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Agent skill

Aliyun Happyhorse Videoedit

by cinience in cinience/alicloud-skills

A skill your agent uses when editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit).

MITAuto-check passedMedia & Creative

Install Aliyun Happyhorse Videoedit

skills CLI
$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-videoedit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cinience/alicloud-skills aliyun-happyhorse-videoedit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-videoedit .claude/skills/aliyun-happyhorse-videoedit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aliyun-happyhorse-videoedit
GitHub stars
397
Token cost
~2.1k tokens
SKILL.md length
601 words
Files
4 (incl. scripts, references)
Skills in repo
96
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit).

  • Works in 4 steps: Confirm intent: style transfer vs.… → Validate the input video meets format /… → Build the media array with exactly 1… → …
  • Editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit)
  • SKILL.md covers Validation, Output And Evidence, Prerequisites and Critical model names, plus 10 more sections
  • Runs Python scripts from its folder; calls python and python3; reaches dashscope.aliyuncs.com; needs DASHSCOPE_API_KEY and API_KEY

What it does

Aliyun Happyhorse Videoedit is an agent skill from cinience/alicloud-skills. Use when editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit). Use when implementing instruction-based video editing such as style transfer or local replacement, optionally guided by 0-5 reference images, via the video-synthesis async API on Alibaba Cloud Model Studio.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api_reference.md`, `references/sources.md` and `scripts/edit_happyhorse.py`).

It sits in Media & Creative, covering Video production and AI video generation. It works with Alibaba Cloud. The repository describes itself as: alibaba cloud skills，qwen ，wan and all skills. The licence is MIT.

When your agent uses it

  • Editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit)
  • Implementing instruction-based video editing such as style transfer
  • Local replacement
  • Optionally guided by 0-5 reference images

Example prompts

  • “/aliyun-happyhorse-videoedit”

Requirements

  • Python 3
  • A credential in DASHSCOPE_API_KEY
  • A credential in API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Confirm intent: style transfer vs. instruction edit, and whether reference images are needed.
  2. Validate the input video meets format / duration / resolution / fps / size limits.
  3. Build the media array with exactly 1 video plus 0-5 reference_image entries.
  4. Create async task and poll /tasks/{task_id} every ~15s; download output.video_url before 24-hour expiration.

What it can do on your machine

Read from SKILL.md and the folder at commit 1818263. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • dashscope.aliyuncs.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DASHSCOPE_API_KEY
    • API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aliyun Happyhorse Videoedit loads about 2.1k tokens when it runs, and up to ~3.4k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 601 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from cinience/alicloud-skills at commit 1818263, republished under its MIT licence (© cinience). 601 words, ~2,086 tokens.

Download SKILL.mdSave it as .claude/skills/aliyun-happyhorse-videoedit/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
aliyun-happyhorse-videoedit
description
Use when editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit). Use when implementing instruction-based video editing such as style transfer or local replacement, optionally guided by 0-5 reference images, via the video-synthesis async API on Alibaba Cloud Model Studio.

HappyHorse 1.0 Video Editing

Validation

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

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

Output And Evidence

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

Prerequisites

  • Install dependencies (recommended in a venv):
bash
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-video-edit — instruction-based video editing with optional reference images and audio retention control

Capabilities

CapabilityDescriptionRequired media
Style transferConvert the input video to a different visual style via a text instructionexactly 1 video
Local replacement / instruction editReplace or modify subjects guided by a prompt and optional reference images1 video + 0-5 reference_image

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-video-edit
  • input.prompt (string, required) — up to 5000 non-CJK / 2500 CJK characters describing the edit
  • input.media (array, required) — exactly 1 video element, plus 0-5 reference_image elements:
    • type: video (required, exactly 1) | reference_image (optional, 0-5)
    • url: public HTTP/HTTPS URL
  • parameters.resolution (string, optional) — 720P or 1080P (default: 1080P)
  • parameters.audio_setting (string, optional) — auto (default, model decides) or origin (keep input audio)
  • parameters.watermark (boolean, optional) — bottom-right "Happy Horse" watermark (default: true)
  • parameters.seed (integer, optional) — range [0, 2147483647]
Media input limits

Input video (type=video):

  • Formats: MP4, MOV (H.264 encoding recommended)
  • Duration: 3-60 seconds (output is capped at 15s; videos >15s are truncated to the first 15s)
  • Resolution: long side ≤ 2160 px, short side ≥ 320 px
  • Aspect ratio: 1:2.5 ~ 2.5:1
  • Frame rate: > 8 fps
  • Max size: 100 MB

Reference image (type=reference_image):

  • Formats: JPEG, JPG, PNG, WEBP
  • Resolution: width and height ≥ 300 pixels
  • Aspect ratio: 1:2.5 ~ 2.5:1
  • Max size: 10 MB
Output duration rule
  • Input ≤ 15s → output duration = input duration.
  • Input > 15s → input is truncated to the first 15s; output ≤ 15s.
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) — edited MP4 (H.264) URL, valid 24 hours
  • output.orig_prompt (string)
  • output.submit_time / output.scheduled_time / output.end_time (string)
  • usage.duration (float) — billable duration in seconds
  • usage.input_video_duration (float)
  • usage.output_video_duration (float)
  • usage.SR (integer) — output resolution tier
  • usage.video_count (integer) — fixed 1
Show full SKILL.md (236 more words)Show less

Quick start (Python + HTTP)

python
import os
import time
import requests

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


def create_videoedit_task(req: dict) -> str:
    """Create a video-edit task and return task_id."""
    media = [{"type": "video", "url": req["video_url"]}]
    for url in req.get("reference_images", []):
        media.append({"type": "reference_image", "url": url})
    if len(media) - 1 > 5:
        raise ValueError("At most 5 reference images")

    payload = {
        "model": "happyhorse-1.0-video-edit",
        "input": {"prompt": req["prompt"], "media": media},
        "parameters": {
            "resolution": req.get("resolution", "1080P"),
            "watermark": req.get("watermark", True),
        },
    }
    if req.get("audio_setting"):
        payload["parameters"]["audio_setting"] = req["audio_setting"]
    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

python
# Style transfer — instruction only, no reference image
task_id = create_videoedit_task({
    "video_url": "https://example.com/input.mp4",
    "prompt": "将整个画面转换为水墨画风格",
    "resolution": "720P",
})

# Local replacement with a reference image, keep original audio
task_id = create_videoedit_task({
    "video_url": "https://example.com/character.mp4",
    "reference_images": ["https://example.com/striped-sweater.webp"],
    "prompt": "让视频中的马头人身角色穿上图片中的条纹毛衣",
    "audio_setting": "origin",
})

Error handling

ErrorLikely causeAction
401 / InvalidApiKeyMissing or invalid DASHSCOPE_API_KEYCheck env var or credentials file
400 InvalidParameterBad resolution, >5 reference images, video out of duration / size limitsValidate parameters and media
current user api does not support synchronous callsMissing X-DashScope-Async: enable headerAdd the required header
task_status: UNKNOWNtask_id older than 24 hoursRe-create the task
429RPS or quota exceededRetry with backoff; query RPS default 20

Output location

  • Default output: output/aliyun-happyhorse-videoedit/videos/
  • Override base dir with OUTPUT_DIR.

Anti-patterns

  • Do not use any model ID other than happyhorse-1.0-video-edit.
  • Do not call this API synchronously — async header is required.
  • Do not pass more than 1 video, more than 5 reference images, or any first_frame / last_frame / driving_audio.
  • Do not pass ratio or duration — output ratio follows the input video and duration follows the truncation rule.
  • Video URLs expire after 24 hours; download and persist immediately.
  • Do not use this skill for generation from scratch — use aliyun-happyhorse-t2v, aliyun-happyhorse-i2v, or aliyun-happyhorse-r2v instead.

Workflow

  1. Confirm intent: style transfer vs. instruction edit, and whether reference images are needed.
  2. Validate the input video meets format / duration / resolution / fps / size limits.
  3. Build the media array with exactly 1 video plus 0-5 reference_image entries.
  4. Create async task and poll /tasks/{task_id} every ~15s; download output.video_url before 24-hour expiration.

References

  • See references/api_reference.md for full HTTP API details.
  • See references/sources.md for source links.

© cinience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/ai/video/aliyun-happyhorse-videoedit of cinience/alicloud-skills.

  • SKILL.md
  • references/api_reference.md
  • references/sources.md
  • scripts/edit_happyhorse.py

Open the folder on GitHubat commit 1818263

Compare with similar skills

Aliyun Happyhorse Videoedit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Aliyun Happyhorse Videoedit compared with similar skills
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Video Shotseternityspring/reelbench-skills8781 repos~1.8kAutomated safety check: NotesApache-2.0
LTX-2.3 Video Generationdigitalsamba/claude-code-video-toolkit2.2k1 repos~2.4kAutomated safety check: NotesMIT
Ergo Remotion Videoitwanger/toBeBetterJavaer18k—~1.1kAutomated safety check: PassNone

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Works with

Categories

Questions about Aliyun Happyhorse Videoedit

What does Aliyun Happyhorse Videoedit do?

A skill your agent uses when editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit). Aliyun Happyhorse Videoedit is an agent skill from cinience/alicloud-skills.0-video-edit).

When should I use Aliyun Happyhorse Videoedit?

Aliyun Happyhorse Videoedit fits situations like: editing videos with DashScope HappyHorse 1.0 video editing model (happyhorse-1.0-video-edit); implementing instruction-based video editing such as style transfer; local replacement; optionally guided by 0-5 reference images.

How do I install Aliyun Happyhorse Videoedit in Claude Code?

Run `npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-videoedit -a claude-code`. Or copy the skill folder (skills/ai/video/aliyun-happyhorse-videoedit in cinience/alicloud-skills) into .claude/skills/aliyun-happyhorse-videoedit in your project. Claude Code loads it when a task matches its description.

How do I install Aliyun Happyhorse Videoedit in Codex?

Run `npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-videoedit -a codex`. Or copy the skill folder (skills/ai/video/aliyun-happyhorse-videoedit in cinience/alicloud-skills) into .agents/skills/aliyun-happyhorse-videoedit in your project. Codex loads it when a task matches its description.

Can I use Aliyun Happyhorse Videoedit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-videoedit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aliyun-happyhorse-videoedit, .gemini/skills/aliyun-happyhorse-videoedit, .github/skills/aliyun-happyhorse-videoedit and .opencode/skills/aliyun-happyhorse-videoedit in your project.

What does Aliyun Happyhorse Videoedit need to run?

Going by SKILL.md and its folder, Aliyun Happyhorse Videoedit needs Python for the scripts in its folder, the command-line tools its instructions call (python and python3) and credentials named DASHSCOPE_API_KEY and API_KEY. Our summary lists: Python 3; A credential in DASHSCOPE_API_KEY; A credential in API_KEY.

Does Aliyun Happyhorse Videoedit access the network?

SKILL.md names 1 domain. In commands or code: dashscope.aliyuncs.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Aliyun Happyhorse Videoedit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Aliyun Happyhorse Videoedit use?

Aliyun Happyhorse Videoedit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aliyun Happyhorse Videoedit use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Aliyun Happyhorse Videoedit?

Skills that share tags, products or a category with Aliyun Happyhorse Videoedit: HyperFrames Video Entry Point (heygen-com/hyperframes, 60k stars), Lanshu Create AI Presenter Video (cclank/lanshu-create-ai-presenter-video, 2.6k stars), Video Shots (eternityspring/reelbench-skills, 878 stars) and LTX-2.3 Video Generation (digitalsamba/claude-code-video-toolkit, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aliyun Happyhorse Videoedit?

cinience (a GitHub user) maintains it in cinience/alicloud-skills, which has 397 GitHub stars. The repository holds 96 skills in this directory. The repository was last updated on August 11, 2026.

Source: cinience/alicloud-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.