Files
video-edit-planner-skill/README.md
T
nite b60931913b feat: add stock material search section + npx skills add install
- SKILL.md: new 'Finding stock materials' section (on-demand, not mandatory)
  - 5 material sources: 爱给网, B站素材酷, Tenor, GIPHY, Klipy
  - Language-based priority (Chinese users → 爱给网 + B站素材酷)
  - Semantic search term expansion guidance
  - B站素材酷: bcut-resource-search userscript or direct API
  - Anti-bot fallback: browser-based tools
- README.md + README.zh.md: add 'npx skills add' install method
  - Compatible with open agent skills ecosystem (73+ frameworks)
  - git clone as fallback option
  - New 'Stock Material Search' section
2026-07-14 12:00:39 +08:00

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# Video Edit Planner
An agent skill that helps plan video edits through iterative dialogue — transcribe audio, extract key frames on demand, analyze visuals, and produce a structured edit plan.
**Repository**: https://git.nite07.com/nite/video-edit-planner-skill
## Features
- **Audio transcription** — bundled [funasr-script](https://github.com/modelscope/FunASR) with Fun-ASR-Nano (high quality, Chinese-optimized) and SenseVoice (fast preview) models
- **On-demand frame extraction** — ffmpeg-based clip cutting (`-c copy`) + scene-change detection + uniform sampling, with hardware acceleration (CUDA/NVDEC preferred)
- **Artifact indexing** — SQLite database tracks all transcriptions, clips, and frames to avoid duplicate processing across sessions
- **Vision analysis guidance** — binary-search-style frame sampling strategy; works with any vision-capable model the agent's runtime provides
- **Iterative edit plans** — Markdown-table output (timecodes, segment descriptions, actions, transitions, notes) refined through follow-up questions
- **Agent-agnostic** — no platform-specific tool names; works with any agent framework (Hermes, Claude Code, Codex, etc.)
## Quick Start
### Prerequisites
| Dependency | Install |
|---|---|
| `ffmpeg` + `ffprobe` | Linux: `pacman -S ffmpeg` / `apt install ffmpeg`; macOS: `brew install ffmpeg`; Windows: `winget install Gyan.FFmpeg` |
| `uv` | Linux: `pacman -S uv`; macOS: `brew install uv`; Windows: `winget install astral-sh.uv`; fallback: `pip install uv` |
| `python3` | Linux: `pacman -S python`; macOS: `brew install python`; Windows: `winget install Python.Python.3` |
### Install the skill
**Option 1: `npx skills add` (recommended)**
This skill is compatible with the [open agent skills ecosystem](https://www.npmjs.com/package/skills). You can install it directly:
```bash
# Install globally (available across all projects)
npx skills add git@git.nite07.com:nite/video-edit-planner-skill.git -g -y
# Install to specific agents
npx skills add git@git.nite07.com:nite/video-edit-planner-skill.git -g -a claude-code -y
# List available skills without installing
npx skills add git@git.nite07.com:nite/video-edit-planner-skill.git --list
```
Supports 73+ agent frameworks including Claude Code, Codex, Cursor, OpenCode, and more.
**Option 2: `git clone` (manual)**
```bash
git clone git@git.nite07.com:nite/video-edit-planner-skill.git ~/.hermes/skills/media/video-edit-planner
```
For Hermes Agent specifically, clone into `~/.hermes/skills/media/`. For other agents, follow their respective skills directory conventions.
### First use
The transcription scripts need a one-time dependency setup:
```bash
cd ~/.hermes/skills/media/video-edit-planner/scripts/transcription && uv sync
```
This creates an isolated venv with `funasr`, `torch`, `torchaudio`. If you've used these packages via uv before, the cache is reused — first-time cost is only the link step.
## Workflow
```
1. Check dependencies (ffmpeg, uv, python3)
2. Gather inputs (video path + audio track index)
3. Ask editing requirements
4. Transcribe audio (skip if cached in index)
5. Extract clips & frames on demand (agent decides when transcript is insufficient)
6. Analyze frames with vision model (binary-search-style sampling)
7. Produce Markdown-table edit plan + overall recommendation
8. Iterate — user asks follow-ups, plan is refined
```
## Stock Material Search (optional)
When planning edits, users may need stock materials — stickers, GIFs, meme video clips, or B-roll. The skill provides guidance for finding these resources on demand.
### Material sources
| Source | URL | Content | Best for |
|---|---|---|---|
| 爱给网 | https://www.aigei.com/ | Chinese meme video clips (热梗, 42K+), free download | Chinese-language creators (B站/抖音) |
| B站素材酷 | https://cool.bilibili.com/ | Bilibili platform-native assets (video, audio, BGM, templates, stickers) | B站 creators; large Chinese meme collection |
| Tenor | https://tenor.com/ | GIFs + stickers, 30+ languages incl. Chinese, free API | Quick GIF/sticker search |
| GIPHY | https://giphy.com/ | Largest GIF library, 30+ languages, sticker channel | English/multilingual GIF search |
| Klipy | https://klipy.com/ | Localized GIF/sticker/clip API, supports Chinese | API-integrated search, localization |
For Chinese-language users, 爱给网 and B站素材酷 are recommended first.
### B站素材酷 search
B站素材酷 has no built-in search. Use the [bcut-resource-search](https://git.nite07.com/nite/bcut-resource-search) Tampermonkey userscript to add search functionality, or let the agent query the API directly.
## Project Structure
```
video-edit-planner/
├── SKILL.md # Skill definition (workflow, guidance, pitfalls)
├── README.md # This file (English)
├── README.zh.md # 中文说明
├── scripts/
│ ├── transcription/ # Bundled funasr-script (self-contained uv project)
│ │ ├── pyproject.toml
│ │ ├── uv.lock
│ │ ├── funasr_common.py # Shared: ffprobe, audio extraction, model runner
│ │ ├── funasr_nano.py # Fun-ASR-Nano entry point (high quality)
│ │ ├── funasr_fast.py # SenseVoice entry point (fast preview)
│ │ └── funasr_regular.py # Paraformer entry point (comparison)
│ ├── frames/
│ │ └── extract_frames.py # Clip extraction + frame sampling (ffmpeg wrapper)
│ └── index/
│ └── manage_index.py # SQLite index management (8 subcommands)
└── references/
└── frame-extraction-guide.md # Vision model token costs, resolution/batch guidance
```
## Index Database
All processing artifacts are tracked in an SQLite database (`<video_stem>.vedit.db`) stored next to the video file:
- **transcription** — JSON path, track index, duration
- **clips** — start/end time, file path, extraction reason
- **frames** — timestamp, file path, scene score, extraction method
This avoids re-transcribing or re-extracting frames for the same video across sessions.
## License
MIT