nite
5e844d17ec
SKILL.md: - Workflow header: explicit statement that every step is optional Example: transcription-only → Steps 1,2,4,5 and stop - When to Use: add standalone transcription as valid use case - Don't use for: remove 'pure audio-only transcription' exclusion - Verification checklist: each item tagged with which step it belongs to and whether it can be skipped (e.g. Step 3/8 skip for transcription-only)
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 with Fun-ASR-Nano (default, 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. You can install it directly:
# Install globally (available across all projects)
npx skills add https://git.nite07.com/nite/video-edit-planner-skill.git -g -y
# Install to specific agents
npx skills add https://git.nite07.com/nite/video-edit-planner-skill.git -g -a claude-code -y
# List available skills without installing
npx skills add https://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)
git clone https://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.
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
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 (default, 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