Seedance2 Api

"""
Seedance 分镜 Skill 通用 API 脚本

供 Cursor Agent 在 MCP 不可用时通过 Shell 调用。
API Key 通过环境变量 XSKILL_API_KEY 读取。

用法:
  python seedance_api.py submit  --model MODEL_ID --params '{"prompt":"..."}' 
  python seedance_api.py query   --task-id TASK_ID
  python seedance_api.py poll    --task-id TASK_ID [--interval 5] [--timeout 600]
  python seedance_api.py balance
"""

import argparse
import json
import os
import sys
import time

import requests

BASE_URL = os.environ.get("XSKILL_BASE_URL", "https://api.xskill.ai")
API_KEY = os.environ.get("XSKILL_API_KEY", "")

CREATE_URL = f"{BASE_URL}/api/v3/tasks/create"
QUERY_URL = f"{BASE_URL}/api/v3/tasks/query"
BALANCE_URL = f"{BASE_URL}/api/v3/user/balance"
UPLOAD_URL = f"{BASE_URL}/api/v3/upload/image"


def _headers():
    if not API_KEY:
        print("错误: 环境变量 XSKILL_API_KEY 未设置", file=sys.stderr)
        print("  export XSKILL_API_KEY=sk-your-api-key", file=sys.stderr)
        print("  获取 API Key: https://www.xskill.ai/#/v2/api-keys", file=sys.stderr)
        sys.exit(1)
    return {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {API_KEY}",
    }


# ------------------------------------------------------------------
# submit: 创建任务
# ------------------------------------------------------------------
def cmd_submit(args):
    params = json.loads(args.params)
    payload = {"model": args.model, "params": params}
    resp = requests.post(CREATE_URL, json=payload, headers=_headers(), timeout=30)
    result = resp.json()

    if result.get("code") != 200:
        print(json.dumps(result, ensure_ascii=False, indent=2))
        sys.exit(1)

    task_id = result["data"]["task_id"]
    price = result["data"].get("price", "?")
    print(json.dumps({
        "task_id": task_id,
        "price": price,
        "status": "submitted",
        "raw": result,
    }, ensure_ascii=False, indent=2))


# ------------------------------------------------------------------
# query: 查询任务状态
# ------------------------------------------------------------------
def cmd_query(args):
    resp = requests.post(QUERY_URL, json={"task_id": args.task_id}, headers=_headers(), timeout=30)
    result = resp.json()
    data = result.get("data", {})
    status = data.get("status", "unknown")

    output_urls = []
    if status == "completed":
        output = data.get("result", {}).get("output", {})
        output_urls = output.get("images", []) or output.get("videos", []) or []

    print(json.dumps({
        "task_id": args.task_id,
        "status": status,
        "output_urls": output_urls,
        "error": data.get("error", ""),
        "raw": result,
    }, ensure_ascii=False, indent=2))


# ------------------------------------------------------------------
# poll: 轮询等待任务完成
# ------------------------------------------------------------------
def cmd_poll(args):
    interval = args.interval
    timeout = args.timeout
    elapsed = 0

    print(f"[轮询] task_id={args.task_id}  间隔={interval}s  超时={timeout}s", file=sys.stderr)

    while elapsed < timeout:
        resp = requests.post(QUERY_URL, json={"task_id": args.task_id}, headers=_headers(), timeout=30)
        result = resp.json()
        data = result.get("data", {})
        status = data.get("status", "unknown")
        print(f"[轮询] {elapsed}s - {status}", file=sys.stderr)

        if status == "completed":
            output = data.get("result", {}).get("output", {})
            output_urls = output.get("images", []) or output.get("videos", []) or []
            print(json.dumps({
                "task_id": args.task_id,
                "status": "completed",
                "output_urls": output_urls,
                "raw": result,
            }, ensure_ascii=False, indent=2))
            return

        if status == "failed":
            print(json.dumps({
                "task_id": args.task_id,
                "status": "failed",
                "error": data.get("error", "未知错误"),
                "raw": result,
            }, ensure_ascii=False, indent=2))
            sys.exit(1)

        time.sleep(interval)
        elapsed += interval

    print(json.dumps({
        "task_id": args.task_id,
        "status": "timeout",
        "error": f"等待 {timeout}s 后仍未完成",
    }, ensure_ascii=False, indent=2))
    sys.exit(1)


# ------------------------------------------------------------------
# balance: 查询余额
# ------------------------------------------------------------------
def cmd_balance(args):
    resp = requests.get(BALANCE_URL, headers=_headers(), timeout=30)
    result = resp.json()
    print(json.dumps(result, ensure_ascii=False, indent=2))


# ------------------------------------------------------------------
# upload: 上传图片
# ------------------------------------------------------------------
def cmd_upload(args):
    payload = {}
    if args.image_url:
        payload["image_url"] = args.image_url
    elif args.image_path:
        import base64
        with open(args.image_path, "rb") as f:
            payload["image_data"] = base64.b64encode(f.read()).decode()
    else:
        print("错误: 需要 --image-url 或 --image-path", file=sys.stderr)
        sys.exit(1)

    resp = requests.post(UPLOAD_URL, json=payload, headers=_headers(), timeout=60)
    result = resp.json()
    print(json.dumps(result, ensure_ascii=False, indent=2))


# ------------------------------------------------------------------
# CLI 入口
# ------------------------------------------------------------------
def main():
    parser = argparse.ArgumentParser(description="Seedance Storyboard API CLI")
    sub = parser.add_subparsers(dest="command")

    p_submit = sub.add_parser("submit", help="创建任务")
    p_submit.add_argument("--model", required=True, help="模型 ID")
    p_submit.add_argument("--params", required=True, help="参数 JSON 字符串")

    p_query = sub.add_parser("query", help="查询任务")
    p_query.add_argument("--task-id", required=True, help="任务 ID")

    p_poll = sub.add_parser("poll", help="轮询等待任务完成")
    p_poll.add_argument("--task-id", required=True, help="任务 ID")
    p_poll.add_argument("--interval", type=int, default=5, help="轮询间隔（秒）")
    p_poll.add_argument("--timeout", type=int, default=600, help="超时时间（秒）")

    sub.add_parser("balance", help="查询余额")

    p_upload = sub.add_parser("upload", help="上传图片")
    p_upload.add_argument("--image-url", help="网络图片 URL")
    p_upload.add_argument("--image-path", help="本地图片路径")

    args = parser.parse_args()

    commands = {
        "submit": cmd_submit,
        "query": cmd_query,
        "poll": cmd_poll,
        "balance": cmd_balance,
        "upload": cmd_upload,
    }

    if args.command not in commands:
        parser.print_help()
        sys.exit(1)

    commands[args.command](args)


if __name__ == "__main__":
    main()

{
  "schema_version": "2.0",
  "meta": {
    "generated_at": "2026-02-22T08:40:49.750Z",
    "slug": "hexiaochun-seedance2-api",
    "source_url": "https://github.com/hexiaochun/seedance2-api/tree/main/.cursor/skills/seedance2-api/",
    "source_ref": "main",
    "model": "claude",
    "analysis_version": "3.0.0",
    "source_type": "community",
    "content_hash": "03dd86116c8c736774e3790bda44acdc8ba2b6421b85f29fe8cb861a78fd40ae",
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  "skill": {
    "name": "seedance2-api",
    "description": "Out-of-the-box Seedance 2.0 API skill — just one API key to generate AI videos. Builds storyboards, generates reference images with Seedream 4.5, submits video tasks, and polls results. Supports both MCP and standalone Python script mode. Use when the user mentions seedance, AI video, storyboard, or video generation.",
    "summary": "Generate AI videos from storyboards using Seedance 2.0 API. Build storyboards, create reference images with Seedream 4.5, submit video tasks, and poll for results.",
    "icon": "📦",
    "version": "1.0.0",
    "author": "hexiaochun",
    "license": "MIT",
    "category": "data",
    "tags": [
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      "video-generation",
      "seedance",
      "storyboard",
      "media"
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    "supported_tools": [
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      "codex",
      "claude-code"
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    "risk_factors": [
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    "is_blocked": false,
    "safe_to_publish": true,
    "summary": "This is a legitimate AI video generation skill that accesses the xskill.ai API. Static findings show network calls and environment variable access, which are expected for API-based skills. The detected 'external_commands' are false positives from Chinese template text in markdown code blocks. No malicious behavior detected.",
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          {
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          {
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          {
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          {
            "file": "SKILL.md",
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        ]
      },
      {
        "factor": "network",
        "evidence": [
          {
            "file": "scripts/seedance_api.py",
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            "line_end": 49
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "SKILL.md",
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          {
            "file": "SKILL.md",
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          {
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          {
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          {
            "file": "SKILL.md",
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          {
            "file": "SKILL.md",
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          {
            "file": "SKILL.md",
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          },
          {
            "file": "SKILL.md",
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        ]
      },
      {
        "factor": "env_access",
        "evidence": [
          {
            "file": "scripts/seedance_api.py",
            "line_start": 22,
            "line_end": 22
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
            "file": "scripts/seedance_api.py",
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          {
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          {
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          {
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        ]
      }
    ],
    "critical_findings": [],
    "high_findings": [],
    "medium_findings": [
      {
        "title": "API Credential Access",
        "description": "Skill accesses XSKILL_API_KEY environment variable for authentication with external API. This is standard practice for API-based skills but warrants documentation.",
        "locations": [
          {
            "file": "scripts/seedance_api.py",
            "line_start": 22,
            "line_end": 23
          }
        ],
        "confidence": 0.9,
        "confidence_reasoning": "Direct evidence of environment variable access for API authentication. Standard and expected behavior for API client skills."
      },
      {
        "title": "Network API Calls",
        "description": "Skill makes HTTP requests to xskill.ai API endpoint for video and image generation. Expected behavior for AI video generation service.",
        "locations": [
          {
            "file": "scripts/seedance_api.py",
            "line_start": 49,
            "line_end": 49
          }
        ],
        "confidence": 0.9,
        "confidence_reasoning": "Direct evidence of HTTP POST requests to legitimate API endpoint (api.xskill.ai). This is the intended functionality of the skill."
      }
    ],
    "low_findings": [
      {
        "title": "False Positive: External Commands Detection",
        "description": "Static analyzer incorrectly flagged 141 instances of 'external_commands' due to Chinese template text using backticks in markdown code blocks (e.g., 【风格】). These are documentation templates, not shell commands.",
        "locations": [
          {
            "file": "reference.md",
            "line_start": 5,
            "line_end": 16
          }
        ],
        "confidence": 0.95,
        "confidence_reasoning": "Chinese characters in markdown code blocks use backtick syntax for formatting, not command execution. The patterns detected are template placeholders like 【风格】, not actual shell commands."
      },
      {
        "title": "False Positive: Weak Cryptographic Algorithm",
        "description": "Static analyzer incorrectly flagged 'weak cryptographic algorithm' in multiple locations. No cryptographic code exists in this skill.",
        "locations": [],
        "confidence": 0.95,
        "confidence_reasoning": "No cryptographic operations found in the codebase. The analyzer appears to have misidentified patterns in Chinese documentation text."
      },
      {
        "title": "False Positive: System Reconnaissance",
        "description": "Static analyzer incorrectly flagged system reconnaissance patterns. These are documentation references, not actual system scanning.",
        "locations": [],
        "confidence": 0.9,
        "confidence_reasoning": "No system reconnaissance code detected. The findings appear to be false positives from documentation text."
      }
    ],
    "dangerous_patterns": [],
    "files_scanned": 3,
    "total_lines": 1030,
    "audit_model": "claude",
    "audited_at": "2026-02-22T08:40:49.750Z",
    "risk_factors": [
      "network",
      "env_access",
      "scripts"
    ]
  },
  "content": {
    "user_title": "Generate AI Videos from Storyboards",
    "value_statement": "This skill enables Claude, Codex, and Claude Code users to generate professional AI videos by building storyboards, creating reference images, and submitting video generation tasks to Seedance 2.0 API. It streamlines the complex multi-step video creation process into a simple, automated workflow.",
    "seo_keywords": [
      "Claude Code AI video generation",
      "Seedance 2.0 skill for Claude",
      "AI video generation Claude",
      "storyboard to video Claude",
      "Claude Code video creation",
      "Seedream 4.5 image generation",
      "video generation API skill",
      "AI video workflow automation",
      "Claude Codex media skill",
      "text to video skill"
    ],
    "actual_capabilities": [
      "Build storyboards with structured templates for video prompts",
      "Generate reference images using Seedream 4.5 text-to-image and editing",
      "Submit video generation tasks to Seedance 2.0 API with flexible parameters",
      "Poll and retrieve video results with automatic status checking",
      "Support both MCP tool mode and standalone Python script mode",
      "Handle multi-asset videos with image, video, and audio references"
    ],
    "limitations": [
      "Requires valid XSKILL_API_KEY for API authentication",
      "Cannot generate realistic human faces in videos",
      "Maximum 12 files per task: up to 9 images, 3 videos, and 3 audio files",
      "Video generation takes approximately 10 minutes per task"
    ],
    "use_cases": [
      {
        "title": "Content Creator Video Production",
        "description": "Quickly generate marketing videos, social media content, or short films by building storyboards and letting AI handle the video generation.",
        "target_user": "Content creators, marketers, social media managers"
      },
      {
        "title": "Developer Prototyping",
        "description": "Integrate AI video generation into automated workflows or pipelines using the Python script mode for programmatic video creation.",
        "target_user": "Software developers building video automation tools"
      },
      {
        "title": "Creative Concept Visualization",
        "description": "Visualize creative concepts and story ideas by generating reference images and video previews before committing to full production.",
        "target_user": "Filmmakers, animators, creative directors"
      }
    ],
    "prompt_templates": [
      {
        "title": "Basic Video Request",
        "prompt": "Help me generate a video about [describe your video concept]. I want it to be [duration] seconds in [aspect ratio] format with a [style] style.",
        "scenario": "User wants a simple video without existing assets"
      },
      {
        "title": "Video with Reference Images",
        "prompt": "Generate a [duration]-second [style] video at [aspect ratio]. I have reference images for the character and background. Help me build a storyboard and submit the video task to Seedance.",
        "scenario": "User has reference images they want to use"
      },
      {
        "title": "First and Last Frame Video",
        "prompt": "Create a video that transitions smoothly from [describe first frame] to [describe last frame]. Use first_last_frames mode with a [duration] second duration at [aspect ratio].",
        "scenario": "User wants precise control over start and end frames"
      },
      {
        "title": "Complete Production Workflow",
        "prompt": "I want to create a complete video: First, generate reference images for [describe scenes]. Then build a storyboard with camera movements and transitions. Finally, submit the video task with all assets and poll for results.",
        "scenario": "User wants full end-to-end video production workflow"
      }
    ],
    "output_examples": [
      {
        "input": "User wants a 15-second sci-fi video of an astronaut on Mars",
        "output": "Storyboard created with cinematic sci-fi style, 16:9 aspect ratio. Reference images generated for astronaut and Mars landscape. Video task submitted successfully. Poll every 30 seconds, results expected in ~10 minutes."
      },
      {
        "input": "Generate an astronaut walking on Mars",
        "output": "Step 1: Generated astronaut + Mars scene images with Seedream 4.5\nStep 2: Submitted video task with image references\nStep 3: Video is generating (~10 minutes)\nStep 4: Video completed: https://example.com/video.mp4"
      }
    ],
    "best_practices": [
      "Provide detailed scene descriptions including lighting, camera movement, and mood for better video quality",
      "Use the auto-poll feature with appropriate timeouts to avoid missing results",
      "Generate reference images first before submitting video tasks to ensure quality assets"
    ],
    "anti_patterns": [
      "Avoid submitting vague prompts without storyboard structure - results will be poor",
      "Do not exceed file limits (9 images, 3 videos, 3 audio) as tasks will fail",
      "Avoid using realistic human face uploads - Seedance does not support this"
    ],
    "faq": [
      {
        "question": "How do I get an API key for Seedance?",
        "answer": "Get your API key from https://www.xskill.ai/#/v2/api-keys and set it as the XSKILL_API_KEY environment variable."
      },
      {
        "question": "What is the difference between Omni Reference and First Last Frames mode?",
        "answer": "Omni Reference uses multiple image/video/audio files as references throughout the video. First Last Frames mode specifically controls the exact starting and ending frames of the video."
      },
      {
        "question": "How long does video generation take?",
        "answer": "Video generation typically takes approximately 10 minutes. Use the poll command with a 600-second timeout to wait for results."
      },
      {
        "question": "Can I use my own images in the video?",
        "answer": "Yes, you can upload local images or provide URLs. Use the upload command or direct URL input when submitting tasks."
      },
      {
        "question": "What aspect ratios are supported?",
        "answer": "Supported ratios include 16:9, 9:16, 1:1, 21:9, 4:3, and 3:4."
      },
      {
        "question": "Does this skill work without MCP?",
        "answer": "Yes, the skill includes a standalone Python script (scripts/seedance_api.py) that can be run directly from the command line when MCP is unavailable."
      }
    ]
  },
  "file_structure": [
    {
      "name": "scripts",
      "type": "dir",
      "path": "scripts",
      "children": [
        {
          "name": "seedance_api.py",
          "type": "file",
          "path": "scripts/seedance_api.py",
          "lines": 210
        }
      ]
    },
    {
      "name": "reference.md",
      "type": "file",
      "path": "reference.md",
      "lines": 362
    },
    {
      "name": "SKILL.md",
      "type": "file",
      "path": "SKILL.md",
      "lines": 458
    }
  ]
}