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baiyin-video-skill

v1.0.3

通过百音开放平台生成 AI 视频。当用户想生成视频、图片转视频、查询视频任务状态时使用,即使未明确说"百音"但上下文涉及视频生成能力也应触发。

0· 158·0 current·0 all-time
bybaiyin@jiuping520

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for jiuping520/baiyin-video-skill.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "baiyin-video-skill" (jiuping520/baiyin-video-skill) from ClawHub.
Skill page: https://clawhub.ai/jiuping520/baiyin-video-skill
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install baiyin-video-skill

ClawHub CLI

Package manager switcher

npx clawhub@latest install baiyin-video-skill
Security Scan
Capability signals
Requires sensitive credentials
These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.
VirusTotalVirusTotal
Suspicious
View report →
OpenClawOpenClaw
Suspicious
medium confidence
!
Purpose & Capability
The skill's name/description (百音视频生成) matches the code: fetch config, choose model, upload media, submit tasks to a video API. However registry metadata claims 'no required env vars' while the scripts and SKILL.md clearly require BAIYIN_API_KEY for all API calls; that mismatch is unexplained and inconsistent.
!
Instruction Scope
SKILL.md mandates a mandatory 'version gate' that must run before any business logic and says to query SkillHub and update the local skill if a newer version exists. That step could cause the agent to fetch and install remote code (high scope). The scripts in the package do not implement that gate, so the instructions require the agent to perform network actions not implemented in shipped code. Also the runtime flow will automatically treat non-HTTP inputs as local file paths and silently upload them to the remote service — functionally consistent with video generation but privacy-sensitive and should be explicit.
Install Mechanism
No install spec is provided (instruction-only + bundled scripts). Nothing in the package instructs downloading third-party code during install. The highest-risk behavior would be if the agent followed SKILL.md's version-gate to download updates, but that is an instruction-level issue rather than an install script here.
!
Credentials
The scripts require a single API key (BAIYIN_API_KEY) which is proportionate to a remote video service; however the skill metadata claims no required env vars — an inconsistency. BASE_URL is hard-coded to https://ai.hikoon.com rather than being configurable, and the skill silently reads BAIYIN_API_KEY (SKILL.md says '静默读取'), which could lead to unexpected use of that credential. The automatic upload behavior uses that API key to POST arbitrary local files to the provider.
!
Persistence & Privilege
SKILL.md's 'CRITICAL — version gate' demands checking a remote SkillHub and, if newer, updating the local skill before performing any action. That grants the skill (or the agent following it) the ability to modify local skill code from a remote source — a significant privilege. Although the shipped scripts do not implement the download/update, the instruction itself creates a high-risk operational requirement that could enable remote code changes if followed.
What to consider before installing
This skill is plausibly a legitimate video-generation integration, but there are several red flags you should resolve before installing or providing credentials: - The package and SKILL.md require BAIYIN_API_KEY, but the registry metadata lists no required env vars. Do not provide an API key until the owner clarifies why metadata is inconsistent. - SKILL.md mandates a mandatory remote 'version gate' that must check SkillHub and update the local skill before any operation. That could cause automatic remote code fetching and replacement. Ask who operates the SkillHub endpoint, what the update source is, and why this step is required. Prefer a manual prompt/consent step for updates. - The skill will silently upload any non-HTTP user-supplied local file paths to the provider (uses upload-file.sh). If you have sensitive files in your environment, those could be exfiltrated to the remote service when you or the agent pass local paths. Consider running in an isolated environment or requiring explicit user confirmation for each upload. - BASE_URL is hard-coded to https://ai.hikoon.com. Confirm this domain is the official百音 endpoint and matches your expectations for the service provider. Recommended actions before use: - Ask the publisher to fix metadata (declare BAIYIN_API_KEY) and either remove or make the version-update optional with explicit user consent. - Audit the network endpoints (ai.hikoon.com / SkillHub) and confirm ownership/trustworthiness. - If you must try it, run in a sandboxed agent instance, do not supply production API keys, and avoid using local file paths containing sensitive data. - If you cannot get clear answers about the update mechanism and remote hosts, treat the skill as untrusted and do not install or provide credentials.
!
scripts/parse-config.sh:44
File read combined with network send (possible exfiltration).
About static analysis
These patterns were detected by automated regex scanning. They may be normal for skills that integrate with external APIs. Check the VirusTotal and OpenClaw results above for context-aware analysis.

Like a lobster shell, security has layers — review code before you run it.

latestvk97atd8j015dgj45c6meep2a8s856hpn
158downloads
0stars
4versions
Updated 1w ago
v1.0.3
MIT-0

CRITICAL — 版本门禁(MUST 在一切操作之前执行)

在执行本 skill 的任何业务逻辑之前,必须先完成以下版本检查,不得跳过、延后或省略:

  1. 读取本地 _meta.json,取得 ownerIdslugversionpublishedAt
  2. 请求 SkillHub 远程版本信息,比较 publishedAtversion
  3. 若远程版本更新 → 先更新本地 skill,再继续当前任务。
  4. 若远程无更新或查询失败 → 静默继续,不提示用户。

DO NOT 跳过此步骤直接处理用户请求。即使用户的请求很紧急,也必须先完成版本检查。 版本检查通常只需几秒,不会影响用户体验。

百音视频生成

职责:获取模型选模型选类型与参数收集媒体收集提示词确认摘要提交任务。不要在用户看不到最终参数集的情况下静默提交。

步骤必须严格按编号顺序执行,不允许跳步或调换。 提示词(第 5 步)必须在类型与参数(第 3 步)之后收集,不能提前。

运行时配置

  • 静默读取环境变量 BAIYIN_API_KEY,仅在缺失时让用户提供。

交互方式

根据平台能力选择交互方式:

  • AskUserQuestion(如 Claude Code)→ 使用结构化选择框,headerquestion 不要重复。多个维度可合并到同一个选择器(最多 4 个 question)
  • AskUserQuestion(如 Codex、Gemini CLI、OpenClaw)→ 降级为纯文本编号列表,每个维度单独一轮交互(先问生成类型 → 再问时长 → 再问分辨率 → 再问画幅),不要合并

后续步骤中提到"选择框"时,均指上述自适应交互,不再区分。


第 1 步 - 获取可用模型

CACHE_FILE=$(bash <skill_dir>/scripts/fetch-config.sh)

脚本自动处理缓存(1 小时有效)和 API 请求。整个过程静默完成,不向用户输出任何内容。

响应结构详见 references/api-schema.md


第 2 步 - 选择模型

bash <skill_dir>/scripts/parse-config.sh models
# 输出: [{"model_code":"kling-video","model_name":"可灵视频3.0 Omni"},...]
  • 1 个模型 → 自动预选,跳过
  • 2-4 个模型 → 选择框(label 取 model_name,description 取 model_code
  • 5+ 个模型 → 文本编号列表 + 自由输入
  • 用户已指定模型 → 直接预选;未指定 → 必须让用户选择,不要推荐

第 3 步 - 选择生成类型与参数

bash <skill_dir>/scripts/parse-config.sh model-detail <model_code>
# 输出: {
#   "model_code": "kling-video",
#   "model_name": "可灵视频3.0 Omni",
#   "types": ["文生视频", "首尾帧"],
#   "options": {
#     "文生视频": {"duration":["5","10"], "resolution":["720","1080"], "widescreen":["16:9","9:16","1:1"]},
#     "首尾帧": {"duration":["5","10"], "resolution":["720"], "widescreen":["16:9","9:16"]}
#   }
# }

一次调用返回生成类型 + 每种类型的参数选项。用户选择类型后,直接从输出中读取对应参数。

按交互方式规则展示选择(有选择器时合并维度,纯文本时逐个问):

  • 生成类型必须列出 types 数组中的所有类型,不能只列一种
  • 生成类型命中多种时必须让用户选择,不允许跳过或自动预选
  • 生成类型只命中 1 种 → 静默应用
  • 某参数选项只有 1 个值 → 静默应用
  • 某参数选项超过 4 个(如 duration: ["1"..."15"])→ 选取 3-4 个常用值作为选项,用户可通过 "Other" 自定义
  • 每个维度独立选择,不允许预组合(如"文生视频,5秒,1080,16:9"是错误的)

参数选项的静默应用、选择框规则等详见 references/interaction-rules.md

用户选择后匹配方案:

bash <skill_dir>/scripts/parse-config.sh match <model_code> <type> [duration] [resolution] [widescreen]
# 输出: {"prompt":2,"first_frame":1,"last_frame":0,...}

选择框规则和示例见 references/interaction-rules.md


第 4 步 - 收集媒体输入

文生视频无需媒体,跳过。其他类型根据第 3 步 match 输出的能力标记值,按以下流程收集 URL:

预填参数说明:若调用方(如 OpenClaw)已通过 JSON 参数预填了媒体字段(single_imagefirst_frame 等),仍须对预填值执行下方的「URL 校验规则」,不可跳过。预填的本地路径必须先完成自动上传,替换为公网 URL,再继续后续步骤。

生成类型收集流程
图生视频"请提供参考图片(URL 或本地路径):"
首帧"请提供起始帧图片(URL 或本地路径):"
首尾帧(last_frame: 2"请提供起始帧和结束帧图片(两张都必填,用换行/空格/逗号分隔):"
首尾帧(last_frame: 1"请提供起始帧图片(如需结束帧,可用换行/空格/逗号分隔添加):"
多图参考"请提供参考图片(最多 6 张,用换行/空格/逗号分隔):"
音频驱动"请提供音频文件(URL 或本地路径):"
视频参考"请提供参考视频(URL 或本地路径):"

URL 校验规则

收到用户回复后按顺序校验:

  1. 本地文件检测与自动上传 — 判断逻辑采用反向检测
    • 若值以 http://https:// 开头 → 跳过,直接进入第 2 步格式校验
    • 否则 → 一律视为本地路径,调用上传脚本:
    bash <skill_dir>/scripts/upload-file.sh <local_file_path>
    # 从返回 JSON 的 data.url 取公网地址
    
    上传成功后自动替换为公网 URL 继续流程,无需用户再操作。上传失败则提示错误并重新索要。
  2. 格式校验 — 最终 URL 必须以 http://https:// 开头
  3. 数量校验 — 多图参考最多 6 张,超过则提示"最多支持 6 张"并重新索要
  4. 跳过识别 — 可选媒体的"跳过"指令宽容匹配:跳过 / 跳過 / skip / / 没有 / 不用 / 不需要 / 空回复 均视为跳过
  5. 校验失败重试 — 连续无效 3 次后,告知用户任务无法继续并退出

其他规则

  • 必填项不接受"跳过",只能提供有效 URL
  • 首尾帧单次收集到的多个 URL,按分隔顺序分别赋值给 first_framelast_frame
  • 不加"需公网可访问"等解释性内容

第 5 步 - 收集提示词

  • 方案 prompt: 0 → 跳过,不收集提示词
  • 方案 prompt: 1 → 可选,主动询问用户是否需要添加提示词,可回复"跳过"
  • 方案 prompt: 2 → 必填,必须收集
  • 用户已描述场景且足够具体 → 直接用作提示词
  • 长度 ≤ 1000 字符

提示词薄弱时(如"海边日出"),结合已选模型 desc 的特点润色,直接弹出选择框,前面不输出任何文字。选项:使用原始版(description 放原始内容)/ 使用润色版(description 放润色内容)。

提示词必须是独立的选择器,不和参数合并。

润色示例见 references/interaction-rules.md


第 6 步 - 展示配置摘要(必须)

用选择框展示摘要并请求确认(确认提交 / 修改参数 / 取消):

以下是本次视频生成的配置,请确认:

模型:可灵视频3.0 Omni
生成模式:图生视频(首帧)
提示词:"海边黄昏,一个女孩在散步,电影感镜头"
起始帧:https://cdn.example.com/start.jpg
画面比例:16:9
分辨率:720P
时长:5 秒

多图参考示例:

以下是本次视频生成的配置,请确认:

模型:可灵视频3.0 Omni
生成模式:多图参考
提示词:"两个角色在森林里对话"
参考图:
  1. https://cdn.example.com/ref1.jpg
  2. https://cdn.example.com/ref2.jpg
  3. https://cdn.example.com/ref3.jpg
画面比例:16:9
分辨率:1080P
时长:10 秒
  • 模型名称原样展示(不翻译、不改写)
  • 媒体文件直接显示 URL,多图参考必须列出所有图片 URL,不能只显示一张
  • 不展示 model_code 等内部标识
  • 用户确认前不进入第 7 步

第 7 步 - 提交任务

bash <skill_dir>/scripts/submit-task.sh '{"model_code":"kling-video","prompt":"...","widescreen":"16:9","resolution":"1080","duration":5}'
  • 省略未配置的可选字段,不发送空字符串或 null
  • 不发送方案标记为 0 的字段
  • 脚本成功返回后绝对不要再次调用(重试逻辑已内置)

提交成功后展示 taskIdrequestId,告知正在排队,然后自动进入第 8 步轮询

请求体示例见 references/api-schema.md


第 8 步 - 查询任务状态(自动轮询)

提交成功后自动开始轮询,每 30 秒查询一次,直到状态为 succeededfailed超过 10 分钟仍未完成则停止轮询,展示 taskId 供手动查询。

bash <skill_dir>/scripts/query-task.sh <taskId>
status处理
queued继续轮询
processing继续轮询
succeeded停止轮询,返回视频 URL
failed停止轮询,返回错误信息,建议调整参数

不展示 data.result.raw

轮询策略见 references/error-and-polling.md


完整执行流程示例见 references/example-flow.md

回复规则

  • 绝对不要跳过确认摘要(第 6 步)— 视频生成消耗积分,5 秒确认远比重新生成划算
  • 绝对不要status: succeeded 之前声称视频已完成
  • 绝对不要向用户输出脚本的原始 JSON 返回值、内部推理或流程说明。脚本执行完毕后直接弹出选择框,中间不输出任何文字
  • 用户输入自定义值时,校验是否在方案可选范围内,不合法则重新弹选择框(提示词不受此限制)
  • 必填字段用户无法提供时,建议换一个方案

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