giggle-generation-video

Files (3)

• SKILL.md
• requirements.txt
• generation_api.py · scripts/

SKILL.md (first lines)

# Giggle Video Generation

**Source**: [giggle-official/skills](https://github.com/giggle-official/skills) · API: [giggle.pro](https://giggle.pro/)

Generates AI videos via giggle.pro's Generation API. Supports text-to-video and image-to-video. Submit task → **agent proactively polls** with `--query` until done (see **Continuous progress updates**). No Cron, no file writes—all operations via exec.

---

## Installation Requirements

| Requirement | Value |
|-------------|-------|
| **Binary** | `python3` |
| **Environment** | `GIGGLE_API_KEY` (required; see below) |
| **Pip** | `requests` |

**Where to get `GIGGLE_API_KEY`:** Open [giggle.pro](https://giggle.pro/) while **logged in** → **left sidebar** → **API Key** (**API 密钥**) → create or copy your key. Then `export GIGGLE_API_KEY=your_api_key`. The script will prompt if not configured.

> **No Retry on Error**: If script execution encounters an error, **do not retry**. Report the error to the user directly and stop.

---

## Supported Models

| Model | Supported Durations (s) | Default | Description |
|-------|-------------------------|---------|-------------|
| grok | 6, 10 | 6 | Strong overall capability, recommended |
| grok-fast | 6, 10 | 6 | grok fast version |
| sora2 | 4, 8, 12 | 4 | OpenAI Sora 2 |
| sora2-pro | 4, 8, 12 | 4 | Sora 2 Pro |
| sora2-fast | 10, 15 | 10 | Sora 2 Fast |
| sora2-pro-fast | 10, 15 | 10 | Sora 2 Pro Fast |
| kling25 | 5, 10 | 5 | Kling video model |
| seedance15-pro | 4, 8, 12 | 4 | Seedance Pro (with audio) |
| seedance15-pro-no-audio | 4, 8, 12 | 4 | Seedance Pro (no audio) |
| veo31 | 4, 6, 8 | 4 | Google Veo 3.1 (with audio) |
| veo31-no-audio | 4, 6, 8 | 4 | Google Veo 3.1 (no audio) |
| minimax23 | 6 | 6 | MiniMax model |
| wan25 | 5, 10 | 0 | Wanxiang model |

**Note**: `--duration` must be chosen from the model's supported durations, otherwise the API will error.

---

## Frame Reference (Image-to-Video)

For image-to-video, `--start-frame` and `--end-frame` support three mutually exclusive formats:

| Method | Format | Example |
|--------|--------|---------|
| asset_id | `asset_id:<ID>` | `asset_id:lkllv0yv81` |
| url | `url:<URL>` | `url:https://example.com/img.jpg` |
| base64 | `base64:<DATA>` | `base64:iVBORw0KGgo...` |

Each frame parameter can only use one of these methods.

---

## Execution Flow: Submit and Query

Video generation is asynchronous (typically 60–300 seconds). **Submit** a task to get `task_id`, then **query** until the task reaches a terminal state. All commands run via `exec`; API key from system env.

---

## Continuous progress updates (default; user need not put this in their prompt)

Video jobs are usually **~1–5+ minutes** (sometimes longer). The user does **not** need to ask you to check progress.

1. **Right after submit**, say you submitted, give `task_id`, and give a **realistic** wait range for the model/load (not a generic “1–3 minutes” if the pipeline is often slower).
2. **Poll proactively**: run `--query` about every **15–30 seconds** until a terminal state—**do not** wait for the user to ask.
3. **After each query**, briefly report status in natural language; if stdout is non-terminal JSON (`processing`, etc.), paraphrase and say you will check again—**do not** go silent.
4. **When done**: forward full **signed video** links on success; explain failures clearly. If still non-terminal after **~20 minutes** (adjust if the user/model context suggests longer is normal), stop blind polling, explain, keep `task_id`, and suggest retry or a later manual check.
5. **If the user explicitly opts out** of polling (“don’t poll”, “I’ll ask”), submit once, give `task_id`, and only `--query` when they ask.

---

### Step 1: Submit Task

**First send a message to the user**: Video generation is submitted; you will query progress on a schedule and report updates—no need to nag. Include `task_id` once you have it from the JSON response.

```bash
# Text-to-video (default grok-fast)
python3 scripts/generation_api.py \
  --prompt "Camera slowly pushes forward, person smiling in frame" \
  --model grok-fast --duration 6 \
  --aspect-ratio 16:9 --resolution 720p

# Image-to-video - use asset_id as start frame
python3 scripts/generation_api.py \
  --prompt "Person slowly turns around" \
  --start-frame "asset_id:lkllv0yv81" \
  --model grok-fast --duration 6 \
  --aspect-ratio 16:9 --resolution 720p

# Image-to-video - use URL as start frame
python3 scripts/generation_api.py \
  --prompt "Scenery from still to motion" \
  --start-frame "url:https://example.com/img.jpg" \
  --model grok-fast --duration 6

# Image-to-video - both start and end frame
python3 scripts/generation_api.py \
  --prompt "Scene transition" \
  --start-frame "asset_id:abc123" \
  --end-frame "url:https://example.com/end.jpg" \
  --model grok --duration 6
```

Response example:
```json
{"status": "started", "task_id": "55bf24ca-e92a-4d9b-a172-8f585a7c5969"}
```

**Store task_id in memory** (`addMemory`):
```
giggle-generation-video task_id: xxx (submitted: YYYY-MM-DD HH:mm)
```

---

From giggle-official/skills, MIT.

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