According to AlphaMoat, as of 2026-09, llm-video-generator has 856 downloads and 0 stars — ranked #5,074 of 5,078 AI skills overall, and #512 of 512 in Design & Media.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 22 | 853 | - | - |
| SkillHub ↗ | 106 | 856 | - | — |
Platforms count differently and update at different times.
The primary describes text-to-video, image-to-video, and frame-guided generation, extending clips through continuation and concatenation, with settings for duration, style, resolution, frame rate, and audio. Prompts and supplied images may be sent to ZhipuAI; submit sensitive content only with user approval. Task metadata and results are stored locally, so review output directories before sharing. Use trusted filenames when concatenating media and avoid untrusted or unusual paths.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 856 | — | - |
| 2026-08 | 797 | +6.4% | - |
| 2026-07 | 749 | +26.1% | - |
| 2026-06 | 594 | +16.7% | - |
| 2026-05 | 509 | — | - |
| 2026-03 | 274 | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 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)
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.