Agent skill

Aliyun Happyhorse T2v

A skill your agent uses when generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v).

MITAuto-check passedMedia & Creative

Install Aliyun Happyhorse T2v

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a claude-code

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

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v --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-t2v .claude/skills/aliyun-happyhorse-t2v && 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/

Paste into Claude Code

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .claude/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-t2v .agents/skills/aliyun-happyhorse-t2v && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .agents/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-t2v .cursor/skills/aliyun-happyhorse-t2v && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .cursor/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/cinience/alicloud-skills.git --path skills/ai/video/aliyun-happyhorse-t2v

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-t2v .gemini/skills/aliyun-happyhorse-t2v && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .gemini/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-t2v .github/skills/aliyun-happyhorse-t2v && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .github/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-happyhorse-t2v --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai/video/aliyun-happyhorse-t2v .opencode/skills/aliyun-happyhorse-t2v && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "aliyun-happyhorse-t2v" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-happyhorse-t2v into .opencode/skills/aliyun-happyhorse-t2v/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-happyhorse-t2v", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: aliyun-happyhorse-t2v
GitHub stars: 397
Token cost: ~1.6k tokens
SKILL.md length: 430 words
Files: 4 (incl. scripts, references)
Skills in repo: 96
Repo updated: Aug 11, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v).

Works in 4 steps: Confirm pure text-to-video intent (no… → Build the prompt and choose resolution,… → Create async task and poll… → …
Generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v)
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 T2v is an agent skill from cinience/alicloud-skills. Use when generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v). Use when implementing pure text-to-video synthesis via the video-synthesis async API on Alibaba Cloud Model Studio.

Its SKILL.md is about 1.6k 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/t2v_happyhorse.py`).

It sits in Media & Creative, covering 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

Generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v)
Implementing pure text-to-video synthesis via the video-synthesis async API on Alibaba Cloud Model Studio

Example prompts

“/aliyun-happyhorse-t2v”

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.

Confirm pure text-to-video intent (no input image/video).
Build the prompt and choose resolution, ratio, duration.
Create async task and poll /tasks/{task_id} every ~15s.
Download output.video_url before the 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 T2v loads about 1.6k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 430 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~64
When it runs · the whole SKILL.md, loaded when a task matches: ~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.7k

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). 430 words, ~1,641 tokens.

Download SKILL.mdSave it as .claude/skills/aliyun-happyhorse-t2v/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: aliyun-happyhorse-t2v
description: Use when generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v). Use when implementing pure text-to-video synthesis via the video-synthesis async API on Alibaba Cloud Model Studio.

HappyHorse 1.0 Text-to-Video

Validation

bash

mkdir -p output/aliyun-happyhorse-t2v
python -m py_compile skills/ai/video/aliyun-happyhorse-t2v/scripts/t2v_happyhorse.py && echo "py_compile_ok" > output/aliyun-happyhorse-t2v/validate.txt

Pass criteria: command exits 0 and output/aliyun-happyhorse-t2v/validate.txt is generated.

Output And Evidence

Save task IDs, polling responses, and final video URLs to output/aliyun-happyhorse-t2v/.
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-t2v — text-to-video, supports resolution, ratio, duration, watermark and seed control

Capabilities

Capability	Description	Required input
Text-to-video	Generate a physically realistic video from a text prompt only	`prompt`

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-t2v
input.prompt (string, required) — up to 5000 non-CJK characters or 2500 CJK characters; longer is truncated
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) — add bottom-right "Happy Horse" watermark (default: true)
parameters.seed (integer, optional) — range [0, 2147483647]

Response (task creation)

output.task_id (string) — use for polling, 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.SR (integer) — output resolution tier
usage.ratio (string)
usage.video_count (integer) — fixed 1

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_t2v_task(req: dict) -> str:
    """Create a text-to-video task and return task_id."""
    payload = {
        "model": "happyhorse-1.0-t2v",
        "input": {"prompt": req["prompt"]},
        "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

python

# Minimal — pure text prompt
task_id = create_t2v_task({
    "prompt": "一座由硬纸板和瓶盖搭建的微型城市，在夜晚焕发出生机。",
    "duration": 5,
})

# Vertical short video at 720P
task_id = create_t2v_task({
    "prompt": "A neon cyberpunk alley at midnight, rain reflections, slow dolly forward.",
    "resolution": "720P",
    "ratio": "9:16",
    "duration": 8,
})

Show full SKILL.md (187 more words)Show less

Error handling

Error	Likely cause	Action
401 / `InvalidApiKey`	Missing or invalid `DASHSCOPE_API_KEY`	Check env var or credentials file
400 `InvalidParameter`	Unsupported resolution/ratio, duration out of [3,15], wrong model name	Validate parameters
`current user api does not support synchronous calls`	Missing `X-DashScope-Async: enable` header	Add the required header
`task_status: UNKNOWN`	task_id older than 24 hours	Re-create the task
429	RPS or quota exceeded	Retry with backoff; query RPS default 20

Output location

Default output: output/aliyun-happyhorse-t2v/videos/
Override base dir with OUTPUT_DIR.

Anti-patterns

Do not use any model ID other than happyhorse-1.0-t2v.
Do not call this API synchronously — async header is required.
Do not pass media, first_frame, or reference_image — t2v takes only a text prompt.
Video and task URLs expire after 24 hours; download and persist immediately.
Do not use this skill for image- or video-conditioned generation — use aliyun-happyhorse-i2v, aliyun-happyhorse-r2v, or aliyun-happyhorse-videoedit instead.

Workflow

Confirm pure text-to-video intent (no input image/video).
Build the prompt and choose resolution, ratio, duration.
Create async task and poll /tasks/{task_id} every ~15s.
Download output.video_url before the 24-hour expiration.

References

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

Files

SKILL.md and 3 other files (scripts, references) in skills/ai/video/aliyun-happyhorse-t2v of cinience/alicloud-skills.

SKILL.md
references/api_reference.md
references/sources.md
scripts/t2v_happyhorse.py

Open the folder on GitHubat commit 1818263

Compare with similar skills

Aliyun Happyhorse T2v 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 T2v compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Aliyun Happyhorse T2v this skillcinience/alicloud-skills	397	—	~1.6k	Automated safety check: Pass	MIT	2 mo ago
Bailian Media Generationmodelstudioai/cli	542	—	~2k	Automated safety check: Pass	Apache-2.0	2 days ago
Video Generationbytedance/deer-flow	84k	3 repos	~1.4k	Automated safety check: Pass	MIT	today
Video Cover Imageitwanger/toBeBetterJavaer	18k	—	~3.3k	Automated safety check: Pass	None	yesterday
Seedancesongguoxs/seedance-prompt-skill	2.9k	1 repos	~2.5k	Automated safety check: Pass	None	8 mo ago
HyperFrames Video Entry Pointheygen-com/hyperframes	60k	3 repos	~5.2k	Automated safety check: Pass	Apache-2.0	today

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Works with

Alibaba Cloud

Questions about Aliyun Happyhorse T2v

What does Aliyun Happyhorse T2v do?

A skill your agent uses when generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v). Aliyun Happyhorse T2v is an agent skill from cinience/alicloud-skills.0-t2v).

When should I use Aliyun Happyhorse T2v?

Aliyun Happyhorse T2v fits situations like: generating videos from text prompts with DashScope HappyHorse 1.0 text-to-video model (happyhorse-1.0-t2v); implementing pure text-to-video synthesis via the video-synthesis async API on Alibaba Cloud Model Studio.

How do I install Aliyun Happyhorse T2v in Claude Code?

Run `npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a claude-code`. Or copy the skill folder (skills/ai/video/aliyun-happyhorse-t2v in cinience/alicloud-skills) into .claude/skills/aliyun-happyhorse-t2v in your project. Claude Code loads it when a task matches its description.

How do I install Aliyun Happyhorse T2v in Codex?

Run `npx skills add cinience/alicloud-skills --skill aliyun-happyhorse-t2v -a codex`. Or copy the skill folder (skills/ai/video/aliyun-happyhorse-t2v in cinience/alicloud-skills) into .agents/skills/aliyun-happyhorse-t2v in your project. Codex loads it when a task matches its description.

Can I use Aliyun Happyhorse T2v 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-t2v -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-t2v, .gemini/skills/aliyun-happyhorse-t2v, .github/skills/aliyun-happyhorse-t2v and .opencode/skills/aliyun-happyhorse-t2v in your project.

What does Aliyun Happyhorse T2v need to run?

Going by SKILL.md and its folder, Aliyun Happyhorse T2v 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 T2v 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 T2v 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 T2v use?

Aliyun Happyhorse T2v 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 T2v use?

About 1.6k tokens (SKILL.md is roughly 6.6k 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 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Aliyun Happyhorse T2v?

Skills that share tags, products or a category with Aliyun Happyhorse T2v: Bailian Media Generation (modelstudioai/cli, 542 stars), Video Generation (bytedance/deer-flow, 84k stars), Video Cover Image (itwanger/toBeBetterJavaer, 18k stars) and Seedance (songguoxs/seedance-prompt-skill, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aliyun Happyhorse T2v?

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.

Install Aliyun Happyhorse T2v

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

HappyHorse 1.0 Text-to-Video

Validation

Output And Evidence

Prerequisites

Critical model names

Capabilities

API endpoint (async only)

Normalized interface

Request

Response (task creation)

Response (task result, on SUCCEEDED)

Quick start (Python + HTTP)

Usage examples

Error handling

Output location

Anti-patterns

Workflow

References

Files

Compare with similar skills

Similar skills

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Related

Questions about Aliyun Happyhorse T2v