v0.4.1 to v0.4.1

59 added, 466 removed. Audit A to A.

---

name: meshy-3d-generation

description: Generate 3D models, textures, images, rig characters, and animate them using the Meshy AI API. Handles API key detection, setup, and all generation workflows via direct HTTP calls. Use when the user asks to create 3D models, convert text/images to 3D, texture models, rig or animate characters, or interact with the Meshy API. For 3D printing requests, use the meshy-3d-printing skill instead.

license: MIT

compatibility: Requires Python 3 with requests package. Works with Claude Code, Cursor, and all Agent Skills compatible tools.

metadata:

author: meshy-dev

version: "0.4.1"

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

allowed-tools: Bash, Read, Write, Glob, Grep

---

# 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).

+ 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 API call and file operation (Step 2) |

+ | [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) | Per-endpoint recipes: exact payloads + script calls for each workflow |

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

---

## IMPORTANT: 3D Printing → Use `meshy-3d-printing` Skill

**If the user's request involves 3D printing** (keywords: print, 3d print, slicer, slice, bambu, orca, prusa, cura, multicolor, 3mf, figurine, miniature, statue, physical model), **use the `meshy-3d-printing` skill instead of this one for the entire workflow.** The printing skill handles generation with correct print-optimized parameters (e.g. `target_formats` with `"3mf"` for multicolor), slicer detection, coordinate conversion, and slicer launch — all in one pipeline.

- This skill's `create_task`/`poll_task`/`download` template functions are reused by the printing skill, but the **workflow orchestration** (what to generate, which formats, what to do after) must come from the printing skill when printing is involved.

+ This skill's `scripts/meshy_task.py` is reused by the printing skill, but the **workflow orchestration** (what to generate, which formats, what to do after) must come from the printing skill when printing is involved.

**Do NOT generate a model with this skill and then hand off to the printing skill** — the printing skill needs to control parameters from the start (e.g. `target_formats`, `should_texture`).

---

## 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.

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

---

## 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:

+ Meshy generation tasks take 1–5 minutes. When polling 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.

+ - The bundled CLI prints unbuffered progress in real time — run each `poll` as a single Bash call and let it finish.

+ - Be patient with long-running polls — do NOT interrupt or kill them prematurely. Tasks at 99% for 30–120s is normal finalization, not a failure.

+ - Pass a larger `--timeout` (e.g. `--timeout 600`) for heavy tasks instead of retrying a timed-out poll.

---

- ## Step 0: Environment Detection (ALWAYS RUN FIRST)

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

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

+ ## IMPORTANT: Never Rebuild Bundled Scripts

- ```bash

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

+ `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 ...`. Reimplementing them inline causes silent behavior drift and doubles the token cost of every run.

- # 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 MESHY_API_KEY="$(grep '^MESHY_API_KEY=' "$f" | head -1 | cut -d= -f2- | tr -d "\"'")"

- fi

- done

+ ## Step 0: Environment Detection (ALWAYS RUN FIRST)

- # 3. Final status

- if [ -n "$MESHY_API_KEY" ]; then

- echo "READY: key=${MESHY_API_KEY:0:12}..."

- else

- echo "READY: NO_KEY_FOUND"

- fi

+ Before any API call, run the bundled environment check:

- # 4. Python requests check

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

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

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

+ ```bash

+ python3 scripts/meshy_task.py check-env

```

+ It reports `ENV_VAR` (current environment), `DOTENV` (`.env` / `.env.local` in the working directory), `PYTHON_REQUESTS`, and a final `READY:` line. The bundled CLI loads the key itself (env var → `.env` → `.env.local`), so no manual `export` is needed to use it.

### 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. 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 for the **current session** and verify:

- **macOS / Linux:**

- ```bash

- 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"

- else

- echo "Key invalid (HTTP $STATUS). Check the key and try again."

- fi

- ```

- **Windows (PowerShell):**

- ```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."

- } else {

- Write-Host "Key invalid (HTTP $status). Check the key and try again."

- }

- ```

- **Do NOT persist the key yourself.** Never write the API key to shell profiles (`~/.zshrc`, `~/.bashrc`, …), Windows user environment variables, or any file outside the current working directory — the key would end up in shell history, the agent transcript, and long-lived config at once. The only file you may write it to is `.env` in the current working directory, and only when the user explicitly asks.

- Once the key verifies, print these instructions so the **user** can persist it themselves:

+ Follow [references/setup.md](references/setup.md). It walks the user through creating a key at https://www.meshy.ai/settings/api (Pro plan required), setting it for the **current session only**, and verifying it against `GET /openapi/v1/balance`.

- > To keep the key across sessions, pick one:

- >

- > - **macOS / Linux:** add `export MESHY_API_KEY="msy_..."` to your shell profile (`~/.zshrc` or `~/.bashrc`) and restart your terminal.

- > - **Windows:** Settings → "Edit environment variables for your account" → add a user variable `MESHY_API_KEY`, then restart your terminal.

- > - **Any platform:** create a `.env` file in your project root containing `MESHY_API_KEY=msy_...` (remember to add `.env` to your `.gitignore`). If you ask me to, I can create this project-local `.env` for you.

+ **Never persist the key yourself** — no shell profiles, no Windows user environment variables, no file outside the current working directory. The only exception is `.env` in the working directory, and only when the user explicitly asks. Otherwise print the persistence instructions and let the user apply them.

---

## 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 "<prompt>" using the following plan:

1. Preview (mesh generation) — 5-20 credits (meshy-6/lowpoly: 20, others: 5)

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 (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

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

| Stylized printable product from a photo (figure / lamp / keychain / fridge-magnet) | Creative Lab — **see the `meshy-3d-printing` skill** for the full prototype→build flow | `POST /openapi/creative-lab/{product}/v1/{prototype,build}` | 36 (6+30) |

---

## Step 2: Execute the Workflow

### CRITICAL: Async Task Model

All generation endpoints return `{"result": "<task_id>"}`, 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:

- ```python

- #!/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=300):

- """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 < 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)

- filled = int(progress / 5)

- bar = f"[{'█' * filled}{'░' * (20 - filled)}] {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 to the given path (within a project directory)."""

- 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)

- size_mb = os.path.getsize(filepath) / (1024 * 1024)

- print(f"DOWNLOADED: {filepath} ({size_mb:.1f} MB)")

- # --- File organization helpers (see File Organization section above) ---

- import re, json

- from datetime import datetime

- 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"):

- timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

- slug = re.sub(r'[^a-z0-9]+', '-', (prompt or task_type).lower())[:30].strip('-')

- folder = f"{timestamp}_{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")

- if os.path.exists(meta_path):

- meta = json.load(open(meta_path))

- else:

- meta = {"project_name": prompt or task_type, "folder": os.path.basename(project_dir),

- "root_task_id": task_id, "created_at": datetime.now().isoformat(),

- "updated_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)

- # Update global history

- if os.path.exists(HISTORY_FILE):

- history = json.load(open(HISTORY_FILE))

- else:

- history = {"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["task_count"] = len(meta["tasks"])

- entry["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 this to the template above and run as one script:

- ```python

- PROMPT = "USER_PROMPT" # max 600 chars

- # --- Preview ---

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

- "mode": "preview",

- "prompt": PROMPT,

- "ai_model": "latest",

- # "model_type": "standard", # "standard" | "lowpoly"

- # "topology": "triangle", # "triangle" | "quad"

- # "target_polycount": 30000, # 100–300000

- # "should_remesh": False,

- # "pose_mode": "t-pose", # "" | "a-pose" | "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"])

+ ### The Bundled CLI: `scripts/meshy_task.py`

- print(f"\nPREVIEW COMPLETE")

- print(f" Task ID: {preview_id}")

- print(f" Project: {project_dir}")

- print(f" Formats: {', '.join(task['model_urls'].keys())}")

+ Every workflow is a sequence of calls to the bundled CLI — do not write your own API code:

- # --- Refine ---

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

- "mode": "refine",

- "preview_task_id": preview_id,

- "enable_pbr": True,

- "ai_model": "latest",

- # "texture_prompt": "",

- # "remove_lighting": True, # Remove baked lighting (meshy-6/latest only, default True)

- })

+ | Subcommand | Purpose |

+ |---|---|

+ | `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 |

- 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"])

+ ### Pick the Workflow

- print(f"\nREFINE COMPLETE")

- print(f" Task ID: {refine_id}")

- print(f" Project: {project_dir}")

- print(f" Formats: {', '.join(task['model_urls'].keys())}")

- ```

+ Follow the matching recipe in [references/pipelines.md](references/pipelines.md) — each lists the exact payload options and the full create → poll → download → record call sequence:

- > **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.)

+ - **Text to 3D** (preview → refine) — the default for "make a 3D model of X"

+ - **Image to 3D** / **Multi-Image to 3D**

+ - **Retexture** / **Remesh**

+ - **Convert / Resize / UV Unwrap** (lightweight mesh utilities)

+ - **Auto-Rigging + Animation** — requires t-pose + a face-count gate; rigging includes walking/running for free

+ - **Text to Image** / **Image to Image** — see the 2D pre-step below

### (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.

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:

| User input | Recommended pre-step |

|---|---|

| 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 (remove background, raise resolution, normalize lighting, fill occlusions). |

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

The optimized image URL feeds directly into `/openapi/v1/image-to-3d`'s `image_url`. **3-9 extra credits typically buy a noticeable quality bump**, and downstream `refine` / texture-on-mesh stages benefit too.

**Skip when**: the user already provided a clean front-facing studio shot — go straight to image-to-3d. Also skip for **Creative Lab** products (figure / lamp / keychain / fridge-magnet): they apply their own built-in stylization, so feed the raw photo (or text, for lamp) straight to Creative Lab — do not pre-generate a design image.

- ```python

- # Example: text-only request → text-to-image → image-to-3d

- img_id = create_task("/openapi/v1/text-to-image", {

- "ai_model": "nano-banana-pro",

- "prompt": "studio render of a sci-fi helmet, neutral background, even lighting",

- "aspect_ratio": "1:1",

- # "generate_multi_view": True, # for character meshes use multi-view + pose_mode

- })

- img_task = poll_task("/openapi/v1/text-to-image", img_id)

- generated_image_url = img_task["image_urls"][0] # use as input for image-to-3d below

- ```

- ### 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

- ```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, # 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

- **IMPORTANT**: Before calling, ask the user to provide a texture style:

- - **Text prompt**: e.g. "rusty metal", "cartoon style" → `text_style_prompt`

- - **Reference image**: URL of style image → `image_style_url`

- One of these is **required**. If both provided, `image_style_url` takes precedence.

- ```python

- # REQUIRED: ask user for text_style_prompt OR image_style_url before calling

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

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

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

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

- "enable_pbr": True,

- # "remove_lighting": True, # Remove baked lighting (meshy-6/latest only, default True)

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

- # "auto_size": True, # AI auto-estimate real-world height

- })

- task = poll_task("/openapi/v1/retexture", task_id)

- download(task["model_urls"]["glb"], "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)

- for fmt, url in task["model_urls"].items():

- download(url, 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)

- ```

- ### 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:

- ```python

- # 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")

- ```

- ```python

- # 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

- ```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: 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 — may include glb, fbx, obj, usdz, 3mf)

4. **Thumbnail URL** if present

- 5. **Credits consumed** and remaining balance (run balance check)

+ 5. **Credits consumed** and remaining balance (run `balance`; each task JSON also has `consumed_credits`)

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 textured model → "Want to 3D print this? Multicolor printing is available!" (requires `meshy-3d-printing` skill)

- 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 | 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 already auto-reports the current balance on 402 and exits non-zero with the server's error message on failure.

---

## Known Behaviors & Constraints

- **99% progress stall**: Tasks commonly sit at 99% for 30–120s during finalization. This is normal. Do NOT kill or restart.

- **CORS**: API blocks browser requests. Always server-side.

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

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

- - **Format availability**: Check keys in `model_urls` before downloading — not all formats are always present. 3MF is available from the Multi-Color Print API.

+ - **Format availability**: Check keys in `model_urls` before downloading — not all formats are always present (the `poll` summary lists them). 3MF is available from the Multi-Color Print API.

- **Download format**: ALWAYS ask the user which format they need before downloading. Recommend: GLB (viewing), OBJ (white model printing), 3MF (multicolor printing), FBX (game engines), USDZ (AR). Do NOT download all formats.

- **3MF format**: 3MF is NOT included in default output of generation endpoints. To get 3MF, pass `"3mf"` in `target_formats` on generate/refine/remesh/retexture, or use the Convert API (`POST /openapi/v1/convert`, 1 credit). For multicolor 3D printing, the Multi-Color Print API outputs 3MF directly — no need to request it from generate/refine.

- **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. `meshy-4` is retired (returns 400).

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

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

- - **Large files**: Refined models can be 50–200 MB. Use streaming downloads with timeouts.

+ - **Large files**: Refined models can be 50–200 MB. The CLI streams downloads with timeouts; just be patient.

---

## Execution Checklist

- - [ ] Ran environment detection (Step 0)

+ - [ ] Ran environment detection (`check-env`, Step 0)

- [ ] API key present and verified

- [ ] Presented cost summary and got user confirmation

- - [ ] Wrote complete workflow as single Python script

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

+ - [ ] Composed the workflow from bundled `scripts/meshy_task.py` calls (never retyped the helpers)

+ - [ ] Followed the matching recipe in [references/pipelines.md](references/pipelines.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, deprecated fields, and detailed error codes, read [reference.md](reference.md).