據 AlphaMoat 統計,截至 2026-09,llm-video-generator 累計下載 856 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 5,074 位,在「設計創意」分類(512 個)中排名第 512 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 22 | 853 | - | - |
| SkillHub ↗ | 106 | 856 | - | — |
各平台的計數口徑不同,統計時間不一致。
主文描述文字生成、圖片生成影片及幀引導生成,可透過連續生成和拼接延長片段,並提供時長、畫面風格、解析度、幀率和音訊設定流程。提示詞與輸入圖片可能被髮送給 ZhipuAI;僅在使用者同意後提交敏感內容。任務後設資料和結果會儲存在本地,分享前應檢查輸出目錄。拼接媒體時只使用可信檔名,避免處理不可信或異常路徑。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 856 | — | - |
| 2026-08 | 797 | +6.4% | - |
| 2026-07 | 749 | +26.1% | - |
| 2026-06 | 594 | +16.7% | - |
| 2026-05 | 509 | — | - |
| 2026-03 | 274 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- name: llm-video-generator description: > Generate videos from text descriptions using ZhipuAI CogVideoX-3 model. Supports text-to-video, image-to-video, and first/last frame-to-video generation. Automatically handles long videos (over 5s) by chaining multiple generation calls with last-frame continuation. Use when the user asks to create/generate a video from text, make a video, text-to-video, 文生视频, 生成视频, 做个视频, or any request involving converting text/images into a video. Supports configuring video content, style, resolution (up to 4K), frame rate (30/60fps), audio, and duration.
---
# LLM Video Generator
Generate videos via ZhipuAI CogVideoX-3. Each API call produces ~5s of video.
For longer videos, chain multiple calls using last-frame continuation, then concatenate.
## Scripts
All scripts use `/opt/anaconda3/bin/python3`. Resolve `<skill-dir>` to this skill's directory.
| Script | Purpose |
|--------|---------|
| `scripts/video_gen.py` | Core generation (3 modes: text2video, image2video, frames2video) |
| `scripts/extract_last_frame.py` | Extract last frame from a video (for continuation) |
| `scripts/concat_videos.py` | Concatenate multiple video segments into one |
## Workflow
### Step 1: Assess Request & Clarify
**Clear request** → proceed to Step 2. A request is clear when:
- Video content/scene is described with enough detail
- Style or visual tone is specified or implied
- Duration is stated (default: 5s if not specified)
**Vague request** → propose a plan first:
```
基于你的需求,我拟定了以下视频方案:
📹 **视频内容**: [detailed scene description with key moments]
🎨 **视频风格**: [e.g., 写实/动画/电影感/温馨...]
⏱️ **视频时长**: [Xs, note: will be generated in 5s segments]
🔊 **背景音乐**: 有/无
📐 **分辨率**: 1920x1080
🎞️ **帧率**: 30fps
你觉得这个方案可以吗?需要调整哪些部分?
```
Iterate with the user until confirmed.
### Step 2: Estimate Time & Notify User
Before starting generation, calculate and report the estimated time:
**Time estimation formula:**
- Base: 1 minute per second of video (e.g., 20s video ≈ 20 minutes)
- High-definition (4K or 60fps): add +30% (e.g., 20s 4K video ≈ 26 minutes)
- Additional overhead: ~2 minutes for frame extraction, concatenation, and compression
- Segments: ceil(target_duration / 5)
**MUST send this message to the user before starting generation:**
```
⏳ **视频生成预估**
📊 分段计划:{N} 段(每段约5秒)
⏱️ 预计总耗时:约 {estimated_minutes} 分钟
📐 分辨率:{resolution}
视频生成是一个耗时过程,请耐心等待。我会在每段完成后实时汇报进度。
```
Example for a 30s 1080P video:
- 6 segments, base time = 30 minutes, +2 min overhead → ~32 minutes
- Message: "预计总耗时:约 32 分钟"
Example for a 20s 4K video:
- 4 segments, base time = 20 * 1.3 = 26 min, +2 min → ~28 minutes
### Step 3: Plan Generation Segments
Each API call produces ~5 seconds. Calculate segments: `ceil(target_duration / 5)`
For multi-segment videos, plan how the content evolves across segments. Write a prompt for each segment describing what happens in that 5-second window, maintaining visual continuity.
### Step 4: Execute Generation with Progress Reports
**CRITICAL: After each segment completes, IMMEDIATELY send a progress message to the user before starting the next segment.** Do not wait until all segments are done.
**Progress message format (send via message tool or inline reply after each segment):**
```
✅ 进度:{completed}/{total} 段完成(第{N}段已生成)
📝 内容:{brief segment description}
⏱️ 本段耗时:{minutes}分钟
📊 预计剩余:约 {remaining_minutes} 分钟
```
**Generation process:**
**Segment 1 — Text-to-Video:**
```bash
/opt/anaconda3/bin/python3 <skill-dir>/scripts/video_gen.py text2video \
--prompt "<segment_1_prompt>" \
--quality quality --audio true --size 1920x1080 --fps 30 \
--output-dir <output-dir> --max-wait 900
```
→ **Send progress message to user**
**Segments 2+ — Image-to-Video (last-frame continuation):**
For each subsequent segment:
1. Extract last frame from the previous segment's video:
```bash
/opt/anaconda3/bin/python3 <skill-dir>/scripts/extract_last_frame.py \
<previous_video.mp4> --output <output-dir>/frame_segN.png
```
2. Generate next segment using the last frame as input:
```bash
/opt/anaconda3/bin/python3 <skill-dir>/scripts/video_gen.py image2video \
--prompt "<segment_N_prompt>" \
--image-url <output-dir>/frame_segN.png \
--quality quality --audio true --size 1920x1080 --fps 30 \
--output-dir <output-dir> --max-wait 900
```
3. → **Send progress message to user**
Repeat for all segments.
**Alternative — Frames-to-Video mode:**
If you have both a starting and ending image for a segment:
```bash
/opt/anaconda3/bin/python3 <skill-dir>/scripts/video_gen.py frames2video \
--prompt "<description>" \
--first-frame <first.png> --last-frame <last.png> \
--quality quality --audio true --size 1920x1080 --fps 30 \
--output-dir <output-dir>
```
### Step 5: Concatenate Segments
After all segments are generated, combine them:
```bash
/opt/anaconda3/bin/python3 <skill-dir>/scripts/concat_videos.py \
--inputs <seg1.mp4> <seg2.mp4> ... \
--output <output-dir>/final_video.mp4
```
If the final file exceeds 25MB (Feishu upload limit), compress with ffmpeg:
```bash
ffmpeg -i <input> -c:v libx264 -crf 32 -c:a aac -b:a 96k -vf "scale=1280:720" -y <output>
```
### Step 6: Deliver
- Share the final video file with the user
- For Feishu delivery: use feishu-send-file skill to send the .mp4 file
- Final report:
```
🎬 **视频生成完成!**
⏱️ 总时长:{duration}秒
📦 文件大小:{size}MB
📊 共 {N} 段,总耗时 {total_minutes} 分钟
```
## Prompt Tips
- Use **English prompts** for best quality (translate Chinese descriptions)
- Be specific: scene, camera angle, lighting, motion, atmosphere
- Include style keywords: cinematic, realistic, cartoon, watercolor, etc.
- For continuation segments, describe the **action progression**, not the full scene from scratch
- Keep each segment prompt concise (1-3 sentences)
## Parameters Reference
| Parameter | Flag | Default | Options |
|-----------|------|---------|---------|
| Prompt | `--prompt` | (required) | Descriptive text |
| Quality | `--quality` | `quality` | `quality` / `speed` |
| Audio | `--audio` | `true` | `true` / `false` |
| Resolution | `--size` | `1920x1080` | `1280x720`, `1920x1080`, `3840x2160` |
| Frame rate | `--fps` | `30` | `30` / `60` |
| Output dir | `--output-dir` | `.` | Any writable path |
| Poll interval | `--poll-interval` | `10` | Seconds |
| Max wait | `--max-wait` | `900` | Seconds (default raised for reliability) |
## Error Handling
- **Missing ZHIPU_API_KEY**: Ask user to set environment variable
- **Missing zai-sdk**: `pip install zai-sdk` (under anaconda)
- **Missing ffmpeg**: Required for frame extraction and concatenation
- **Task timeout**: Increase `--max-wait` or retry; check task status manually via API
- **Task failed**: Simplify the prompt and retry
- **File too large for Feishu**: Compress with ffmpeg (reduce resolution or increase CRF)
ContextWeave 架構圖一鍵生成
用 Nano Banana Pro 生成或編輯圖片的指令碼技能。
用 whisper 命令列工具在本機將音訊轉成文字或進行翻譯。
透過 MCP 服務生成與編輯圖表的技能。
面向介面設計的思路與落地建議技能,覆蓋佈局、互動流程、設計令牌與前端改動。
面向多家影片模型 API 的 AI 影片生成與編排技能。
用 ffmpeg 從影片裡取出畫面幀,或生成便於快速檢視的縮圖。
用本地 Whisper 把音訊檔案轉成文字,模型下載完成後可完全離線執行。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。