meshy-3d-generation

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

python3 scripts/meshy_task.py check-env

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?

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

---
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.

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 |

---

## Security & Data Handling

- **API key (`MESHY_API_KEY`)** — sent only in the HTTP `Authorization: Bearer` header to `https://api.meshy.ai`. Never logged in full (only a `key[:8]...` prefix is ever printed). The bundled script never persists it; it is written to `.env` in the current working directory *only* when the user explicitly asks, and never to shell profiles, Windows user variables, or any path outside the working directory (see [references/setup.md](references/setup.md)).
- **Key sources read** — the current session environment, then `.env` / `.env.local` in the current working directory. Home directories and shell profiles are never scanned.
- **Network** — the only external endpoint is `https://api.meshy.ai`. System proxies are bypassed (`trust_env = False`) so the key is never handed to an environment-configured proxy.
- **Filesystem writes** — `.env` in the working directory (on explicit request only) and `./meshy_output/` for downloaded models, thumbnails, and metadata. Input files (e.g. local images for image-to-3D) are read only at the exact path the user provides.
- **Data leaving the machine** — the API key, user-provided text prompts, and image URLs/data go to `api.meshy.ai` only. No other local data is transmitted; downloaded assets are saved locally.

---

## 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 `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 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 polling for completion:

- 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.

---

## IMPORTANT: Never Rebuild Bundled Scripts

`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.

---

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

Before any API call, run the bundled environment check:

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

```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

- **`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)

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`.

**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

| User wants to... | API | Endpoint | Credits |
|---|---|---|---|
| 3D model from text | Text to 3D | `POST /openapi/v2/text-to-3d` | 5–20 (preview) + 10 (refine) |
| 3D model from one image | Image to 3D | `POST /openapi/v1/image-to-3d` | 5–30 |
| 3D model from multiple images | Multi-Image to 3D | `POST /openapi/v1/multi-image-to-3d` | 5–30 |
| New textures on existing model | Retexture | `POST /openapi/v1/retexture` | 10 |
| Change mesh format/topology | Remesh | `POST /openapi/v1/remesh` | 5 |
| Convert a model to other formats (no remesh) | Convert | `POST /openapi/v1/convert` | 1 |
| Rescale a model to real-world size | Resize | `POST /openapi/v1/resize` | 1 |
| Generate fresh UVs (GLB, ≤40k faces) before external texturing | UV Unwrap | `POST /openapi/v1/uv-unwrap` | 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 |
| Browse animations to pick an `action_id` | Animation Library (public, **no API key**) | `GET https://api.meshy.ai/web/public/animations/resources` | 0 |
| 2D image from text (recommended pre-step before image-to-3d) | Text to Image | `POST /openapi/v1/text-to-image` | 3 / 6 / 9 / 9 |
| Optimize/edit a 2D image (recommended pre-step before image-to-3d) | Image to Image | `POST /openapi/v1/image-to-image` | 3 / 6 / 9 / 12 |
| Check FDM printability (watertight / non-manifold edges / holes) | Analyze Printability | `POST /openapi/v1/print/analyze` | **0 (free)** |
| Repair non-manifold/degenerate-face/hole topology | Repair Printability | `POST /openapi/v1/print/repair` | 10 |
| Multi-color 3D print | Multi-Color Print | `POST /openapi/v1/print/multi-color` | 10 |
| 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) |
| Check credit balance | Balance | `GET /openapi/v1/balance` | 0 |

---

## 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.

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

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

| 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