Meshy 3d Generation

Meshy 3D Generation

Directly communicate with the Meshy AI API to generate 3D assets. This skill handles the complete lifecycle: environment setup, API key detection, task creation, polling, downloading, and chaining multi-step pipelines.

For full endpoint reference (all parameters, response schemas, error codes), read [reference.md](reference.md).

---

IMPORTANT: First-Use Session Notice

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 in meshy_output/history.json.

This only needs to be said once per session, at the beginning.

IMPORTANT: File Organization

All downloaded files MUST go into a structured meshy_output/ directory in the current working directory. Do NOT scatter files randomly.

• Each project gets its own folder: meshy_output/{YYYYMMDD_HHmmss}_{prompt_slug}_{task_id_prefix}/
• For chained tasks (preview → refine → rig), reuse the same project_dir
• Track tasks in metadata.json per project, and global history.json
• Auto-download thumbnails alongside models

The Reusable Script Template below includes get_project_dir(), record_task(), and save_thumbnail() helpers.

---

IMPORTANT: Shell Command Rules

Use only standard POSIX tools in shell commands. Do NOT use rg (ripgrep), fd, or other non-standard CLI tools — they may not be installed. Use these standard alternatives instead:

| Do NOT use | Use instead | |---|---| | rg | grep | | fd | find | | bat | cat | | exa / eza | ls |

---

IMPORTANT: Run Long Tasks Properly

Meshy generation tasks take 1–5 minutes. When running Python scripts that poll for completion:

• Write the entire create → poll → download flow as ONE Python script and execute it in a single Bash call. Do NOT split into multiple commands. This keeps the API key, task IDs, and session in one process context.
• Use python3 -u script.py (unbuffered) so progress output is visible in real time.
• Be patient with long-running scripts — do NOT interrupt or kill them prematurely. Tasks at 99% for 30–120s is normal finalization, not a failure.

---

Step 0: Environment Detection (ALWAYS RUN FIRST)

Before any API call, detect whether the environment is ready:

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 files in workspace
for f in .env .env.local; do
  if [ -f "$f" ] && grep -q "MESHY_API_KEY" "$f" 2>/dev/null; then
    echo "DOTENV($f): FOUND"
    export $(grep "MESHY_API_KEY" "$f" | head -1)
  fi
done

# 3. Check shell profiles
for f in ~/.zshrc ~/.bashrc ~/.bash_profile ~/.profile; do
  if [ -f "$f" ] && grep -q "MESHY_API_KEY" "$f" 2>/dev/null; then
    echo "SHELL_PROFILE: FOUND in $f"
  fi
done

# 4. Final status
if [ -n "$MESHY_API_KEY" ]; then
  echo "READY: key=${MESHY_API_KEY:0:12}..."
else
  echo "READY: NO_KEY_FOUND"
fi

# 5. 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 ==="

Decision After Detection

• Key found → Proceed to Step 1.
• Key NOT found → Go to Step 0a.
• Python requests missing → Run pip install requests.

Step 0a: API Key Setup (Only If No Key Found)

Tell the user:

> To use the Meshy API, you need an API key. Here's how to get one: > > 1. Go to https://www.meshy.ai/settings/api > 2. Click "Create API Key", give it a name, and copy the key (it starts with msy_) > 3. The key is only shown once — save it somewhere safe > > Note: API access requires a Pro plan or above. Free-tier accounts cannot create API keys. If you see "Please upgrade to a premium plan to create API tasks", you'll need to upgrade at https://www.meshy.ai/pricing first.

Once the user provides their key, set it and verify:

macOS (zsh):

export MESHY_API_KEY="msy_PASTE_KEY_HERE"

# Verify
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"
  echo 'export MESHY_API_KEY="msy_PASTE_KEY_HERE"' >> ~/.zshrc
  echo "Persisted to ~/.zshrc"
else
  echo "Key invalid (HTTP $STATUS). Check the key and try again."
fi

Linux (bash):

export MESHY_API_KEY="msy_PASTE_KEY_HERE"

# Verify (same as above), then persist to ~/.bashrc
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"
  echo 'export MESHY_API_KEY="msy_PASTE_KEY_HERE"' >> ~/.bashrc
  echo "Persisted to ~/.bashrc"
else
  echo "Key invalid (HTTP $STATUS). Check the key and try again."
fi

Windows (PowerShell):

$env:MESHY_API_KEY = "msy_PASTE_KEY_HERE"

# Verify
$status = (Invoke-WebRequest -Uri "https://api.meshy.ai/openapi/v1/balance" -Headers @{Authorization="Bearer $env:MESHY_API_KEY"} -UseBasicParsing).StatusCode
if ($status -eq 200) {
    Write-Host "Key valid."
    # Persist permanently
    [System.Environment]::SetEnvironmentVariable("MESHY_API_KEY", $env:MESHY_API_KEY, "User")
    Write-Host "Persisted to user environment variables. Restart terminal to take effect."
} else {
    Write-Host "Key invalid (HTTP $status). Check the key and try again."
}

Alternative (all platforms): Create a .env file in your project root:

MESHY_API_KEY=msy_PASTE_KEY_HERE

---

Step 1: Confirm Plan With User Before Spending Credits

CRITICAL: Before creating any task, present the user with a summary and get confirmation:

I'll generate a 3D model of "" 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:  credits

  Shall I proceed?

For multi-step pipelines (e.g., text-to-3d → rig → animate), present the FULL pipeline cost upfront:

| Step | API | Credits | |---|---|---| | Preview | Text to 3D | 20 | | Refine | Text to 3D | 10 | | Rig | Auto-Rigging | 5 | | Total | | 35 |

> Note: Rigging automatically includes basic walking + running animations for free (in result.basic_animations). Only add Animate (3 credits) if the user needs a custom animation beyond walking/running.

Wait for user confirmation before executing.

Intent → API Mapping

| 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 (includes walking + running) | | Animate a rigged character (custom) | 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 |

---

Step 2: Execute the Workflow

CRITICAL: Async Task Model

All generation endpoints return {"result": ""}, NOT the model. You MUST poll.

NEVER read model_urls from the POST response.

Reusable Script Template

Use this as the base for ALL generation workflows:

#!/usr/bin/env python3
"""Meshy API task runner. Handles create → poll → download."""
import requests, time, os, sys

API_KEY = os.environ.get("MESHY_API_KEY", "")
if not API_KEY:
    sys.exit("ERROR: MESHY_API_KEY not set")

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)")
    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). Current 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 task with exponential backoff (5s→30s, fixed 15s at 95%+)."""
    elapsed = 0
    delay = 5            # Initial delay: 5s
    max_delay = 30       # Cap: 30s
    backoff = 1.5        # Backoff multiplier
    finalize_delay = 15  # Fixed delay during finalization (95%+)
    poll_count = 0
    while elapsed = 95 else delay
        time.sleep(current_delay)
        elapsed += current_delay
        if progress  **Refine compatibility**: Only previews generated with `meshy-5` or `latest` can be refined. `meshy-6` previews do NOT support refine (API returns 400). If the user wants to refine later, always use `meshy-5` or `latest` for the preview step.

### Image to 3D

```python
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,            # Default is False; set True for metallic/roughness/normal maps
    "ai_model": "latest",
    # "image_enhancement": True,   # Optimize input image (meshy-6/latest only, default True)
    # "remove_lighting": True,     # Remove baked lighting from texture (meshy-6/latest only, default True)
})

task = poll_task("/openapi/v1/image-to-3d", task_id)
download(task["model_urls"]["glb"], "model.glb")

Multi-Image to 3D

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,            # Default is False; set True for metallic/roughness/normal maps
    "ai_model": "latest",
    # "image_enhancement": True,   # Optimize input images (meshy-6/latest only, default True)
    # "remove_lighting": True,     # Remove baked lighting from texture (meshy-6/latest only, default True)
})
task = poll_task("/openapi/v1/multi-image-to-3d", task_id)
download(task["model_urls"]["glb"], "model.glb")

Retexture

task_id = create_task("/openapi/v1/retexture", {
    "input_task_id": "PREVIOUS_TASK_ID",      # or "model_url": "URL"
    "text_style_prompt": "wooden texture",     # or "image_style_url": "URL"
    "enable_pbr": True,
    # "remove_lighting": True,     # Remove baked lighting (meshy-6/latest only, default True)
})
task = poll_task("/openapi/v1/retexture", task_id)
download(task["model_urls"]["glb"], "retextured.glb")

Remesh / Format Conversion

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)
for fmt, url in task["model_urls"].items():
    download(url, f"remeshed.{fmt}")

Auto-Rigging + Animation

IMPORTANT: When the user explicitly asks to rig or animate, the generation step (text-to-3d / image-to-3d) MUST use pose_mode: "t-pose" for best rigging results. If the model was already generated without t-pose, recommend regenerating with pose_mode: "t-pose" first.

Before rigging, verify the model's polygon count is under 300,000. The script should auto-check and block if exceeded:

# Pre-rig check: verify face count (MUST be ≤ 300,000)
source_endpoint = "/openapi/v2/text-to-3d"  # adjust to match the source task's endpoint
source_task_id = "TASK_ID"
check_resp = SESSION.get(f"{BASE}{source_endpoint}/{source_task_id}", headers=HEADERS, timeout=30)
check_resp.raise_for_status()
source = check_resp.json()
face_count = source.get("face_count", 0)
if face_count > 300000:
    print(f"ERROR: Model has {face_count:,} faces (limit: 300,000). Remesh first:")
    print(f"  create_task('/openapi/v1/remesh', {{'input_task_id': '{source_task_id}', 'target_polycount': 100000}})")
    sys.exit("Rigging blocked: face count too high")

# Rig (humanoid bipedal characters only, polycount must be ≤ 300,000)
rig_id = create_task("/openapi/v1/rigging", {
    "input_task_id": "TASK_ID",
    "height_meters": 1.7,
})
rig_task = poll_task("/openapi/v1/rigging", rig_id)
download(rig_task["result"]["rigged_character_glb_url"], "rigged.glb")

# Rigging automatically includes basic walking + running animations — download them:
download(rig_task["result"]["basic_animations"]["walking_glb_url"], "walking.glb")
download(rig_task["result"]["basic_animations"]["running_glb_url"], "running.glb")

# Only call meshy_animate if you need a CUSTOM animation beyond walking/running:
# anim_id = create_task("/openapi/v1/animations", {
#     "rig_task_id": rig_id,
#     "action_id": 1,  # from Animation Library
# })
# anim_task = poll_task("/openapi/v1/animations", anim_id)
# download(anim_task["result"]["animation_glb_url"], "animated.glb")

Text to Image / Image to Image

# 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: 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)

---

Step 3: Report Results

After task succeeds, report:

1. Downloaded file paths and sizes
2. Task IDs (for follow-up operations like refine, rig, retexture)
3. Available formats (list model_urls keys)
4. Thumbnail URL if present
5. Credits consumed and remaining balance (run balance check)
6. Suggested next steps:

• Preview done → "Want to refine (add textures)?"
• Model done → "Want to rig this character for animation?"
• Rigged → "Want to apply an animation?"
• Any model → "Want to remesh / export to another format?"
• Any model → "Want to 3D print this model?" (requires meshy-3d-printing skill)

---

Error Recovery

| HTTP Status | Meaning | Action | |---|---|---| | 401 | Invalid API key | Re-run Step 0; ask user to check key | | 402 | Insufficient credits | Auto-query balance (GET /openapi/v1/balance), show current balance, link https://www.meshy.ai/pricing | | 422 | Cannot process | Explain limitation (e.g., non-humanoid for rigging) | | 429 | Rate limited | Auto-retry after 5s (max 3 times) | | 5xx | Ser

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Laic-parsiism709
• Source: Laic-parsiism709/meshy-3d-agent
• License: MIT

Install and usage instructions live in the source repository linked above.