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/aliyun-happyhorse-r2v

Use when generating videos that fuse 1-9 reference images with DashScope HappyHorse 1.0 reference-to-video model (happyhorse-1.0-r2v). Use when implementing multi-subject reference-to-video synthesis where prompts cite the input images as character1..N via the video-synthesis

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Install
$ npx -y skills add cinience/alicloud-skills --skill aliyun-happyhorse-r2v --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/aliyun-happyhorse-r2v

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when generating videos that fuse 1-9 reference images with DashScope HappyHorse 1.0 reference-to-video model (happyhorse-1.0-r2v). Use when implementing multi-subject reference-to-video synthesis where prompts cite the input images as character1..N via the video-synthesis

SKILL.md

aliyun-happyhorse-r2v.SKILL.md
name: aliyun-happyhorse-r2v
description: Use when generating videos that fuse 1-9 reference images with DashScope HappyHorse 1.0 reference-to-video model (happyhorse-1.0-r2v). Use when implementing multi-subject reference-to-video synthesis where prompts cite the input images as character1..N via the video-synthesis async API.

HappyHorse 1.0 Reference-to-Video

Validation

mkdir -p output/aliyun-happyhorse-r2v
python -m py_compile skills/ai/video/aliyun-happyhorse-r2v/scripts/r2v_happyhorse.py && echo "py_compile_ok" > output/aliyun-happyhorse-r2v/validate.txt

Pass criteria: command exits 0 and `output/aliyun-happyhorse-r2v/validate.txt` is generated.

Output And Evidence

  • Save task IDs, polling responses, and final video URLs to `output/aliyun-happyhorse-r2v/`.
  • Keep at least one end-to-end run log for troubleshooting.

Prerequisites

  • Install dependencies (recommended in a venv):
python3 -m venv .venv
. .venv/bin/activate
python -m pip install requests
  • Set `DASHSCOPE_API_KEY` in your environment, or add `dashscope_api_key` to `~/.alibabacloud/credentials`.

Critical model names

  • `happyhorse-1.0-r2v` — reference-to-video; 1-9 reference images fused into a single video, with `character1..N` references in the prompt

Capabilities

| Capability | Description | Required media | |---|---|---| | Reference-to-video | Generate a video by fusing multiple subject/object reference images, guided by a prompt that references them as `character1`, `character2`, ... in input order | 1-9 `reference_image` entries |

API endpoint (async only)

POST https://dashscope.aliyuncs.com/api/v1/services/aigc/video-generation/video-synthesis

Required headers:

  • `Authorization: Bearer $DASHSCOPE_API_KEY`
  • `Content-Type: application/json`
  • `X-DashScope-Async: enable`

Singapore endpoint: replace `dashscope.aliyuncs.com` with `dashscope-intl.aliyuncs.com`.

Polling endpoint: `GET https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}` — recommended interval 15s.

Normalized interface

Request

  • `model` (string, required) — fixed `happyhorse-1.0-r2v`
  • `input.prompt` (string, required) — up to 5000 non-CJK / 2500 CJK characters; reference subjects via `character1`, `character2`, ... matching `media` array order
  • `input.media` (array, required) — 1 to 9 elements, each:
  • `type`: `reference_image`
  • `url`: public HTTP/HTTPS URL of a reference image
  • `parameters.resolution` (string, optional) — `720P` or `1080P` (default: `1080P`)
  • `parameters.ratio` (string, optional) — `16:9` (default), `9:16`, `1:1`, `4:3`, `3:4`
  • `parameters.duration` (integer, optional) — video length in seconds, range [3, 15] (default: 5)
  • `parameters.watermark` (boolean, optional) — bottom-right "Happy Horse" watermark (default: `true`)
  • `parameters.seed` (integer, optional) — range [0, 2147483647]

Media input limits

**Reference image** (`type=reference_image`):

  • Formats: JPEG, JPG, PNG, WEBP
  • Resolution: short side ≥ 400 pixels (720P or higher recommended)
  • Max size: 10 MB per image
  • Avoid blurry, over-compressed, or very small images

Character indexing rule

The first `reference_image` in the `media` array maps to `character1`, the second to `character2`, and so on up to `character9`. Reorder the array if you want a specific reference to bind to a specific `characterN`.

Response (task creation)

  • `output.task_id` (string) — valid 24 hours
  • `output.task_status` (string) — `PENDING` | `RUNNING` | `SUCCEEDED` | `FAILED` | `CANCELED` | `UNKNOWN`
  • `request_id` (string)

Response (task result, on SUCCEEDED)

  • `output.video_url` (string) — generated MP4 (H.264) URL, valid 24 hours
  • `output.orig_prompt` (string)
  • `output.submit_time` / `output.scheduled_time` / `output.end_time` (string)
  • `usage.duration` (integer) — billable duration in seconds
  • `usage.output_video_duration` (integer)
  • `usage.input_video_duration` (integer) — fixed 0 for r2v
  • `usage.SR` (integer) — output resolution tier
  • `usage.ratio` (string)
  • `usage.video_count` (integer) — fixed 1

Quick start (Python + HTTP)

import os
import time
import requests

API_KEY = os.getenv("DASHSCOPE_API_KEY")
BASE_URL = "https://dashscope.aliyuncs.com/api/v1"


def create_r2v_task(req: dict) -> str:
    """Create a reference-to-video task and return task_id."""
    refs = req["reference_images"]
    if not 1 <= len(refs) <= 9:
        raise ValueError("Need 1-9 reference images")
    payload = {
        "model": "happyhorse-1.0-r2v",
        "input": {
            "prompt": req["prompt"],
            "media": [{"type": "reference_image", "url": u} for u in refs],
        },
        "parameters": {
            "resolution": req.get("resolution", "1080P"),
            "ratio": req.get("ratio", "16:9"),
            "duration": req.get("duration", 5),
            "watermark": req.get("watermark", True),
        },
    }
    if req.get("seed") is not None:
        payload["parameters"]["seed"] = req["seed"]

    resp = requests.post(
        f"{BASE_URL}/services/aigc/video-generation/video-synthesis",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json",
            "X-DashScope-Async": "enable",
        },
        json=payload,
    )
    resp.raise_for_status()
    return resp.json()["output"]["task_id"]


def poll_task(task_id: str, interval: int = 15) -> dict:
    while True:
        resp = requests.get(
            f"{BASE_URL}/tasks/{task_id}",
            headers={"Authorization": f"Bearer {API_KEY}"},
        )
        resp.raise_for_status()
        data = resp.json()
        if data["output"]["task_status"] in ("SUCCEEDED", "FAILED", "CANCELED"):
            return data
        time.sleep(interval)

Usage examples

# Single subject — character1 references the only image
task_id = create_r2v_task({
    "reference_images": ["https://example.com/girl.jpg"],
    "prompt": "character1 walk
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