Token导航 LogoToken导航TokenDH.com
待分类需要联网github未标认证来源可访问许可证需确认审计提醒

ugc-content-factoryUGC 内容工厂

Agent Skill

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。

总安装

685

周安装

28

GitHub Stars

7

下载量

220
CodexClaudeCursorGemini CLI

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

unknown

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: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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

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 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

继续浏览同类 Skills