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tupian-shipin-shengcheng图片食品盛城

Agent Skill

tupian-shipin-shengcheng 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,717

周安装

111

GitHub Stars

公开资料未说明

下载量

870
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tupian-shipin-shengcheng(图片食品盛城)
来源仓库:https://github.com/xuhongfeii2/tupian-shipin-shengcheng
安装命令:
openclaw skills install tupian-shipin-shengcheng
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install tupian-shipin-shengcheng

简介

可调用 banana、sora、veo 等模型生成图片视频,适合图片、视频与短剧素材生产。

SKILL.md

name
图片视频生成
description
可调用 banana、sora、veo 等模型生成图片视频,适合图片、视频与短剧素材生产。
metadata
{"openclaw":{"requires":{"anyBins":["python","python3"]}}}

Image And Video Generation Skill

Use this skill when OpenClaw should call this platform relay API for:

  • VEO video models
  • Banana image models
  • Sora video models

Read these references before using the scripts:

Environment

Preferred:

  • EASYCLAW_PLATFORM_API_TOKEN

Compatible fallback:

  • CHANJING_PLATFORM_API_TOKEN
  • EASYCLAW_PLATFORM_API_KEY
  • EASYCLAW_PLATFORM_API_SECRET
  • CHANJING_PLATFORM_API_KEY
  • CHANJING_PLATFORM_API_SECRET

Optional base URL override:

  • EASYCLAW_PLATFORM_BASE_URL
  • CHANJING_PLATFORM_BASE_URL

Important guidance:

  • Always tell the user to get the platform token from http://easyclaw.bar/shuziren/user/
  • The token is issued by this platform, not by the upstream provider
  • The base URL defaults to http://easyclaw.bar/shuzirenapi
  • Do not modify OpenClaw core code or openclaw.json to make this skill work

Important Rules

  • Only execute the provided Python scripts under scripts/
  • Do not hand-write HTTP requests
  • Do not probe unrelated platform routes such as /api/me, auth routes, or non-skill routes
  • If a needed capability is not exposed by an existing script, update a script first
  • Submit requests only through scripts/generate_video.py
  • Query an existing task only through scripts/fetch_video.py
  • scripts/generate_video.py must submit exactly one task for one user intent
  • After submission, the skill must create the watcher in the same run unless the user explicitly asks for --no-watch
  • The watcher must be implemented inside the skill scripts, not by modifying OpenClaw core behavior
  • The watcher checks task status every 30 seconds by default
  • Before creating the watcher, pass the current OpenClaw session binding to scripts/generate_video.py with --notify-session-key
  • --notify-session-key may be either the real OpenClaw session key or the current delivery target such as easyclaw:bot:11
  • Prefer the current session key from session_status; do not guess another session when the current one can be resolved
  • When the task reaches a terminal state, the watcher must return the result and delete its own cron job
  • If watcher creation fails, tell the user submission succeeded but automatic notification was not armed
  • Do not promise automatic notification unless a non-empty watcher job_id is returned
  • Do not claim there will be fallback polling or periodic checking unless that behavior is actually implemented and armed
  • Pass only the parameters the user specified
  • Prefer guided builder flags because they validate model-specific requirements and return clearer errors
  • Use raw JSON or raw multipart mode only when the user needs fields not exposed by the guided builder

Async Workflow

For VEO, Banana, and Sora generation:

  1. Run scripts/generate_video.py
  2. Let the script submit the request
  3. Pass the current session binding with --notify-session-key so the watcher can write the final result back to the originating session
  4. Let the script create the cron watcher in the same execution by default
  5. Return the submission result and watcher state
  6. The watcher will call scripts/cron_watch_task.py every 30 seconds
  7. If the task is still running, the watcher returns exactly NO_REPLY
  8. If the task succeeds or fails, the watcher writes the final result into the original session, returns NO_REPLY, and removes the cron job

Do not replace this with model-side polling or ad hoc direct requests.

Script Selection

  • Submit generation request: scripts/generate_video.py
  • Query one task by id: scripts/fetch_video.py

Submit Modes

scripts/generate_video.py supports:

  • Guided builder mode

- Use flags such as --prompt, --reference-file, --image-url, --style, --size, --aspect-ratio - This mode validates model-specific rules - Use --list-models or --describe-model <model> when guidance is needed

  • JSON request mode

- Use --payload-json or --payload-file - Use this when the upstream payload needs custom JSON fields

  • Multipart form-data mode

- Use repeated --field name=value - Use repeated --file-field name=path - Use this for local file uploads such as input_reference

Do not mix JSON payload args with multipart field args in one command.

Model Usage

VEO video models

Typical models:

  • veo_3_1-fast
  • veo_3_1-fast-fl
  • veo_3_fast
  • veo_3

Rules:

  • veo_3_1-fast supports text-to-video and reference-image video with up to 3 images
  • veo_3_1-fast-fl requires 1 or 2 images
  • If the user selects veo_3_1-fast-fl and does not provide the required images, stop and tell them exactly what is missing
  • VEO JSON mode does not accept HTTP image URLs for input_reference; use local files or Base64 JSON in raw mode

Banana image models

Standard Banana:

  • nano_banana_2
  • nano_banana_pro
  • nano_banana_pro-1K
  • nano_banana_pro-2K
  • nano_banana_pro-4K

Gemini Banana:

  • nano_banana_2-landscape
  • nano_banana_2-portrait
  • nano_banana_pro-1K-landscape
  • nano_banana_pro-1K-portrait
  • nano_banana_pro-2K-landscape
  • nano_banana_pro-2K-portrait
  • nano_banana_pro-4K-landscape
  • nano_banana_pro-4K-portrait

Rules:

  • Standard Banana uses prompt plus optional metadata
  • Gemini Banana uses contents plus generationConfig
  • Guided builder mode supports both families and performs basic validation

Sora video models

Typical models:

  • sora-2-landscape-10s
  • sora-2-portrait-10s
  • sora-2-landscape-15s
  • sora-2-portrait-15s
  • sora-2-pro-landscape-25s
  • sora-2-pro-portrait-25s
  • sora-2-pro-landscape-hd-15s
  • sora-2-pro-portrait-hd-15s

Rules:

  • Prompt is required
  • image_url and local input_reference are mutually exclusive
  • Use raw JSON or raw multipart when extra provider-specific fields are required

Examples

Print the built-in model guide:

python "{baseDir}/scripts/generate_video.py" --list-models

Describe one model:

python "{baseDir}/scripts/generate_video.py" --describe-model "veo_3_1-fast-fl"

Submit VEO with JSON:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --payload-json "{\"model\":\"veo_3_1-fast\",\"prompt\":\"a cat running in snow\",\"size\":\"1920x1080\"}"

Submit VEO with local reference files:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --prompt "animate this portrait" --reference-file "C:\path\
eference1.jpg" --reference-file "C:\path\
eference2.jpg"

Submit VEO start/end frame mode:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast-fl" --prompt "animate between these two frames" --start-frame-file "C:\path\start.jpg" --end-frame-file "C:\path\end.jpg"

Submit Banana standard:

python "{baseDir}/scripts/generate_video.py" --model "nano_banana_pro" --prompt "a coffee cup on a wooden desk" --aspect-ratio "1:1" --reference-url "https://example.com/ref.png"

Submit Gemini Banana:

python "{baseDir}/scripts/generate_video.py" --model "nano_banana_2-landscape" --prompt "a futuristic city skyline"

Submit Sora with image URL:

python "{baseDir}/scripts/generate_video.py" --model "sora-2-landscape-10s" --payload-json "{\"model\":\"sora-2-landscape-10s\",\"prompt\":\"turn this into a cinematic teaser\",\"image_url\":\"https://example.com/source.png\",\"style\":\"cinematic\"}"

Submit only without watcher:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --payload-file "C:\path\payload.json" --no-watch

Query an existing task:

python "{baseDir}/scripts/fetch_video.py" --task-id "task_123"

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.89%
按下载量换算643

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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