v0.4.1 to v0.4.1

60 added, 680 removed. Audit B to B.

---

name: meshy-openclaw

description: 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. For Claude Code or Cursor, use the meshy-3d-generation and meshy-3d-printing skills instead.

license: MIT-0

compatibility: Requires Python 3 with requests package. Compatible with OpenClaw and all Agent Skills tools.

metadata:

author: meshy-dev

version: "0.4.1"

homepage: https://github.com/meshy-dev/meshy-3d-agent

openclaw:

primaryEnv: MESHY_API_KEY

requires:

env:

- MESHY_API_KEY

bins:

- python3

- curl

install:

- kind: uv

package: requests

allowed-tools: Bash, Write

---

# Meshy 3D — Generation + Printing

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.

---

## SECURITY MANIFEST

**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:**

- - Read: `.env` in the current working directory only (API key lookup)

+ - Read: `.env` / `.env.local` in the current working directory only (API key lookup)

- Write: `.env` in the current working directory only (API key storage, only on user request)

- Write: `./meshy_output/` in the current working directory (downloaded model files, metadata)

- Read: files explicitly provided by the user (e.g., local images passed for image-to-3D conversion), accessed only at the exact path the user specifies

- No access to home directories, shell profiles, or any path outside the above

**Data leaving this machine:**

- API requests to `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.

---

+ All paths below are relative to **this skill's own directory** (the directory containing this SKILL.md). Resolve them before running.

+ | Resource | When to use |

+ |---|---|

+ | `scripts/meshy_task.py` | Bundled CLI for every Meshy API call (create / poll / download / record / …) |

+ | `scripts/slicers.py` | Detect installed slicers; open a model file in a slicer |

+ | `scripts/fix_obj.py` | Fix OBJ coordinate system, scale, and origin for slicers |

+ | [reference.md](reference.md) | Full API reference: every parameter, response schema, error code |

+ | [references/setup.md](references/setup.md) | API key setup — read when Step 0 finds no key |

+ | [references/pipelines.md](references/pipelines.md) | Generation recipes: exact payloads + script calls per endpoint |

+ | [references/printing.md](references/printing.md) | Print pipeline walkthroughs: slicer detection, analyze/repair, white model, multicolor, Creative Lab |

+ | [references/troubleshooting.md](references/troubleshooting.md) | Error recovery trees and task failure messages |

+ ---

## 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**.

---

## 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: `meshy_output/{YYYYMMDD_HHmmss}_{prompt_slug}_{task_id_prefix}/`

- 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 bundled CLI implements this: `project-dir`, `record`, and `thumbnail` subcommands.

---

## IMPORTANT: Shell Command Rules

Use only standard POSIX tools. Do NOT use `rg`, `fd`, `bat`, `exa`/`eza`.

---

## IMPORTANT: Run Long Tasks Properly

- 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.

+ Meshy generation takes 1–5 minutes. Run each `poll` as a single Bash call and let it finish — the bundled CLI prints unbuffered progress in real time. Tasks sitting at 99% for 30–120s is normal finalization — do NOT interrupt. Pass a larger `--timeout` for heavy tasks instead of retrying.

---

- ## Step 0: API Key Detection (ALWAYS RUN FIRST)

- **Only check the current session environment and the `.env` file in the current working directory. Do NOT scan home directories or shell profile files.**

- ```bash

- echo "=== Meshy API Key Detection ==="

+ ## IMPORTANT: Never Rebuild Bundled Scripts

- # 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

+ `scripts/meshy_task.py` is the single source of truth for `create_task` / `poll_task` / `download` / `get_project_dir` / `record_task` / `save_thumbnail`. **Never retype, paraphrase, or "reconstruct" these helpers from memory** — not even partially. Compose CLI calls in bash, or write a small Python script that does `sys.path.insert(0, "<this skill's scripts dir>")` and `from meshy_task import ...`.

- # 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

+ ## Step 0: API Key Detection (ALWAYS RUN FIRST)

- # 4. Python requests check

- python3 -c "import requests; print('PYTHON_REQUESTS: OK')" 2>/dev/null || echo "PYTHON_REQUESTS: MISSING (run: pip install requests)"

+ **Only the current session environment and `.env` / `.env.local` in the current working directory are checked. Never scan home directories or shell profile files.**

- echo "=== Detection Complete ==="

+ ```bash

+ python3 scripts/meshy_task.py check-env

```

### Decision After Detection

- - **Key found** → Proceed to Step 1.

- - **Key NOT found** → Go to Step 0a.

- - **Python requests missing** → Run `pip install requests`.

+ - **`READY: key=...`** → Proceed to Step 1.

+ - **`READY: NO_KEY_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:

- >

- > 1. Go to **https://www.meshy.ai/settings/api**

- > 2. Click **"Create API Key"**, name it, and copy the key (starts with `msy_`)

- > 3. 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`:

- ```bash

- # 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."

- fi

- ```

- **To persist the key (current project only):**

- ```bash

- # 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 `.env` in your current project directory. Never commit this file to version control. `.env` has been automatically added to `.gitignore`.

+ Follow [references/setup.md](references/setup.md): create a key at https://www.meshy.ai/settings/api (Pro plan required), verify it against `GET /openapi/v1/balance`, and optionally persist it to `.env` in the current project (auto-added to `.gitignore`).

---

## Step 1: Confirm Plan With User Before Spending Credits

**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.

### Intent → API Mapping

|---|---|---|---|

| Photo → styled physical product (figure/lamp/keychain/fridge-magnet) | Creative Lab | `POST /openapi/creative-lab/{product}/v1/prototype` then `.../build` | 6 + 30 |

- | 3D print a model (white) | → See Print Pipeline section | — | 20 |

+ | 3D print a model (white) | → See 3D Printing Workflow section | — | 20 |

---

## Step 2: Execute the Workflow

- ### Reusable Script Template

- Use this as the base for ALL workflows. It loads the API key securely from environment or `.env` in the current directory only:

- ```python

- #!/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=300):

- """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: pass

- ```

- ---

- ### Text to 3D (Preview + Refine)

- Append to the template above:

- ```python

- 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

- # "hd_texture": True, # 4K base color on refine (meshy-6/latest only)

- # "target_formats": ["glb", "3mf"], # 3mf must be explicitly requested

- # NOTE: symmetry_mode / art_style / is_a_t_pose are deprecated (symmetry_mode & art_style ignored; use pose_mode)

- })

- 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())}")

- ```

- > **Refine compatibility:** Refine works with `meshy-5`, `meshy-6`, or `latest` (= Meshy 6) — pick the same family as your preview for consistency. Refine costs 10 credits regardless of model. (`meshy-4` is retired and returns 400.)

- ---

- ### (Optional but strongly recommended) 2D Optimization Pre-Step

- **Prefer the image-to-3d route over direct text-to-3d** — it's higher quality and more controllable, so for a text-only request make a design image first, then 3D-ify.

+ All generation endpoints return `{"result": "<task_id>"}`, NOT the model — you MUST poll. **NEVER** read `model_urls` from the POST response.

- Image quality directly determines 3D model quality. Before calling `/openapi/v1/image-to-3d` or `/openapi/v1/multi-image-to-3d`, evaluate the user's input and proactively suggest a 2D pass:

+ Every workflow is a sequence of calls to the bundled CLI `scripts/meshy_task.py` — do not write your own API code:

- | User input | Recommended pre-step |

+ | Subcommand | Purpose |

|---|---|

- | Only a text description, no reference image | `/openapi/v1/text-to-image` with `nano-banana-pro`. For characters add `generate_multi_view: True` and `pose_mode: "a-pose"` or `"t-pose"` for rig-friendly output. |

- | Reference image is low-resolution / cluttered background / unclear subject / bad lighting | `/openapi/v1/image-to-image` with `nano-banana-pro` to clean up. |

- | User wants to adjust style / colors / details | `/openapi/v1/image-to-image` for style transfer, then 3D-ify. |

- 3-9 extra credits typically buy a noticeable quality bump. Skip when the user already provided a clean studio-style image. Also skip for **Creative Lab** products (figure / lamp / keychain / fridge-magnet): they apply their own built-in stylization — feed the raw photo (or text, for lamp) straight to Creative Lab, not through text-to-image / image-to-image first.

- ### 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,

- "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"])

- ```

- ---

- ### Multi-Image to 3D

- ```python

- 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"))

- ```

- ---

- ### Retexture

- **IMPORTANT**: Ask user for texture style first — `text_style_prompt` OR `image_style_url` (one required, image takes precedence if both given).

- ```python

- # REQUIRED: ask user for text_style_prompt OR image_style_url

- task_id = create_task("/openapi/v1/retexture", {

- "input_task_id": "PREVIOUS_TASK_ID",

- "text_style_prompt": "wooden texture", # REQUIRED if no image_style_url

- # "image_style_url": "URL", # REQUIRED if no prompt (takes precedence)

- "enable_pbr": True,

- # "target_formats": ["glb", "3mf"], # 3mf must be explicitly requested

- })

- 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"))

- ```

- ---

- ### Remesh / Format Conversion

- ```python

- 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}"))

- ```

- ---

- ### Mesh Utilities (Convert / Resize / UV Unwrap)

- Lightweight post-processing on a finished model (via `input_task_id` or `model_url`):

- ```python

- # Convert to other formats without remeshing (1 credit). Cheapest way to get 3MF/STL.

- conv_id = create_task("/openapi/v1/convert", {

- "input_task_id": "TASK_ID", # or "model_url": "URL"

- "target_formats": ["stl", "3mf"], # required: glb/fbx/obj/usdz/blend/stl/3mf

- })

- poll_task("/openapi/v1/convert", conv_id)

- # Resize to a real-world size (1 credit). Give EXACTLY ONE resize mode.

- resize_id = create_task("/openapi/v1/resize", {

- "input_task_id": "TASK_ID", # or "model_url": "URL"

- "resize_height": 0.15, # meters — OR "resize_longest_side": 0.2 OR "auto_size": True

- # "origin_at": "bottom", # "bottom" | "center"

- })

- poll_task("/openapi/v1/resize", resize_id)

- # UV Unwrap a GLB (5 credits). GLB only, ≤ 40,000 faces (else 400 → remesh down first).

- # Output: a GLB "UV white model" (fresh UVs + placeholder grey material) for external texturing.

- uv_id = create_task("/openapi/v1/uv-unwrap", {

- "input_task_id": "TASK_ID", # or "model_url": "GLB_URL"

- })

- poll_task("/openapi/v1/uv-unwrap", uv_id)

- ```

- ---

- ### Creative Lab Consumer Products

- Turn a photo into a styled, printable physical product. Products: **figure**, **lamp**, **keychain**, **fridge-magnet**. Two stages (replace `{product}` with one of those):

- 1. **Prototype** (6 credits): photo → styled concept image.

- 2. **Build** (30 credits): runs image-to-3d on the SUCCEEDED prototype → textured GLB / OBJ+MTL.

- ```python

- PRODUCT = "figure" # figure | lamp | keychain | fridge-magnet

- # Stage 1 — prototype (6 credits)

- proto_id = create_task(f"/openapi/creative-lab/{PRODUCT}/v1/prototype", {

- "image_url": "PHOTO_URL_OR_DATA_URI", # jpg/jpeg/png/webp

- # "name": "My figure", # optional, ≤ 100 chars

- })

- poll_task(f"/openapi/creative-lab/{PRODUCT}/v1/prototype", proto_id)

- # Stage 2 — build (30 credits). Must reference a SUCCEEDED prototype of the SAME product.

- # Web-app prototypes are rejected with 404 — only API-created prototypes are accepted.

- build_id = create_task(f"/openapi/creative-lab/{PRODUCT}/v1/build", {

- "input_task_id": proto_id,

- })

- build = poll_task(f"/openapi/creative-lab/{PRODUCT}/v1/build", build_id)

- project_dir = get_project_dir(build_id, task_type=f"creative-lab-{PRODUCT}")

- download(build["model_urls"]["glb"], os.path.join(project_dir, "creative-lab.glb"))

- # To MULTICOLOR a Creative Lab result: a Creative Lab model can only be sent to multi-color

- # as model_url — pass the build's GLB URL (or a data URI of the downloaded GLB):

- # create_task("/openapi/v1/print/multi-color", {"model_url": build["model_urls"]["glb"], "max_colors": 4})

- ```

- ---

- ### Auto-Rigging + Animation

- **When the user asks to rig or animate, the generation step MUST use `pose_mode: "t-pose"`.**

- ```python

- # 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 / Image to Image

+ | `check-env` | Step 0 environment report |

+ | `balance` | Current credit balance |

+ | `create --endpoint E (--payload JSON \| --payload-file F)` | Create a task; prints the new task ID |

+ | `poll --endpoint E --task-id ID [--timeout 300] [--project-dir D]` | Poll to completion; saves the task JSON into the project dir |

+ | `get --endpoint E --task-id ID [--save F]` | One-shot status / progress / face_count check |

+ | `download (--url U \| --task-json F [--format FMT]) --output PATH` | Stream-download a model file |

+ | `project-dir --task-id ID [--prompt P]` | Create + print the project folder path |

+ | `record --project-dir D --task-id ID --task-type T --stage S [--files "a,b"]` | Update `metadata.json` + `history.json` |

+ | `thumbnail --project-dir D (--url U \| --task-json F)` | Save the project thumbnail |

+ | `check-faces --endpoint E --task-id ID [--max-faces 300000]` | Pre-rigging polycount gate |

- ```python

- # 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"]

+ Follow the matching recipe in [references/pipelines.md](references/pipelines.md): **Text to 3D** (preview → refine), **Image to 3D**, **Multi-Image to 3D**, **Retexture**, **Remesh**, **Convert / Resize / UV Unwrap**, **Auto-Rigging + Animation** (t-pose + face-count gate), **Text/Image to Image**.

- # 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)

- ```

+ **2D Optimization Pre-Step (strongly recommended):** prefer the image-to-3d route over direct text-to-3d — for a text-only request, first make a design image via `/openapi/v1/text-to-image` (`nano-banana-pro`; characters: `generate_multi_view: true` + `pose_mode`), then 3D-ify. For low-quality reference images, clean up first via `/openapi/v1/image-to-image`. 3–9 extra credits typically buy a noticeable quality bump. Skip when the user provides a clean studio shot, and always skip for Creative Lab products (they stylize internally).

---

## 3D Printing Workflow

**IMPORTANT: When the user's request involves 3D printing, use this section for the ENTIRE workflow — including model generation.** Do NOT run the generation workflows above and then come here. This section controls `target_formats` and other print-specific parameters from the start.

Trigger when the user mentions: print, 3d print, slicer, slice, bambu, orca, prusa, cura, multicolor, multi-color, 3mf, figurine, miniature, statue, physical model, desk toy, phone stand.

### Decision: White Model vs Multicolor

- 1. **Detect installed slicers** first (see script below)

+ 1. **Detect installed slicers** first: `python3 scripts/slicers.py detect`

2. Ask the user: "White model (single-color) or multicolor?"

3. If **multicolor**: check for multicolor-capable slicer (OrcaSlicer, Bambu Studio, Creality Print, Elegoo Slicer, Anycubic Slicer Next), ask max_colors (1-16, default 4) and max_depth (3-6, default 4), confirm cost: 40 credits (+10 if repair is needed)

4. **(Recommended)** After generation, run a **printability analysis** (`POST /openapi/v1/print/analyze`, FREE). Run **`POST /openapi/v1/print/repair`** (10 credits) only if status = error.

- ### Printability Analysis & Repair

- ```python

- # After the textured/final mesh is ready:

- INPUT_TASK_ID = refine_id # or whatever produced the print-ready mesh

- # input_task_id MUST refer to a Meshy 6 / Preview task. For Meshy 4/5 outputs,

- # pass `model_url` (the GLB download URL) instead.

- analyze_id = create_task("/openapi/v1/print/analyze", {

- "input_task_id": INPUT_TASK_ID,

- })

- analyze_task = poll_task("/openapi/v1/print/analyze", analyze_id)

- p = analyze_task.get("printability") or {}

- metrics = p.get("metrics", {})

- print(f"Printability: {p.get('status')}: {metrics}")

- if p.get("status") == "error":

- repair_id = create_task("/openapi/v1/print/repair", {

- "input_task_id": INPUT_TASK_ID, # output is GLB

- })

- repair_task = poll_task("/openapi/v1/print/repair", repair_id)

- repaired_url = next((u for u in repair_task["model_urls"].values() if u), None)

- # Use repaired_url for the next step (download / multicolor / slicer)

- ```

- **Status meanings**: `healthy` (print as-is) | `warning` (degenerate faces / holes — repair optional) | `error` (non-watertight / non-manifold — repair recommended) | `unknown` (analyze failed). Repair preserves geometry only, not textures — re-texture if needed for multicolor.

- ### Slicer Detection + Opening

- ```python

- import subprocess, shutil, platform, os, glob as glob_mod

- SLICER_MAP = {

- "OrcaSlicer": {"mac_app": "OrcaSlicer", "win_exe": "orca-slicer.exe", "win_dir": "OrcaSlicer", "linux_exe": "orca-slicer"},

- "Bambu Studio": {"mac_app": "BambuStudio", "win_exe": "bambu-studio.exe", "win_dir": "BambuStudio", "linux_exe": "bambu-studio"},

- "Creality Print": {"mac_app": "Creality Print", "win_exe": "CrealityPrint.exe", "win_dir": "Creality Print*", "linux_exe": None},

- "Elegoo Slicer": {"mac_app": "ElegooSlicer", "win_exe": "elegoo-slicer.exe", "win_dir": "ElegooSlicer", "linux_exe": None},

- "Anycubic Slicer Next": {"mac_app": "AnycubicSlicerNext", "win_exe": "AnycubicSlicerNext.exe", "win_dir": "AnycubicSlicerNext", "linux_exe": None},

- "PrusaSlicer": {"mac_app": "PrusaSlicer", "win_exe": "prusa-slicer.exe", "win_dir": "PrusaSlicer", "linux_exe": "prusa-slicer"},

- "UltiMaker Cura": {"mac_app": "UltiMaker Cura", "win_exe": "UltiMaker-Cura.exe", "win_dir": "UltiMaker Cura*", "linux_exe": None},

- }

- MULTICOLOR_SLICERS = {"OrcaSlicer", "Bambu Studio", "Creality Print", "Elegoo Slicer", "Anycubic Slicer Next"}

- def detect_slicers():

- found = []

- system = platform.system()

- for name, info in SLICER_MAP.items():

- path = None

- if system == "Darwin":

- app = info.get("mac_app")

- if app and os.path.exists(f"/Applications/{app}.app"):

- path = f"/Applications/{app}.app"

- elif system == "Windows":

- win_dir, win_exe = info.get("win_dir", ""), info.get("win_exe", "")

- for base in [os.environ.get("ProgramFiles", r"C:\Program Files"),

- os.environ.get("ProgramFiles(x86)", r"C:\Program Files (x86)")]:

- if "*" in win_dir:

- matches = glob_mod.glob(os.path.join(base, win_dir, win_exe))

- if matches: path = matches[0]; break

- else:

- candidate = os.path.join(base, win_dir, win_exe)

- if os.path.exists(candidate): path = candidate; break

- else:

- exe = info.get("linux_exe")

- if exe: path = shutil.which(exe)

- if path:

- found.append({"name": name, "path": path, "multicolor": name in MULTICOLOR_SLICERS})

- return found

- def open_in_slicer(file_path, slicer_name):

- info = SLICER_MAP.get(slicer_name, {})

- system, abs_path = platform.system(), os.path.abspath(file_path)

- if system == "Darwin":

- subprocess.run(["open", "-a", info.get("mac_app", slicer_name), abs_path])

- elif system == "Windows":

- exe_path = shutil.which(info.get("win_exe", ""))

- (subprocess.Popen([exe_path, abs_path]) if exe_path else os.startfile(abs_path))

- else:

- exe_path = shutil.which(info.get("linux_exe", ""))

- (subprocess.Popen([exe_path, abs_path]) if exe_path else subprocess.run(["xdg-open", abs_path]))

- print(f"Opened {abs_path} in {slicer_name}")

- # Several SEPARATE/unrelated models (e.g. results from different tasks)? Call this once per

- # model with a short gap — Bambu Studio especially may respond to only one if fired

- # back-to-back. (Parts of ONE model belong in a single project — not spaced.)

- # import time

- # for f in [model_a, model_b, model_c]:

- # open_in_slicer(f, slicer_name); time.sleep(2)

- slicers = detect_slicers()

- for s in slicers:

- mc = " [multicolor]" if s["multicolor"] else ""

- print(f" - {s['name']}{mc}: {s['path']}")

- ```

- ### White Model Pipeline

- | Step | Action | Credits |

- |------|--------|---------|

- | 1 | Generate untextured model | 20 |

- | 2 | Download OBJ | 0 |

- | 3 | Fix OBJ (`fix_obj_for_printing`) | 0 |

- | 4 | Open in slicer | 0 |

- Generate with `target_formats` including `"obj"`, then fix for printing:

- ```python

- # --- Generate for white model printing ---

- # Text to 3D:

- task_id = create_task("/openapi/v2/text-to-3d", {

- "mode": "preview", "prompt": "USER_PROMPT", "ai_model": "latest",

- "target_formats": ["obj"], # Only OBJ for white model printing

- })

- # OR Image to 3D:

- # task_id = create_task("/openapi/v1/image-to-3d", {

- # "image_url": "URL", "should_texture": False,

- # "target_formats": ["glb", "obj"],

- # })

- task = poll_task("/openapi/v2/text-to-3d", task_id)

- project_dir = get_project_dir(task_id, "print")

- obj_url = task["model_urls"].get("obj") or task["model_urls"].get("glb")

- obj_path = os.path.join(project_dir, "model.obj")

- download(obj_url, obj_path)

- def fix_obj_for_printing(input_path, output_path=None, target_height_mm=75.0):

- if output_path is None: output_path = input_path

- lines = open(input_path, "r").readlines()

- rotated, min_x, max_x, min_y, max_y, min_z, max_z = [], float("inf"), float("-inf"), float("inf"), float("-inf"), 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()

- rotated.append(("vn", float(parts[1]), -float(parts[3]), float(parts[2]), []))

- else:

- rotated.append(("line", line))

- h = max_z - min_z

- s = target_height_mm / h if h > 1e-6 else 1.0

- xo, yo, zo = -(min_x+max_x)/2*s, -(min_y+max_y)/2*s, -(min_z*s)

- with open(output_path, "w") as f:

- for item in rotated:

- if item[0] == "v":

- _, rx, ry, rz, extra = item

- e = " "+" ".join(extra) if extra else ""

- f.write(f"v {rx*s+xo:.6f} {ry*s+yo:.6f} {rz*s+zo:.6f}{e}\n")

- elif item[0] == "vn":

- f.write(f"vn {item[1]:.6f} {item[2]:.6f} {item[3]:.6f}\n")

- else:

- f.write(item[1])

- print(f"OBJ fixed: scaled to {target_height_mm:.0f}mm, Z-up, centered")

- fix_obj_for_printing(obj_path, target_height_mm=75.0)

- if slicers: open_in_slicer(obj_path, slicers[0]["name"])

- ```

- ### Multicolor Pipeline

- | Step | Action | Credits |

- |------|--------|---------|

- | 1 | Generate + texture | 30 |

- | 2 | Multi-color processing | 10 |

- | 3 | Download 3MF | 0 |

- | 4 | Open in multicolor slicer | 0 |

- ```python

- mc_slicers = [s for s in slicers if s["multicolor"]]

- if not mc_slicers:

- print("WARNING: No multicolor slicer detected. Install: OrcaSlicer, Bambu Studio, etc.")

- # --- Generate + texture with target_formats including 3mf ---

- preview_id = create_task("/openapi/v2/text-to-3d", {

- "mode": "preview", "prompt": "USER_PROMPT", "ai_model": "latest",

- # No target_formats needed — 3MF comes from multi-color API

- })

- poll_task("/openapi/v2/text-to-3d", preview_id)

- refine_id = create_task("/openapi/v2/text-to-3d", {

- "mode": "refine", "preview_task_id": preview_id, "enable_pbr": True,

- })

- poll_task("/openapi/v2/text-to-3d", refine_id)

- project_dir = get_project_dir(preview_id, "multicolor-print")

- # --- Multi-color processing ---

- mc_task_id = create_task("/openapi/v1/print/multi-color", {

- "input_task_id": refine_id,

- "max_colors": 4, # 1-16, ask user

- "max_depth": 4, # 3-6, ask user

- })

- task = poll_task("/openapi/v1/print/multi-color", mc_task_id)

- threemf_path = os.path.join(project_dir, "multicolor.3mf")

- download(task["model_urls"]["3mf"], threemf_path)

- if mc_slicers: open_in_slicer(threemf_path, mc_slicers[0]["name"])

- ```

+ Then follow the full walkthroughs in [references/printing.md](references/printing.md):

- ### Printability Checklist

+ - **White Model Pipeline** (20 credits): generate untextured (`target_formats: ["obj"]`) → download OBJ → `scripts/fix_obj.py` (Y-up→Z-up, scale to mm, center, bottom at Z=0) → open in slicer

+ - **Multicolor Pipeline** (40 credits): generate + texture (refine/retexture REQUIRED) → multi-color API → download 3MF → open in multicolor slicer. The multi-color API outputs 3MF directly — no coordinate conversion, no `target_formats` needed at generation.

+ - **Creative Lab** (36 credits): photo → `prototype` (6) → `build` (30) → textured GLB, ready to print; multicolor via `model_url`.

+ - **Print-quality checklist** (wall thickness, overhangs, base stability, …) is in [references/printing.md](references/printing.md#manual-sanity-checks-in-addition-to-the-automated-analyze-api).

- | 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 |

+ Key rules: always detect slicer first and report; always run the FREE analyze for production/functional prints; repair only on `error` (or `warning` when quality matters); repair does NOT preserve textures (repair → re-texture → multicolor); if OBJ is unavailable, download GLB and import manually; after opening in a slicer, remind the user to check print settings (layer height, infill, supports).

---

## Step 3: Report Results

After task succeeds:

1. Downloaded file paths and sizes

2. Task IDs (for follow-up: refine, rig, retexture)

3. Available formats (list `model_urls` keys)

- 4. Credits consumed + current balance

+ 4. Credits consumed + current balance (task JSON has `consumed_credits`; run `balance`)

5. Suggested next steps:

- Preview done → "Want to refine (add textures)?"

- Model done → "Want to rig this character?"

- Rigged → "Want to apply a custom animation?"

- Any textured model → "Want to 3D print this? Multicolor printing is available!"

- Any model → "Want to 3D print this?"

---

## Error Recovery

- | 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 once

+ On any failure, follow [references/troubleshooting.md](references/troubleshooting.md): HTTP status handling (401/402/422/429/5xx), retry policy, and known task `FAILED` messages. The bundled CLI auto-reports the current balance on 402 and exits non-zero with the server's error message on failure.

---

## Known Behaviors & Constraints

- **99% stall**: Normal finalization (30–120s). Do NOT interrupt.

- **Asset retention**: Files deleted after **3 days** (non-Enterprise). Download immediately.

- **PBR maps**: Must set `enable_pbr: true` explicitly.

- **Refine**: Works with `meshy-5`, `meshy-6`, or `latest` — pick the same family as your preview for consistency. 10 credits regardless of model. (`meshy-4` is retired → 400.)

- **Deprecated params**: `symmetry_mode` no longer affects output; `art_style` is ignored by Meshy-6; use `pose_mode` instead of the old `is_a_t_pose` flag.

- **`consumed_credits`**: Every task GET response includes `consumed_credits` — read it to report the real credits spent rather than estimating.

- - **Rigging**: Humanoid bipedal only, polycount ≤ 300,000.

- - **Printing formats**: White model → OBJ with `fix_obj_for_printing()`. Multicolor → 3MF from Multi-Color Print API. Always detect slicer first.

+ - **Rigging**: Humanoid bipedal only, polycount ≤ 300,000 (enforced by `check-faces`).

+ - **Printing formats**: White model → OBJ with `scripts/fix_obj.py`. Multicolor → 3MF from Multi-Color Print API. Always detect slicer first.

- **Download format**: Ask the user which format they need before downloading. GLB (viewing), OBJ (printing), 3MF (multicolor), FBX (games), USDZ (AR). Do NOT download all formats.

- **3MF for multicolor**: Multi-Color Print API outputs 3MF directly — no need to request 3MF from generate/refine. For non-print use cases needing 3MF, pass `"3mf"` in `target_formats`.

- **Timestamps**: All API timestamps are Unix epoch **milliseconds**.

---

## Execution Checklist

- - [ ] Ran API key detection (Step 0) — checked env var and `.env` only

+ - [ ] Ran API key detection (`check-env`, Step 0) — checked env var and `.env` / `.env.local` only

- [ ] API key verified (never printed in full)

- [ ] Presented cost summary and got user confirmation

- - [ ] Wrote complete workflow as single Python script

- - [ ] Ran with `python3 -u` for unbuffered output

+ - [ ] Composed the workflow from bundled script calls (never retyped the helpers)

+ - [ ] Followed the matching recipe in references/pipelines.md or references/printing.md

- [ ] Reported file paths, formats, task IDs, and balance

- [ ] Suggested next steps

---

## Additional Resources

For the complete API endpoint reference including all parameters, response schemas, and error codes, read [reference.md](reference.md).