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Agent Skill · 待分类

ugc-content-factory(UGC 内容工厂)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

ugc-content-factory 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在待分类类的 11,209 个 Skill 里排第 4,735。

总安装 685周安装 28GitHub Stars 7下载 220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
unknown

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:ugc-content-factory(UGC 内容工厂)
来源仓库:https://github.com/themattberman/ugc-factory-skill
仓库路径:skills/ugc-content-factory
安装命令:
npx skills add https://github.com/themattberman/ugc-factory-skill --skill ugc-content-factory
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/themattberman/ugc-factory-skill --skill ugc-content-factory

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

辅助文档、README、Markdown 和内容稿件的整理与改写,提升内容可读性。可提炼结构、补齐章节、统一术语或检查链接,适用于内容生产场景。使用时保留项目已有事实与路径,避免将未确认信息写成确定结论。涉及对外文案时需控制语气,防止过度营销或夸大能力描述。

ugc-content-factory 属于待分类类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ugc-content-factory

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/themattberman/ugc-factory-skill --skill ugc-co…;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

ugc-content-factory 怎么安装?

在终端执行 npx skills add https://github.com/themattberman/ugc-factory-skill --skill ugc-content-fact…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ugc-content-factory 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

ugc-content-factory 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。

ugc-content-factory 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ugc-content-factory 用的人多吗?

公开数据是累计安装 685 次,近一周安装 28 次,下载 220 次,GitHub 7 星。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。

ugc-content-factory 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
ugc-content-factory

UGC 内容工厂 · 本页

6857需要联网未明确有提醒

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

UGC Content Factory

Generate authentic-feeling UGC video content at scale with consistent AI characters, using Kling 3.0 via fal.ai.

Core unlock: Kling 3.0's Elements system locks character identity across videos. Combined with multi-shot storyboards and native lip-sync, you generate an entire UGC library with consistent "creators" — no real humans needed.

Director-First principle: Visuals before script. Design how each shot looks and moves FIRST, then write dialogue that fits. The script serves the visuals, not the other way around. This prevents the "sitting and talking" problem and forces visual variety.


Contract

Input

  • Required: product/brand name + what it does + target audience
  • Optional: existing character Element URLs, product image URLs, seed frames, brand voice file, competitor UGC examples
  • Format: text description, URLs, or brand context file

Output

  • Produces: Generated UGC video(s) via fal.ai Kling 3.0, plus creative brief, visual beat sheet, and synchronized script
  • Format: Video file (MP4), supporting docs inline
  • Downstream: Paid creative pipeline, social posting, client deliverables

Validation

  • Pre-conditions: fal.ai API access, product/audience known
  • Post-conditions: Video passes quality gate, all 4 stealth checks pass
  • Failure checks: If character/audience mismatch is unresolvable, flag before generating

Pipeline

MODULE A: Creative Director    → Creative Brief        → GATE 1
MODULE B: Cinematographer      → Visual Beat Sheet      → GATE 2
MODULE C: Screenwriter         → Synchronized Script    → GATE 3
MODULE D: Engineer             → fal.ai JSON + Execute  → Output

Each gate requires user approval before proceeding. Do not skip gates.


Before entering the pipeline

Ask the user:

  1. "Do you have an existing character to reuse?" (Element image URL)
  2. "Do you have product images to use as Elements?" (image URL)
  3. "Do you have a seed frame / first-frame image?" (image URL)

If no → new images generated with Nano Banana Pro via /ai-image-generation.

Check references/CHARACTER_LIBRARY.md for saved characters/products.


Module A: Creative Director

Read references/MODULE_A_CREATIVE_DIRECTOR.md for full format/hook/validation details. Also load: references/CHARACTER_LIBRARY.md, references/SCENE_SETTINGS.md.

Core decisions:

  1. Pick ONE content format (Testimonial, Unboxing, Problem/Solution, Comparison, Demo)
  2. Pick ONE hook angle (Gatekeep, Skeptic, Fail, Visual Shock)
  3. Select character archetype + setting
  4. Run Stealth Validation (all 4 checks must pass)

Stealth Validation (mandatory — content must pass ALL 4):

  • CAMOUFLAGE: Would a scroller identify this as an ad in the first 0.5s? If yes → de-polish.
  • VIBE: Does the viewer leave within 3s? If yes → lead with value before product.
  • INTEGRATION: When the product appears, does the viewer swipe away? If yes → make product appearance inevitable, not forced.
  • IMPERFECTION: Does this feel like a marketing team's output? If yes → add messiness, filler words, casual language.

⛔ GATE 1 output — present to user:

FieldContent
Product[name]
Target Audience[who]
Content Format[which one]
Hook Angle[which one]
Audio Hook[exact opening line]
Visual Hook[what's on screen]
Character[archetype + specifics]
Setting[environment + details]
Target Duration[seconds]
Stealth: CamouflagePASS/FAIL + note
Stealth: VibePASS/FAIL + note
Stealth: IntegrationPASS/FAIL + note
Stealth: ImperfectionPASS/FAIL + note

STOP. Wait for approval.


Module B: Cinematographer

Read references/MODULE_B_CINEMATOGRAPHER.md for Kling prompt engineering, shot design, and image generation details.

Core decisions:

  1. Design shot-by-shot visual sequence (NO dialogue yet)
  2. Assign variable durations: 3s (punchy hooks), 4s (setup), 5s (demos that breathe)
  3. Apply the 6 prompt elements: Camera, Subject, Environment, Lighting, Texture, Emotion
  4. Verify scene variety: at least 2 of 4 shots must change position or angle
  5. Generate seed images if using I2V workflow (Nano Banana Pro only)

4 rules of Kling prompting (always apply):

  1. Lead with cinematic camera verbs (dolly push, whip-pan, shoulder-cam drift)
  2. Include texture details (grain, reflections, condensation, fabric sheen)
  3. Describe temporal flow (beginning → middle → end of each shot)
  4. Name real light sources (not "dramatic lighting" — say neon signs, golden hour, fluorescent tubes)

⛔ GATE 2 output — present to user:

For each shot:

FieldContent
Shot #Sequential
Duration3s / 4s / 5s
CameraShot type + movement
SubjectWho + physical action
EnvironmentSetting details
LightingLight source + feel
TexturePhysical details that sell realism
ExpressionFacial/emotional state
Action FlowBeginning → middle → end

Plus scene variety check (passes 2-of-4 rule?) and Kling prompt checklist.

STOP. Wait for approval.


Module C: Screenwriter

Read references/MODULE_C_SCREENWRITER.md for audio rules, script templates, and sync mapping.

The Visual Beat Sheet is LOCKED. Write dialogue that fits existing shot durations. Do not change durations or camera directions.

Core rules:

  • Word budget: ~2.5 words/sec (3s = 7-8 words, 4s = 9-10 words, 5s = 12-13 words)
  • Authentic cadence: Include filler words ("honestly," "like," "okay so"), self-corrections, natural pauses
  • No ad-speak: Ban "revolutionary," "game-changing," "best-in-class." Nobody talks like that.
  • The bridge: Product mention must feel inevitable, not forced. It's a detail in a story, not a pivot point.
  • Product name: Once, maybe twice. Not more.

Dialogue litmus test: Read it aloud. If it sounds like copy, rewrite it.

⛔ GATE 3 output — present to user:

For each shot:

FieldContent
Shot #From beat sheet
DurationFrom beat sheet (DO NOT CHANGE)
Word BudgetDuration × 2.5
ScriptExact dialogue
Word CountActual / budget
Delivery NotesTone direction

Plus: full continuous script, total word count, sync check (PASS/FAIL).

STOP. Wait for approval.


Module D: Engineer

Read references/MODULE_D_ENGINEER.md for API reference, JSON assembly, chunking strategy, and gotchas.

Core execution:

  1. Merge Visual Beat Sheet + Script into multi_prompt JSON
  2. Run compilation checklist
  3. Submit to fal.ai (standard for test, pro for final)
  4. Use queue endpoint for anything over 5s
  5. Present output with quality check

Critical API constraints:

  • voice_ids and elements are mutually exclusive — drives chunking strategy for >15s videos
  • Minimum shot duration is 3 seconds
  • Don't include both prompt and multi_prompt
  • Use voice_url (not audio_url) for create-voice endpoint

Quality Gate (before delivering final output)

Every output must pass ALL of these:

  • Character feels native to the target audience (not a model, not a stock photo)
  • Script sounds like a real person, not a marketer (read it aloud)
  • First 2 seconds create genuine stop power
  • Setting feels plausible and incidental, not staged
  • Visual sequence varies enough (no 4 shots of sitting-and-talking)
  • Dialogue fits shot durations (sync check passes)
  • Product enters after the hook, not during it
  • No ad-speak in dialogue
  • One core message, not three
  • Works with sound off (text overlays carry the story if used)
  • All 4 stealth checks still pass on final output
  • A real person scrolling would not immediately identify this as an ad

If any check fails, fix before delivering. Do not note the issue and move on.


The 80/20

80% of UGC success comes from:

  1. Right character for target audience
  2. Specific, genuine-sounding script (not marketing speak)
  3. Appropriate casual setting (not studio)
  4. Strong hook in first 2 seconds

Get these right and the rest is optimization.


Integration with other skills

SkillUse for
/ai-image-generationCharacter reference images via Nano Banana Pro
/hooksGenerate hook variations for A/B testing
/direct-response-copyScript optimization for conversion
/brand-voiceEnsure scripts match client voice

Reference files

FileWhen to readContent
MODULE_A_CREATIVE_DIRECTOR.mdGate 1Formats, hooks, stealth validation, creative brief template
MODULE_B_CINEMATOGRAPHER.mdGate 2Visual design, Kling prompting, beat sheet template, Nano Banana prompts
MODULE_C_SCREENWRITER.mdGate 3Audio rules, sync, script templates, mapping template
MODULE_D_ENGINEER.mdExecutionAPI reference, JSON assembly, queue management, chunking
CHARACTER_LIBRARY.mdGate 1Archetypes, saved characters, design process
SCENE_SETTINGS.mdGate 1Setting tables and selection rules
TESTING-PLAN.mdWhen validatingComponent, integration, and quality test matrix

平台分布

Codex

35.98%
按下载量换算79

Claude

28.55%
按下载量换算63

Cursor

18.58%
按下载量换算41

Gemini CLI

8.26%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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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