology": "quad",
"target_polycount": 10000,
})
task = poll_task("/openapi/v1/remesh", task_id)
project_dir = get_project_dir(task_id, task_type="remesh")
for fmt, url in task["model_urls"].items():
download(url, os.path.join(project_dir, f"remeshed.{fmt}"))When the user asks to rig or animate, the generation step MUST use pose_mode: "t-pose".
# Pre-rig check: polycount must be ≤ 300,000
source_endpoint = "/openapi/v2/text-to-3d" # adjust to match source task endpoint
source_task_id = "TASK_ID"
check = SESSION.get(f"{BASE}{source_endpoint}/{source_task_id}", headers=HEADERS, timeout=30)
check.raise_for_status()
face_count = check.json().get("face_count", 0)
if face_count > 300000:
sys.exit(f"ERROR: {face_count:,} faces exceeds 300,000 limit. Remesh first.")
# Rig
rig_id = create_task("/openapi/v1/rigging", {
"input_task_id": source_task_id,
"height_meters": 1.7,
})
rig_task = poll_task("/openapi/v1/rigging", rig_id)
project_dir = get_project_dir(rig_id, task_type="rigging")
download(rig_task["result"]["rigged_character_glb_url"], os.path.join(project_dir, "rigged.glb"))
download(rig_task["result"]["basic_animations"]["walking_glb_url"], os.path.join(project_dir, "walking.glb"))
download(rig_task["result"]["basic_animations"]["running_glb_url"], os.path.join(project_dir, "running.glb"))
# Custom animation (optional, 3 credits — only if user needs beyond walking/running)
# anim_id = create_task("/openapi/v1/animations", {"rig_task_id": rig_id, "action_id": 1})
# anim_task = poll_task("/openapi/v1/animations", anim_id)
# download(anim_task["result"]["animation_glb_url"], os.path.join(project_dir, "animated.glb"))# Text to Image
task_id = create_task("/openapi/v1/text-to-image", {
"ai_model": "nano-banana-pro",
"prompt": "a futuristic spaceship",
})
task = poll_task("/openapi/v1/text-to-image", task_id)
# Result URL: task["image_url"]
# Image to Image
task_id = create_task("/openapi/v1/image-to-image", {
"ai_model": "nano-banana-pro",
"prompt": "make it look cyberpunk",
"reference_image_urls": ["URL"],
})
task = poll_task("/openapi/v1/image-to-image", task_id)Trigger when the user mentions: print, 3d print, slicer, slice, bambu, orca, prusa, cura, figurine, miniature, statue, physical model, desk toy, phone stand.
Text-to-3D Print:
| Step | Action | Credits |
|---|---|---|
| 1 | Text to 3D (mode: "preview", no texture) | 20 |
| 2 | Printability check (see checklist) | 0 |
| 3 | Download OBJ | 0 |
| 4 | Open in slicer (direct launch or manual import) | 0 |
| 5 (optional) | Retexture for multi-color | 10 |
Image-to-3D Print:
| Step | Action | Credits |
|---|---|---|
| 1 | Image to 3D with should_texture: False | 20 |
| 2 | Printability check | 0 |
| 3 | Download OBJ | 0 |
| 4 | Open in slicer (direct launch or manual import) | 0 |
Append to the template after task SUCCEEDED:
import subprocess, shutil
# Download OBJ for printing
obj_url = task["model_urls"].get("obj")
if not obj_url:
print("OBJ not available. Available:", list(task["model_urls"].keys()))
print("Download GLB and import manually into your slicer.")
obj_url = task["model_urls"].get("glb")
obj_path = os.path.join(project_dir, "model.obj")
download(obj_url, obj_path)
# --- Post-process OBJ for slicer compatibility ---
def fix_obj_for_printing(input_path, output_path=None, target_height_mm=75.0):
"""
Fix OBJ coordinate system, scale, and position for 3D printing slicers.
- Rotates from glTF Y-up to slicer Z-up: (x, y, z) -> (x, -z, y)
- Scales model to target_height_mm (default 75mm)
- Centers model on XY plane (so slicer places it at bed center)
- Aligns model bottom to Z=0 (origin at bottom)
"""
if output_path is None:
output_path = input_path
lines = open(input_path, "r").readlines()
# Pass 1: rotate vertices Y-up -> Z-up, collect bounds
rotated = []
min_x, max_x = float("inf"), float("-inf")
min_y, max_y = float("inf"), float("-inf")
min_z, max_z = float("inf"), float("-inf")
for line in lines:
if line.startswith("v "):
parts = line.split()
x, y, z = float(parts[1]), float(parts[2]), float(parts[3])
rx, ry, rz = x, -z, y
min_x, max_x = min(min_x, rx), max(max_x, rx)
min_y, max_y = min(min_y, ry), max(max_y, ry)
min_z, max_z = min(min_z, rz), max(max_z, rz)
rotated.append(("v", rx, ry, rz, parts[4:]))
elif line.startswith("vn "):
parts = line.split()
nx, ny, nz = float(parts[1]), float(parts[2]), float(parts[3])
rotated.append(("vn", nx, -nz, ny, []))
else:
rotated.append(("line", line))
model_height = max_z - min_z
scale = target_height_mm / model_height if model_height > 1e-6 else 1.0
x_offset = -(min_x + max_x) / 2.0 * scale
y_offset = -(min_y + max_y) / 2.0 * scale
z_offset = -(min_z * scale)
# Pass 2: write transformed OBJ
with open(output_path, "w") as f:
for item in rotated:
if item[0] == "v":
_, rx, ry, rz, extra = item
tx = rx * scale + x_offset
ty = ry * scale + y_offset
tz = rz * scale + z_offset
extra_str = " " + " ".join(extra) if extra else ""
f.write(f"v {tx:.6f} {ty:.6f} {tz:.6f}{extra_str}\n")
elif item[0] == "vn":
_, nx, ny, nz, _ = item
f.write(f"vn {nx:.6f} {ny:.6f} {nz:.6f}\n")
else:
f.write(item[1])
print(f"OBJ fixed: rotated Y-up→Z-up, scaled to {target_height_mm:.0f}mm, centered on XY, bottom at Z=0")
fix_obj_for_printing(obj_path, target_height_mm=75.0)
print(f"\nModel ready for printing: {os.path.abspath(obj_path)}")
target_height_mm: Default 75mm. Adjust based on user request (e.g. "print at 15cm" →150.0).
Opening OBJ in slicer: When the user specifies a slicer (e.g. Bambu Studio, OrcaSlicer, Creality Print, PrusaSlicer, Cura), open the downloaded OBJ file directly:
subprocess.run(["open", "-a", "<AppName>", obj_path]) — the OS resolves the app location automatically.shutil.which("<binary_name>") to find the executable in PATH, then subprocess.Popen([exe, obj_path]). If not found, print the file path and instruct manual open.Automated printability analysis API is coming soon.
| Check | Recommendation |
|---|---|
| Wall thickness | Min 1.2mm FDM, 0.8mm resin |
| Overhangs | Keep below 45° or add supports |
| Manifold mesh | Watertight, no holes |
| Minimum detail | 0.4mm FDM, 0.05mm resin |
| Base stability | Flat base or add brim/raft in slicer |
| Floating parts | All parts connected or printed separately |
Automated multi-color API is coming soon.
After task succeeds:
model_urls keys)| HTTP Status | Meaning | Action |
|---|---|---|
| 401 | Invalid API key | Re-run Step 0; ask user to check key |
| 402 | Insufficient credits | Show balance, link https://www.meshy.ai/pricing |
| 422 | Cannot process | Explain (e.g., non-humanoid for rigging) |
| 429 | Rate limited | Auto-retry after 5s (max 3 times) |
| 5xx | Server error | Auto-retry after 10s (once) |
Task FAILED messages:
"The server is busy..." → retry with backoff (5s, 10s, 20s)"Internal server error." → simplify prompt, retry onceenable_pbr: true explicitly.meshy-5 / latest previews support refine; meshy-6 does not..env onlypython3 -u for unbuffered outputFor the complete API endpoint reference including all parameters, response schemas, and error codes, read reference.md.
© LeoYeAI, MIT-0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in skills/meshy-3d-agent of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Meshy 3D Agent 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Meshy 3D Agent this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~6.7k | Automated safety check: Notes | MIT-0 | |
| Game Asset Generatorhtdt/godogen | 7.1k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Threejs 3D Generatorvalkor-ai/loom | 1.2k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Asset Pipelinerehan-remade/universal-modder | 6.1k | — | ~2k | Automated safety check: Pass | MIT | |
| Blender Image To 3Dmajidmanzarpour/blender-game-skills | 135 | — | ~5.7k | Automated safety check: Pass | MIT | |
| Text To 3D AssetLaurentiuGabriel/unreal-game-assets-creation-skill | 148 | — | ~2.1k | Automated safety check: Pass | None |
htdt/godogen
Generates game art from text prompts: PNG images, GLB 3D models, rigged characters, animations and sprites, with background removal.
valkor-ai/loom
Generate, texture, rig, animate, stylize, convert, and download 3D assets for Three.js games via the Tripo API.
rehan-remade/universal-modder
Turn generated or hand-made art into exactly what a game engine loads.
majidmanzarpour/blender-game-skills
Build a game-ready 3D asset in Blender from reference images (concept art, photos, turnarounds, sketches, screenshots) for any category, including characters, creatures, architecture, vehicles…
LaurentiuGabriel/unreal-game-assets-creation-skill
Generate a game-ready 3D asset by running the local AI pipeline sequentially: Fooocus (SDXL text-to-image) - Hunyuan3D-2 (image-to-textured-GLB) - optional Blender FBX convert + Unreal import.
notque/vexjoy-agent
Deterministic palette/matrix pixel art (not AI). An agent skill from notque/vexjoy-agent.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Categories
Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API. Meshy 3D Agent is an agent skill from LeoYeAI/openclaw-master-skills. Generate 3D models, textures, images, rig characters, animate them, and prepare for 3D printing using the Meshy AI API.
Meshy 3D Agent fits situations like: the user asks to create 3D models; convert text/images to 3D; animate characters; 3D print a model.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a claude-code`. Or copy the skill folder (skills/meshy-3d-agent in LeoYeAI/openclaw-master-skills) into .claude/skills/meshy-3d-agent in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a codex`. Or copy the skill folder (skills/meshy-3d-agent in LeoYeAI/openclaw-master-skills) into .agents/skills/meshy-3d-agent in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill meshy-3d-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/meshy-3d-agent, .gemini/skills/meshy-3d-agent, .github/skills/meshy-3d-agent and .opencode/skills/meshy-3d-agent in your project.
Going by SKILL.md and its folder, Meshy 3D Agent needs the command-line tools its instructions call (python3, curl and pip) and credentials named MESHY_API_KEY and API_KEY. Our summary lists: Python 3; A credential in MESHY_API_KEY; A credential in API_KEY. Its frontmatter pre-approves these tools: Bash, Write. Compatibility (from SKILL.md): Requires Python 3 with requests package. Compatible with OpenClaw and all Agent Skills tools..
SKILL.md names 2 domains. In commands or code: api.meshy.ai and meshy.ai; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
Meshy 3D Agent is published under the MIT-0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.7k tokens (SKILL.md is roughly 27k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Meshy 3D Agent: Game Asset Generator (htdt/godogen, 7.1k stars), Threejs 3D Generator (valkor-ai/loom, 1.2k stars), Asset Pipeline (rehan-remade/universal-modder, 6.1k stars) and Blender Image To 3D (majidmanzarpour/blender-game-skills, 135 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.