video-edit-planner-skill/SKILL.md

name, description, license, tags

name	description	license	tags
video-edit-planner	Use when planning video edits: transcribe audio, extract key frames, analyze visuals, and produce cut/transition/assembly plans via iterative dialogue.	MIT	video-editing transcription funasr frame-extraction vision planning ffmpeg

Video Edit Planner

Overview

A conversational video-editing planning assistant. The user provides a video file and an audio track index; the skill transcribes the audio, extracts key frames on demand, analyzes them visually, and produces an iterative Markdown-table edit plan (timecodes, segment descriptions, transitions, notes).

The skill is agent-driven, not scripted: it provides tools (transcription, clipping, frame extraction, index management) and guidance for the agent to autonomously decide when and what to extract based on user needs.

When to Use

• User provides a video path and wants help planning cuts, transitions, or assembly.
• User asks "help me edit this video" or "what parts should I cut from this recording."
• User needs to understand video content beyond what the transcript alone reveals.
• User wants an iterative dialogue to refine an edit plan over multiple turns.
• User only needs audio transcription from a video file.
• User needs to extract and analyze specific video segments visually.
• User needs help finding stock materials (stickers, GIFs, meme clips) for editing.

Any subset of these capabilities is a valid use case — not every session requires the full workflow.

Don't use for: actual video editing/rendering (this skill plans and transcribes, it does not produce rendered video files).

Workflow

Every step is optional. The steps below describe the full end-to-end workflow, but the agent should only execute the steps relevant to the user's request. Users may need any subset of these capabilities — transcription only, frame extraction only, material search only, or a full planning session. Use the user's request to determine which steps apply; do not force the full pipeline when it is not needed.

Step 1 — Check dependencies

Before any processing, verify the environment. Missing dependencies must be installed only with explicit user consent.

Dependency	Check command	Install (prefer OS package manager; fallback to pip)
ffmpeg + ffprobe	which ffmpeg ffprobe	Linux: sudo pacman -S ffmpeg / sudo apt install ffmpeg; Windows: winget install Gyan.FFmpeg; macOS: brew install ffmpeg
uv	which uv	Linux: sudo pacman -S uv / sudo apt install uv (if available); macOS: brew install uv; Windows: winget install astral-sh.uv; fallback: pip install uv
python3 / Python 3.12 for transcription	python --version / python3 --version	Use Python 3.12 for the bundled FunASR project, especially on Windows. Linux: sudo pacman -S python / sudo apt install python3; Windows: winget install Python.Python.3.12; macOS: brew install python@3.12

Completion criteria: all three dependencies confirmed present, or user has explicitly declined installation (in which case stop — the skill cannot proceed).

Step 2 — Gather inputs

Ask the user for:

1. Video file path (absolute path; translate Windows paths like C:\Users\...\x.mkv to /mnt/c/Users/.../x.mkv on WSL).
2. Audio track index(es) — the ffmpeg stream index, not ordinal. The user may provide multiple tracks (e.g., track 1 = own mic, track 2 = Discord audio). Each track is transcribed separately and saved as a separate JSON file. If unknown, list tracks:

   ffprobe -v error -select_streams a -show_entries stream=index,codec_name,channels,channel_layout:stream_tags=language,title -of json VIDEO_PATH
   

Completion criteria: video path and at least one track index confirmed.

Step 3 — Ask editing requirements

Before transcribing, ask the user what they want to do with the video. User requirements are often vague in the first pass — that's expected. The plan is refined iteratively.

If the agent's runtime supports structured questioning (e.g., a clarify tool or a grill-me skill), use it to gather requirements systematically. Ask questions one at a time with multiple-choice options — this is much more effective than a free-text wall of questions.

The requirements questionnaire below covers the key axes for gameplay/creator video editing. Not every question applies to every session — pick the relevant ones based on the user's initial request. See references/editing-requirements-questionnaire.md for the full questionnaire with choices.

Axis	Why it matters
Target platform (B站/抖音/YouTube/Shorts)	Determines aspect ratio, pacing, subtitle density, meme/热梗 density
Narrative style (搞笑集锦/故事线+搞笑/高光混剪)	Determines structure: pure clip reel vs. narrative arc with 起承转合
Role distribution (主视角/平权联机/主持人)	Determines whose voice/reactions to prioritize in cuts
Effect density (少量/中等/高密度)	Determines how many 表情包/贴纸/特效 to search for and apply
Editor tool (Premiere Pro/剪映/DaVinci/必剪)	Determines how specific transition/effect instructions are phrased
First edit or existing draft	Determines whether to plan from scratch or optimize existing cuts
BGM style preference	Determines BGM search/recommendation direction
Number of participants	Determines how to handle multi-person interaction and voice allocation
Audio handling (保留原声+字幕/重新配音/混合)	Determines subtitle work, BGM vs voice balance, and narration needs
Privacy (露脸+声音/不露脸声音OK/需打码变声)	Determines face-cam handling and any masking needs
Output format (时间戳笔记/脚本表格/分镜方案)	Determines deliverable detail level
Target duration	Determines how aggressively to cut and pace the content

Completion criteria: user has described at least a rough editing goal, and the agent has probed the relevant axes above.

Step 4 — Transcribe audio

Use the bundled funasr-script in scripts/transcription/. Prefer Fun-ASR-Nano as the default transcription model — it has higher accuracy and sensitivity on Chinese audio. Use SenseVoice only for fast previews.

First run requires uv sync (installs funasr + torch, may take several minutes). Subsequent runs reuse the cached venv.

All output files (transcription JSON, clips, frames) go in the same directory as the video file. Never use ~/whisperx_output/ or any other separate output directory — the --output-dir flag should point to the video's own directory. This keeps all artifacts co-located and portable.

Choose the execution environment based on where the video file lives and where the command will run. Keep heavy media I/O local to the filesystem that stores the video:

• If the video is on the Windows host filesystem (for example a WSL path under /mnt/c/..., /mnt/d/..., or a native Windows path like C:\\\\..., D:\\\\...), prefer running transcription on the Windows host. Give PowerShell commands with Windows paths. Do not hand the user wsl ... commands for the long transcription in this case; using WSL to read /mnt/c or /mnt/d defeats the storage-locality rule.
• If the video is inside the WSL/Linux filesystem (for example /home/..., /var/..., or another Linux mount), prefer running transcription in WSL/Linux. Give Linux shell commands with Linux paths.
• If the location is ambiguous, ask the user where the video is stored and where they want the workload to run.

Agent should do the lightweight setup work. Installing/checking uv, copying scripts/transcription/ to a Windows-local directory, and running uv sync are setup tasks. If the agent can execute them through its available command interface, it should do them itself after getting any needed consent for dependency installation. Only the actual long-running transcription command should be handed to the user to execute manually.

If the agent runs in WSL and Windows execution is chosen, the agent can invoke the Windows host via powershell.exe. It should copy or clone the transcription project to a Windows-local path (for example C:\\Users\\<user>\\AppData\\Local\\Temp\\vedit-transcription) and run setup there. Do not ask the user to manually copy files or run uv sync unless the agent cannot access the host command line or lacks permission.

Windows uv constraint: Windows uv should not operate on WSL UNC paths like \\\\wsl.localhost\\... or \\\\wsl$\\...; it can fail while managing .venv directories. Therefore, when the chosen execution environment is Windows, the transcription project itself must also be available under a Windows-local path. Follow references/wsl-windows-uv-transcription.md.

Transcription can be long-running. After setup is complete, generate the final transcription command and give it to the user to execute manually. Use PowerShell syntax for Windows execution and Linux shell syntax for WSL/Linux execution.

Critical: when you tell the user you will provide a command, include it immediately in the same response. Do not say "I'll give you the transcription command" and then move on to other topics without actually printing the command. The user should never have to ask "you said you'd give me the command but didn't."

Command templates (adjust paths for the user's environment):

# Recommended default: Fun-ASR-Nano (high quality, Chinese-optimized)
uv run --directory SKILL_DIR/scripts/transcription funasr-nano VIDEO_PATH --track TRACK_INDEX --output-dir VIDEO_DIR

# Fast preview only: SenseVoice
uv run --directory SKILL_DIR/scripts/transcription funasr-fast VIDEO_PATH --track TRACK_INDEX --output-dir VIDEO_DIR

# List tracks
uv run --directory SKILL_DIR/scripts/transcription funasr-nano VIDEO_PATH --list-tracks

Where VIDEO_DIR is the directory containing the video file, and SKILL_DIR is the skill installation directory.

Common OBS multi-track audio layouts. When the user says "multi-track" or mentions OBS recording, these are the typical stream layouts:

Track	Typical content
Stream 1 (index 1)	Game audio (desktop/system sound)
Stream 2 (index 2)	Microphone (commentary/narration)
Stream 3 (index 3)	Discord/voice chat (other participants)

Always verify with ffprobe before transcribing — the user may have a different layout. When the user says "transcribe the mic and Discord tracks", that typically means tracks 2 and 3.

Multi-track transcription: when the user provides multiple track indexes (e.g., track 1 = mic, track 2 = Discord), run the transcription command once per track. Each run produces a separate JSON file named <video_stem>_track<INDEX>_<model>.json. Record each transcription in the index (Step 5) with its corresponding track index. When answering user questions, the agent should cross-reference all available transcripts to understand the full conversation context.

Note on Discord audio quality: Discord tracks may contain noise, crosstalk, or non-participant chatter. This is normal — the ASR transcript will be noisier. When cross-referencing transcripts, treat Discord track content as supplementary context, not as the primary narrative source.

Skip if cached: check the index file (Step 5) for existing transcription entries. If a record exists for the same track index and the video file hasn't changed, reuse it.

Completion criteria: a transcription JSON exists for every requested track, and each is recorded in the index. Each JSON contains segments with timestamps and text.

Step 5 — Manage index file

A JSON file tracks all processing artifacts to avoid duplicate work. It lives next to the video file and is human-readable and editable.

<video_dir>/<video_stem>.vedit.json

All file paths are stored as relative paths (relative to the video directory). Moving the entire directory does not break any references. If files are split across directories, paths outside the video directory are stored as absolute paths — the user can edit the JSON directly with a text editor to fix them.

Structure (managed by scripts/index/manage_index.py):

{
  "transcriptions": [
    {
      "id": 1,
      "json_path": "video_track1_fun-asr-nano.json",
      "track_index": 1,
      "created_at": 1783997794.78,
      "duration_s": 120.5
    }
  ],
  "clips": [
    {
      "id": 1,
      "start_time": 30.0,
      "end_time": 60.0,
      "path": "video_clips/clip_30-60.mkv",
      "created_at": 1783997794.85,
      "reason": "user asked about this segment"
    }
  ],
  "frames": [
    {
      "id": 1,
      "clip_id": 1,
      "timestamp": 32.0,
      "path": "video_frames/frame_0001.jpg",
      "scene_score": 0.45,
      "method": "scene",
      "created_at": 1783997795.0
    }
  ]
}

Use scripts/index/manage_index.py for all CRUD operations:

# Initialize index file (idempotent)
uv run --directory SKILL_DIR python scripts/index/manage_index.py init --video VIDEO_PATH

# Add transcription record
uv run --directory SKILL_DIR python scripts/index/manage_index.py add-transcription --json-path PATH --track-index N --duration S

# Query existing transcription (all tracks)
uv run --directory SKILL_DIR python scripts/index/manage_index.py get-transcription

# Query specific track
uv run --directory SKILL_DIR python scripts/index/manage_index.py get-transcription --track 1

# Add a clip record
uv run --directory SKILL_DIR python scripts/index/manage_index.py add-clip --start S --end E --path PATH --reason "user asked about this segment"

# Add frame records (batch from a directory)
uv run --directory SKILL_DIR python scripts/index/manage_index.py add-frames --clip-id N --frames-dir DIR --method scene

# List all clips and frames
uv run --directory SKILL_DIR python scripts/index/manage_index.py list

# Check if a time range has already been extracted
uv run --directory SKILL_DIR python scripts/index/manage_index.py check-range --start S --end E

# Remove records for files that no longer exist on disk
uv run --directory SKILL_DIR python scripts/index/manage_index.py clean

Completion criteria: index file exists and all produced artifacts are recorded in it.

Step 6 — Extract clips and frames (on demand)

When to extract: the agent autonomously decides based on user needs. If the transcript alone is insufficient to answer (e.g., user asks about visual content, or a transcript segment is sparse/empty for a time range the user cares about), extract frames for that range.

Binary-search-style frame analysis:

1. Extract a clip for the target time range.
2. Extract scene-change frames + uniform samples from the clip.
3. Send a few representative frames to vision analysis.
4. If a sub-range needs deeper understanding, extract more frames from that sub-range.
5. Once the relevant range is identified, send all frames in that range (in batches if needed) to vision.

Clip extraction (keyframe-accurate, -c copy):

For frame extraction purposes, only the video stream is needed:

ffmpeg -hide_banner -y -ss START -to END -i VIDEO_PATH -c copy -map 0:v:0 CLIP_PATH

For clips that the user may want to import into a video editor or play back, preserve all audio tracks by mapping all streams instead of only the video:

ffmpeg -hide_banner -y -ss START -to END -i VIDEO_PATH -c copy -map 0 CLIP_PATH

-map 0 copies all streams (video + all audio tracks + subtitles). The agent should ask the user whether they need a full-multitrack clip or a video-only clip for frame extraction. When in doubt, produce both: a video-only clip for fast frame extraction, and a full-multitrack clip if the user plans to use it in an editor.

Note: When using -c copy with -ss before -i, seek accuracy is keyframe-level. If the user needs frame-exact cuts, re-encode is required (significantly slower). Keyframe accuracy is sufficient for planning purposes.

Frame extraction (scene detection + uniform sampling, hardware-accelerated, downsampled):

# Hardware-accelerated (CUDA/NVDEC preferred)
ffmpeg -hide_banner -y -hwaccel cuda -i CLIP_PATH \
  -vf "scale=1280:-2,fps=1/2,select='gt(scene,THRESHOLD)+gte(n,0)',showinfo" \
  -vsync vfr -q:v 3 FRAMES_DIR/frame_%04d.jpg

# CPU fallback
ffmpeg -hide_banner -y -i CLIP_PATH \
  -vf "scale=1280:-2,fps=1/2,select='gt(scene,THRESHOLD)',showinfo" \
  -vsync vfr -q:v 3 FRAMES_DIR/frame_%04d.jpg

Parameters the agent chooses:

Parameter	Default	Guidance
scale width	1280	Raise to 1920 for short clips (<30s) where detail matters; lower to 854 for long clips (>120s) to save tokens
THRESHOLD	0.3	Lower to 0.1–0.2 for gameplay footage (subtle scene changes); raise to 0.4 for dynamic content
fps sample rate	1/2 (1 frame per 2s)	Raise to 1 (1fps) for short clips; lower to 1/5 for long clips
-q:v (JPEG quality)	3	Range 2–31, lower = higher quality. 3 is high quality; 5 is a good balance

All clips and frames are saved next to the video file (e.g., <video_stem>_clips/ and <video_stem>_frames/). The user manages cleanup.

Record every extracted clip and frame in the index before proceeding.

Completion criteria: clips and frames exist on disk and are recorded in the index.

Step 7 — Vision analysis

Use whatever vision capability the agent's runtime provides to analyze extracted frames. The agent chooses:

• Which model to use (depends on the agent's runtime and available vision capabilities).
• Batch size — depends on the model's context window. See references/frame-extraction-guide.md for token cost estimates at different resolutions.
• Windows setup troubleshooting — see references/windows-funasr-uv-setup.md for Windows-host PowerShell transcription commands and uv/Python wheel issues such as editdistance source builds.
• How many frames per analysis pass — use the binary-search approach from Step 6.

Completion criteria: the agent has enough visual understanding to answer the user's question or produce the requested edit plan.

Step 8 — Produce edit plan

Output a Markdown table with overall recommendations, then let the user iterate. Respond in the same language the user is using.

## Edit Plan

### Overall Recommendation

<1-3 sentences of high-level recommendation addressing the user's goal>

### Detailed Plan

| Timecode          | Duration | Segment Description             | Action        | Transition | Notes              |
| ----------------- | -------- | ------------------------------- | ------------- | ---------- | ------------------ |
| 00:01:23–00:01:45 | 22s      | Character enters town, dialogue | Keep          | Hard cut   | Can add subtitles  |
| 00:01:45–00:02:10 | 25s      | Walking, no dialogue or event   | Cut           | —          | —                  |
| 00:02:10–00:02:35 | 25s      | Combat starts, highlight moment | Keep, slow-mo | Fade in    | Sync to BGM rhythm |
| ...               | ...      | ...                             | ...           | ...        | ...                |

Completion criteria: user receives a Markdown table addressing their stated goal, and is invited to ask follow-up questions.

Step 9 — Iterate

The user will likely ask follow-ups: "what about this section?", "add a transition here", "can you check what's happening at 5:30?". For each:

1. Check if the answer can be derived from existing transcript + frames (check index first).
2. If not, extract new clips/frames for the requested range (Step 6).
3. Analyze and update the plan.

Completion criteria: user is satisfied with the plan or explicitly ends the session.

Finding stock materials (optional, on demand)

When the user needs editing materials — stickers, GIFs, meme video clips, B-roll — the agent can help search or provide links for the user to search themselves. This section is only triggered when the user asks for materials; it is never a mandatory step.

Material sources

Source	URL	Content	Best for
爱给网	https://www.aigei.com/	Chinese meme video clips (热梗, 4.2万+), 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

Priority by language

If the user communicates in Chinese, recommend 爱给网 and B站素材酷 first — they have the most Chinese-relevant meme and sticker content. For non-Chinese users, Tenor and GIPHY are good defaults.

Search strategy

• Expand search terms based on semantic understanding of the user's request. If the user says "I want a clip of someone looking shocked", search terms might include: "惊讶 / 震惊 / 吃惊 / 哇 / wtf / shocked / surprised". Use the user's language as the base, expand with synonyms and internet slang.
• The agent can either search directly (using web tools or API calls) or suggest expanded search terms for the user to search manually.
• If the user only wants links, provide the source URLs above and let the user search themselves.

B站素材酷 special handling

B站素材酷 (cool.bilibili.com) has no built-in search function. Two options:

1. Recommend the userscript: bcut-resource-search — a Tampermonkey script that adds search to 素材酷, supporting video, sticker/image, music, sound effect, and template categories.
2. Search via API directly: the agent can read the userscript's source code to discover the API endpoints and make requests directly (via HTTP tools or curl). Note: the site has CORS restrictions and UA-based access control, so direct API calls may require appropriate headers. If the agent's HTTP tools are blocked, fall back to suggesting the userscript to the user.

Anti-bot fallback

Some material sites may block automated access (anti-bot, Cloudflare, CAPTCHA). If a web request or HTTP tool is blocked:

1. Try with different headers (User-Agent, Accept-Language) if the tool allows.
2. Fall back to a browser-based tool (e.g., Playwright, Puppeteer, or any browser automation the agent's runtime provides) to access the content.
3. If browser tools are also blocked, inform the user and provide the direct URL for manual search.

Long-running operations

Before any operation expected to take >1 minute (transcription of long videos, large frame extraction):

1. Do lightweight setup directly when possible: dependency checks, copying scripts, preparing a Windows-local transcription directory, and running uv sync.
2. Ask for consent before installing missing system dependencies (ffmpeg, uv, Python), then install them if the runtime can do so.
3. Hand off only genuinely long-running media operations to the user. Generate the command and give it to the user to execute manually.
4. Match command syntax to the chosen execution environment: PowerShell commands for Windows execution; Linux shell commands for WSL/Linux execution.
5. After the user reports completion, the agent should verify the output exists before proceeding.

Self-contained setup

The skill bundles its own transcription project under scripts/transcription/. On first use, the agent should prepare the environment before handing off the transcription command.

For WSL/Linux execution, run setup in the bundled transcription directory:

cd SKILL_DIR/scripts/transcription && uv sync

For Windows execution, ensure the transcription project is in a Windows-local directory before running setup. If the agent itself is running in WSL, it can use powershell.exe to run host commands and should do this setup itself when possible:

# Example Windows-local setup directory
$TransDir = "$env:LOCALAPPDATA\\Temp\\vedit-transcription"
New-Item -ItemType Directory -Force -Path $TransDir | Out-Null
# Copy or clone scripts/transcription into $TransDir before this step.
Set-Location $TransDir
uv sync

This creates an isolated venv with funasr, torch, torchaudio. If the user has previously installed these packages via uv in other projects, uv's cache will reuse them — first-time cost is often only the link step. Do not ask the user to manually copy files or run uv sync unless the agent cannot access the host command line or lacks permission.

Common pitfalls

1. Installing dependencies without consent. Always ask the user first. If they decline, stop — the skill cannot proceed without ffmpeg, uv, and python3.
2. Re-processing already-transcribed videos. Always check the index file first. If a transcription record exists and the video hasn't been modified, reuse it.
3. Extracting frames from the entire video. This is extremely slow for long videos. Always clip the target range first with -c copy, then extract frames from the clip.
4. Using full-resolution frames. A 2560×1600 PNG can cost 2000+ vision tokens. Downsample to 1280 width with scale=1280:-2 and use JPEG (-q:v 3).
5. Forgetting to record artifacts in the index. Every clip and frame batch must be recorded. Otherwise subsequent turns will re-extract the same content.
6. Assuming the transcript is sufficient. Gameplay videos often have long stretches of action with no dialogue. The agent should proactively check whether visual analysis is needed for segments the user asks about.
7. Mixing funasr-script flags. The bundled funasr-nano/funasr-fast use --track STREAM_INDEX and --output-dir DIR. Do not use the legacy ~/.local/bin/funasr-transcribe wrapper's flags.
8. Hardcoding tool names. This skill is agent-agnostic. Do not assume specific MCP tools or APIs exist. Use whatever the runtime provides for vision analysis, background execution, and user notification.
9. Hardcoding output language. The edit plan table and all user-facing text must follow the user's language, not a fixed language. The English table in Step 8 is a template — translate columns and content to match the user's language at runtime.
10. Dropping audio tracks during clip extraction. The default clip command maps only 0:v:0 (video-only) for fast frame extraction. If the user plans to import the clip into a video editor, use --all-streams (or -map 0) to preserve all audio tracks. Always ask the user which they need.
11. Using ~/whisperx_output/ or other separate output directories. All artifacts (transcription JSON, clips, frames, index file) must go in the same directory as the video file. The --output-dir flag should always point to the video's own directory. This keeps everything co-located and portable.
12. Choosing command syntax or execution environment solely from where the agent runs. Choose based on where the video file lives and where the command will execute. Windows execution → PowerShell commands and Windows paths; WSL/Linux execution → Linux shell commands and Linux paths. If the agent runs in WSL but Windows execution is chosen, use powershell.exe to do lightweight setup (copy files, install/check dependencies, run uv sync) before handing only the long transcription command to the user. Never ask Windows uv to run from \\\\wsl.localhost\\... / \\\\wsl$\\....
13. Saying you'll provide a command but not including it. If you mention giving the user a transcription command, include it immediately in that same response. Do not move on to other content without printing the actual command. The user should never have to ask for it.
14. Sending a wall of free-text questions. When gathering editing requirements, use structured one-at-a-time multiple-choice questions via the runtime's clarify/prompt tool. This is far more effective than dumping 10 questions as a paragraph.

Verification checklist

Check only the items relevant to the steps actually executed. Not all items apply to every session — the user may only need a subset of the skill's capabilities.

• Dependencies (ffmpeg, uv, python3) confirmed present or user declined. (Step 1 — always required)
• Video path and audio track index(es) confirmed. (Step 2 — required when video processing is needed)
• User's editing goal understood. (Step 3 — skip if not relevant to the user's request)
• Transcription completed for all requested tracks, or reused from cache. (Step 4)
• Index file initialized at <video_dir>/<video_stem>.vedit.json. (Step 5 — always required when any processing is done)
• All extracted clips and frames recorded in the index. (Step 6 — only if frames were extracted)
• Vision analysis performed where needed. (Step 7 — only if frames were extracted)
• Edit plan produced as Markdown table with overall recommendation. (Step 8 — skip if not relevant to the user's request)
• User invited to iterate. (Step 9)