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Agent Skill · 研究检索

shorts-builder(短裤制造商)

shorts-builder 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

shorts-builder 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 13,741。

总安装 9,131周安装 373GitHub Stars 公开资料未说明下载 2,954核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

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:shorts-builder(短裤制造商)
来源仓库:https://github.com/hexidyg/shorts-builder
安装命令:
openclaw skills install shorts-builder
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 shorts-builder

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

Shorts builder 是故事生成管道技能,支持连续多集生产和双重质量控制机制。可管理图文素材并通过 AI 质检加人工确认流程保障输出质量。通过 clawhub 安装,需配置工作流节点和执行触发条件规则。使用前应定义清晰的故事大纲和角色设定以避免偏离预期方向。建议建立版本控制系统追踪每次迭代修改记录便于回溯优化。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install shorts-builder;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

shorts-builder 怎么安装?

在终端执行 openclaw skills install shorts-builder。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

shorts-builder 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

shorts-builder 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

shorts-builder 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

shorts-builder 用的人多吗?

公开数据是累计安装 9,131 次,近一周安装 373 次,下载 2,954 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

shorts-builder 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
shorts-builder

短裤制造商 · 本页

9,131—需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
story-pipeline
description
Story generation pipeline skill. Supports multi-episode continuous generation, graph management, AI quality check + human confirmation dual control mechanism. Automatically manages relationships between characters, scenes, and hooks.

Story Generation Pipeline Skill

Features

This skill implements a complete story generation pipeline:

  1. Continuous Episode Generation - Automatically generates next episode based on previous content
  2. Graph Management - Storage and querying of character, scene, and hook relationships
  3. Dual Confirmation Control - AI quality check followed by human confirmation
  4. State Persistence - Supports pause, resume, and multiple parallel stories

Core Workflow

┌──────────────┐    ┌──────────────┐    ┌──────────────┐    ┌──────────────────┐
│ Generate Ep N│ -> │ AI Review    │ -> │ Graph Storage│ -> │ Wait Human Confirm│
└──────────────┘    └──────────────┘    └──────────────┘    └──────────────────┘
       │                   │                   │                      │
       ▼                   ▼                   ▼                      ▼
  Query Graph         Pass/Retry        Store Relations        Continue/Modify/End

Usage

Start New Pipeline

User: Start a new story pipeline, theme: Chinese girl comeback, target episodes: 10

Continue Pipeline

User: Continue story pipeline [pipeline_id]

User Confirmation Actions

User: Approved, continue to next episode
User: Modify: [specific feedback]
User: Pause
User: End

Dual Confirmation Control Mechanism

Layer 1: AI Quality Check

After each episode is generated, AI automatically checks:

Check ItemDescription
Plot CoherenceNatural connection with previous episode
Character ConsistencyCharacter behavior matches established traits
Hook HandlingReasonable addition/closure of hooks
Pacing ControlAppropriate plot progression speed
Emotional CurveReasonable emotional ups and downs

Scoring Standard: 0-10 points, below 7 triggers retry (max 3 times)

Layer 2: Human Confirmation

After AI review passes, display preview and wait for user confirmation:

📋 Episode N Preview
━━━━━━━━━━━━━━━━━━━━━━━━
【Plot Summary】
...

✅ AI Score: 8.5/10
✅ Hook Status: 1 new, 1 closed

Please select:
1️⃣ Approved, continue to next episode
2️⃣ Needs modification (please specify)
3️⃣ Pause
4️⃣ End
━━━━━━━━━━━━━━━━━━━━━━━━

Graph Management

Graph Query

Before generating episode N, query episode N-1's graph:

graph_manager.query_graph(pipeline_id, episode=N-1)

Returns:

  • Complete content of previous episode
  • Character list and their statuses
  • Unclosed hooks
  • Key scenes
  • Relationship network

Graph Storage

After user confirmation, store current episode's complete content:

graph_manager.save_graph(pipeline_id, episode=N, content)

Stores: Complete generation result of the episode (not split into elements)


State Management

State File: data/pipeline_state.json

{
  "pipelines": {
    "pipeline_2026-03-05-001": {
      "theme": "Chinese girl comeback",
      "target_episodes": 10,
      "current_episode": 3,
      "status": "waiting_user_confirm",
      "created_at": "2026-03-05T15:00:00",
      "updated_at": "2026-03-05T15:30:00",
      "ai_review": {
        "score": 8.5,
        "checks": {...}
      },
      "last_output": {
        "episode": 3,
        "summary": "...",
        "content": "Complete content..."
      }
    }
  }
}

Status Types

StatusDescription
generatingCurrently generating
ai_reviewingAI review in progress
waiting_user_confirmWaiting for human confirmation
pausedPaused
completedCompleted
errorError state

Script Description

pipeline.py - Main Control Loop

  • Initialize pipeline
  • Coordinate modules
  • Handle user commands
  • Manage loop state

ai_reviewer.py - AI Quality Check

  • Execute quality scoring
  • Generate review report
  • Determine pass/fail

episode_generator.py - Episode Generation

  • Generate new episode based on graph context
  • Handle hook continuation and closure
  • Handle retry logic

graph_manager.py - Graph Management

  • Graph query (call remote API)
  • Graph storage (call remote API)
  • Local cache management

API Description

Graph API

Query API:

{
  "action": "query",
  "pipeline_id": "pipeline_2026-03-05-001",
  "episode": 2
}

Storage API:

{
  "action": "save",
  "pipeline_id": "pipeline_2026-03-05-001",
  "episode": 3,
  "content": "Complete generation content for episode 3..."
}

Episode Generation Logic

First Episode Generation

Based on user-provided theme and goals, generate:

  • Main character settings
  • Initial scenes
  • Core conflict
  • Open hooks

Subsequent Episode Generation

Based on graph query results:

  1. Read previous episode content and unclosed hooks
  2. Continue main plot line
  3. Handle hooks (continue/close/add)
  4. Advance character growth arc
  5. Adjust emotional curve

Finale Generation

When target episodes reached or user requests end:

  • Close all remaining hooks
  • Complete character growth arcs
  • Generate conclusive ending

Important Notes

  1. Retry Mechanism - Max 3 retries when AI review fails
  2. Pause/Resume - Can resume via pipeline_id after pause
  3. Multiple Pipelines - Supports running multiple different-themed pipelines simultaneously
  4. Graph Consistency - Ensure correct character and hook relationships
  5. Hook Management - Track creation and closure status of each hook

Complete Workflow

Step 1: Create Pipeline

User: Start a new pipeline, theme: Cultivation boy, target 20 episodes

AI: OK, creating pipeline pipeline_20260305160000
    Theme: Cultivation boy
    Target: 20 episodes
    Style: Realistic cinematic
    
    Status: Initialized, ready to generate episode 1

Step 2: Generate Episode

AI calls start_generation(pipeline_id) to get generation prompt, then generates episode content based on the prompt.

Step 3: Submit AI Review

AI calls submit_episode(pipeline_id, episode, content) to submit generated content, then executes AI review.

Step 4: Process Review Result

AI calls process_ai_review(pipeline_id, episode, ai_result, content) to process review results.

If review fails (score < 7), automatically retry (max 3 times).

Step 5: Wait User Confirmation

After review passes, display preview and wait for user confirmation:

📋 Episode 1 Preview
━━━━━━━━━━━━━━━━━━━━━━━━
【Cultivation Journey Begins】

Young Li Yun discovers a mysterious jade pendant in the mountains,
从此踏上修仙之路...

✅ AI Score: 9.0/10
✅ New Hook: H-001 Origin of mysterious jade pendant

━━━━━━━━━━━━━━━━━━━━━━━━

Please select:
1️⃣ Approved, continue to next episode
2️⃣ Needs modification (please specify)
3️⃣ Pause
4️⃣ End

Step 6: Process User Confirmation

After user confirms, AI calls user_confirm(pipeline_id, action, note):

  • approve: Store graph, prepare next episode
  • modify: Regenerate based on feedback
  • pause: Pause pipeline
  • end: End pipeline

Step 7: Loop Generation

Repeat steps 2-6 until target episodes reached or user ends.


API Reference

Create Pipeline

create_pipeline(theme: str, target_episodes: int, style: str = "realistic cinematic")
# Returns: {"success": True, "pipeline_id": "...", "message": "..."}

Start Generation

start_generation(pipeline_id: str)
# Returns: {"success": True, "episode": N, "prompt": "generation prompt"}

Submit Episode

submit_episode(pipeline_id: str, episode: int, content: str)
# Returns: {"success": True, "review_prompt": "review prompt"}

Process AI Review

process_ai_review(pipeline_id: str, episode: int, ai_result: str, content: str)
# Returns: {"success": True, "passed": True/False, ...}

User Confirm

user_confirm(pipeline_id: str, action: str, note: str = None)
# action: "approve" / "modify" / "pause" / "end"
# Returns: {"success": True, "status": "...", ...}

Get Status

get_status(pipeline_id: str)
# Returns: Current pipeline status info

List Pipelines

list_pipelines()
# Returns: List of all pipelines

Resume Pipeline

resume_pipeline(pipeline_id: str)
# Returns: Resume result

Example Dialogue

User: Start a new pipeline, theme: Cultivation boy, target 20 episodes

AI: OK, creating pipeline pipeline_20260305160000
    Generating episode 1...
    
    📋 Episode 1 Preview
    ━━━━━━━━━━━━━━━━━━
    【Cultivation Journey Begins】
    Young Li Yun discovers a mysterious jade pendant in the mountains...
    
    ✅ AI Score: 9.0/10
    ✅ New Hook: H-001 Origin of mysterious jade pendant
    
    Please confirm: Approve/Modify/Pause/End
    
User: Approved

AI: Storing graph...
    Starting generation of episode 2...

Important Notes

  1. Retry Mechanism: Max 3 retries when AI review fails
  2. State Persistence: All states saved in data/pipeline_state.json
  3. Graph Storage: Stored via remote API, requires network connection
  4. Pause/Resume: Can resume via resume_pipeline after pause
  5. Multiple Pipelines: Supports running multiple different-themed pipelines simultaneously

平台分布

OpenClaw

94.96%
按下载量换算2,805

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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