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