tuzi-short-video

为小红书、抖音、X/Twitter、视频号等平台生成短视频内容。分析用户输入,自动适配平台规格,生成视频脚本并调用视频生成后端。当用户要求"生成短视频"、"做一个视频"、"短视频"或为社交平台创建视频内容时使用。

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Install skill "tuzi-short-video" with this command: npx skills add tuziapi/tuzi-skills/tuziapi-tuzi-skills-tuzi-short-video

Short Video Creator

Creates short videos for social media platforms. Analyzes content, adapts to platform specs, generates prompts, and calls tuzi-video-gen backend.

Script Directory

This is a workflow-only skill (no scripts). It delegates video generation to tuzi-video-gen.

VIDEO_GEN_DIR: Resolve from skills/tuzi-video-gen/SKILL.md relative to project root.

Step 0: Load Preferences ⛔ BLOCKING

CRITICAL: This step MUST complete BEFORE any video generation.

0.1 Check API Key

echo "${TUZI_API_KEY:-not_set}"
grep -s TUZI_API_KEY .tuzi-skills/.env "$HOME/.tuzi-skills/.env"
ResultAction
Key foundContinue to Step 0.2
Key NOT found⛔ Run API key setup (see references/config/first-time-setup.md)

0.2 Check EXTEND.md

test -f .tuzi-skills/tuzi-short-video/EXTEND.md && echo "project"
test -f "$HOME/.tuzi-skills/tuzi-short-video/EXTEND.md" && echo "user"
ResultAction
FoundLoad, parse, apply settings
Not found⛔ Run first-time setup (references/config/first-time-setup.md)
PathLocation
.tuzi-skills/tuzi-short-video/EXTEND.mdProject directory
$HOME/.tuzi-skills/tuzi-short-video/EXTEND.mdUser home

EXTEND.md Supports: Default platform | Default model

Schema: references/config/preferences-schema.md

Step 1: Analyze User Input

Analyze the user's content (text, article, script, topic description):

  • Identify main topic and key points
  • Determine content type (tutorial, story, showcase, explainer)
  • Estimate ideal video length and segment count

Step 2: Confirm Platform and Parameters

Use AskUserQuestion to let user choose target platform (unless specified or saved in EXTEND.md):

header: "Target Platform"
question: "目标发布平台?"
options:
  - label: "小红书"
    description: "9:16 竖屏, 720x1280"
  - label: "抖音"
    description: "9:16 竖屏, 1080x1920"
  - label: "X/Twitter"
    description: "16:9 横屏, 1280x720"
  - label: "视频号"
    description: "9:16/16:9, 1080x1920"

Platform Presets

PlatformAspectSizeDurationNotes
小红书9:16720x128015-60s竖屏短视频
抖音9:161080x192015-60s竖屏,高分辨率
X/Twitter16:91280x7205-140s横屏为主
视频号9:161080x192015-60s竖屏优先

Platform details: See references/platforms/

Step 3: Generate Video Script

Based on content analysis and platform:

Single Video (content fits one clip)

  1. Write a detailed video generation prompt in English
  2. Include: visual scenes, camera movements, style, mood, lighting
  3. Save prompt to prompts/01-video-prompt.md

Long Video (content needs multiple segments)

  1. Split content into logical segments (opening + body + ending)
  2. Write per-segment prompts with continuity notes
  3. Save to prompts/01-segment-opening.md, 02-segment-main.md, etc.

Workflow details: See references/workflows/

Step 4: Generate Video

Single Video

npx -y bun ${VIDEO_GEN_DIR}/scripts/main.ts \
  --promptfiles <output-dir>/prompts/01-video-prompt.md \
  --video <output-dir>/01-video-<slug>.mp4 \
  --size <platform-size> \
  --seconds <duration>

Long Video

npx -y bun ${VIDEO_GEN_DIR}/scripts/main.ts \
  --video <output-dir>/01-video-<slug>.mp4 \
  --segments <N> \
  --segment-prompts <output-dir>/prompts/01-segment-opening.md <output-dir>/prompts/02-segment-main.md ... \
  --size <platform-size> \
  --seconds <per-segment-seconds>

Step 5: Completion Report

Display:

  • Output video path
  • Platform and specs used
  • Model used
  • Duration

Output Directory

short-video/{topic-slug}/
├── source-{slug}.md
├── prompts/
│   ├── 01-video-prompt.md          (single video)
│   ├── 01-segment-opening.md       (long video)
│   ├── 02-segment-main.md
│   └── 03-segment-ending.md
├── segments/                        (long video temp, cleaned up)
└── 01-video-{slug}.mp4             (final output)

Extension Support

Custom configurations via EXTEND.md. See Step 0 for paths and supported options.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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