videodb — for Claude Code videodb, everything-claude-code, official, for Claude Code, ide skills, video analysis, audio transcription, media editing, RTSP live feeds, transcoding, Claude Code

Verified
v1.0.0
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About this Skill

Perfect for Media Analysis Agents needing advanced video and audio processing capabilities. videodb is a media analysis AI agent skill that enables ingestion, transcription, and editing of video and audio content

Features

Ingest video and audio from local files, URLs, or live feeds using RTSP
Transcode and normalize media using codec, fps, resolution, and aspect ratio
Extract frames and build visual, semantic, and temporal indexes for search
Perform timeline edits with subtitles, text/image overlays, and audio overlays
Generate media assets, such as images, audio, and video clips

# Core Topics

affaan-m affaan-m
[116.8k]
[15188]
Updated: 3/30/2026

Agent Capability Analysis

The videodb skill by affaan-m is an open-source official AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for for Claude Code, video analysis, audio transcription.

Ideal Agent Persona

Perfect for Media Analysis Agents needing advanced video and audio processing capabilities.

Core Value

Empowers agents to ingest, analyze, and generate insights from video and audio files, URLs, and live streams using RTSP, with capabilities including real-time context extraction, timeline editing, and media asset generation, all leveraging Python and the videodb library.

Capabilities Granted for videodb

Automating video content analysis for object detection and tracking
Generating searchable timelines and playable evidence links for video and audio files
Debugging live stream issues with real-time alerts and monitoring workflows

! Prerequisites & Limits

  • Requires VIDEO_DB_API_KEY for authentication
  • Python environment necessary for execution
  • Dependent on videodb library for functionality
Labs Demo

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videodb

Unlock video and audio insights with VideoDB, an AI agent skill for Claude Code. Analyze, transcode, and edit media with ease, and discover how it benefits...

SKILL.md
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VideoDB Skill

Perception + memory + actions for video, live streams, and desktop sessions.

When to use

Desktop Perception

  • Start/stop a desktop session capturing screen, mic, and system audio
  • Stream live context and store episodic session memory
  • Run real-time alerts/triggers on what's spoken and what's happening on screen
  • Produce session summaries, a searchable timeline, and playable evidence links

Video ingest + stream

  • Ingest a file or URL and return a playable web stream link
  • Transcode/normalize: codec, bitrate, fps, resolution, aspect ratio

Index + search (timestamps + evidence)

  • Build visual, spoken, and keyword indexes
  • Search and return exact moments with timestamps and playable evidence
  • Auto-create clips from search results

Timeline editing + generation

  • Subtitles: generate, translate, burn-in
  • Overlays: text/image/branding, motion captions
  • Audio: background music, voiceover, dubbing
  • Programmatic composition and exports via timeline operations

Live streams (RTSP) + monitoring

  • Connect RTSP/live feeds
  • Run real-time visual and spoken understanding and emit events/alerts for monitoring workflows

How it works

Common inputs

  • Local file path, public URL, or RTSP URL
  • Desktop capture request: start / stop / summarize session
  • Desired operations: get context for understanding, transcode spec, index spec, search query, clip ranges, timeline edits, alert rules

Common outputs

  • Stream URL
  • Search results with timestamps and evidence links
  • Generated assets: subtitles, audio, images, clips
  • Event/alert payloads for live streams
  • Desktop session summaries and memory entries

Running Python code

Before running any VideoDB code, change to the project directory and load environment variables:

python
1from dotenv import load_dotenv 2load_dotenv(".env") 3 4import videodb 5conn = videodb.connect()

This reads VIDEO_DB_API_KEY from:

  1. Environment (if already exported)
  2. Project's .env file in current directory

If the key is missing, videodb.connect() raises AuthenticationError automatically.

Do NOT write a script file when a short inline command works.

When writing inline Python (python -c "..."), always use properly formatted code — use semicolons to separate statements and keep it readable. For anything longer than ~3 statements, use a heredoc instead:

bash
1python << 'EOF' 2from dotenv import load_dotenv 3load_dotenv(".env") 4 5import videodb 6conn = videodb.connect() 7coll = conn.get_collection() 8print(f"Videos: {len(coll.get_videos())}") 9EOF

Setup

When the user asks to "setup videodb" or similar:

1. Install SDK

bash
1pip install "videodb[capture]" python-dotenv

If videodb[capture] fails on Linux, install without the capture extra:

bash
1pip install videodb python-dotenv

2. Configure API key

The user must set VIDEO_DB_API_KEY using either method:

  • Export in terminal (before starting Claude): export VIDEO_DB_API_KEY=your-key
  • Project .env file: Save VIDEO_DB_API_KEY=your-key in the project's .env file

Get a free API key at console.videodb.io (50 free uploads, no credit card).

Do NOT read, write, or handle the API key yourself. Always let the user set it.

Quick Reference

Upload media

python
1# URL 2video = coll.upload(url="https://example.com/video.mp4") 3 4# YouTube 5video = coll.upload(url="https://www.youtube.com/watch?v=VIDEO_ID") 6 7# Local file 8video = coll.upload(file_path="/path/to/video.mp4")

Transcript + subtitle

python
1# force=True skips the error if the video is already indexed 2video.index_spoken_words(force=True) 3text = video.get_transcript_text() 4stream_url = video.add_subtitle()

Search inside videos

python
1from videodb.exceptions import InvalidRequestError 2 3video.index_spoken_words(force=True) 4 5# search() raises InvalidRequestError when no results are found. 6# Always wrap in try/except and treat "No results found" as empty. 7try: 8 results = video.search("product demo") 9 shots = results.get_shots() 10 stream_url = results.compile() 11except InvalidRequestError as e: 12 if "No results found" in str(e): 13 shots = [] 14 else: 15 raise
python
1import re 2from videodb import SearchType, IndexType, SceneExtractionType 3from videodb.exceptions import InvalidRequestError 4 5# index_scenes() has no force parameter — it raises an error if a scene 6# index already exists. Extract the existing index ID from the error. 7try: 8 scene_index_id = video.index_scenes( 9 extraction_type=SceneExtractionType.shot_based, 10 prompt="Describe the visual content in this scene.", 11 ) 12except Exception as e: 13 match = re.search(r"id\s+([a-f0-9]+)", str(e)) 14 if match: 15 scene_index_id = match.group(1) 16 else: 17 raise 18 19# Use score_threshold to filter low-relevance noise (recommended: 0.3+) 20try: 21 results = video.search( 22 query="person writing on a whiteboard", 23 search_type=SearchType.semantic, 24 index_type=IndexType.scene, 25 scene_index_id=scene_index_id, 26 score_threshold=0.3, 27 ) 28 shots = results.get_shots() 29 stream_url = results.compile() 30except InvalidRequestError as e: 31 if "No results found" in str(e): 32 shots = [] 33 else: 34 raise

Timeline editing

Important: Always validate timestamps before building a timeline:

  • start must be >= 0 (negative values are silently accepted but produce broken output)
  • start must be < end
  • end must be <= video.length
python
1from videodb.timeline import Timeline 2from videodb.asset import VideoAsset, TextAsset, TextStyle 3 4timeline = Timeline(conn) 5timeline.add_inline(VideoAsset(asset_id=video.id, start=10, end=30)) 6timeline.add_overlay(0, TextAsset(text="The End", duration=3, style=TextStyle(fontsize=36))) 7stream_url = timeline.generate_stream()

Transcode video (resolution / quality change)

python
1from videodb import TranscodeMode, VideoConfig, AudioConfig 2 3# Change resolution, quality, or aspect ratio server-side 4job_id = conn.transcode( 5 source="https://example.com/video.mp4", 6 callback_url="https://example.com/webhook", 7 mode=TranscodeMode.economy, 8 video_config=VideoConfig(resolution=720, quality=23, aspect_ratio="16:9"), 9 audio_config=AudioConfig(mute=False), 10)

Reframe aspect ratio (for social platforms)

Warning: reframe() is a slow server-side operation. For long videos it can take several minutes and may time out. Best practices:

  • Always limit to a short segment using start/end when possible
  • For full-length videos, use callback_url for async processing
  • Trim the video on a Timeline first, then reframe the shorter result
python
1from videodb import ReframeMode 2 3# Always prefer reframing a short segment: 4reframed = video.reframe(start=0, end=60, target="vertical", mode=ReframeMode.smart) 5 6# Async reframe for full-length videos (returns None, result via webhook): 7video.reframe(target="vertical", callback_url="https://example.com/webhook") 8 9# Presets: "vertical" (9:16), "square" (1:1), "landscape" (16:9) 10reframed = video.reframe(start=0, end=60, target="square") 11 12# Custom dimensions 13reframed = video.reframe(start=0, end=60, target={"width": 1280, "height": 720})

Generative media

python
1image = coll.generate_image( 2 prompt="a sunset over mountains", 3 aspect_ratio="16:9", 4)

Error handling

python
1from videodb.exceptions import AuthenticationError, InvalidRequestError 2 3try: 4 conn = videodb.connect() 5except AuthenticationError: 6 print("Check your VIDEO_DB_API_KEY") 7 8try: 9 video = coll.upload(url="https://example.com/video.mp4") 10except InvalidRequestError as e: 11 print(f"Upload failed: {e}")

Common pitfalls

ScenarioError messageSolution
Indexing an already-indexed videoSpoken word index for video already existsUse video.index_spoken_words(force=True) to skip if already indexed
Scene index already existsScene index with id XXXX already existsExtract the existing scene_index_id from the error with re.search(r"id\s+([a-f0-9]+)", str(e))
Search finds no matchesInvalidRequestError: No results foundCatch the exception and treat as empty results (shots = [])
Reframe times outBlocks indefinitely on long videosUse start/end to limit segment, or pass callback_url for async
Negative timestamps on TimelineSilently produces broken streamAlways validate start >= 0 before creating VideoAsset
generate_video() / create_collection() failsOperation not allowed or maximum limitPlan-gated features — inform the user about plan limits

Examples

Canonical prompts

  • "Start desktop capture and alert when a password field appears."
  • "Record my session and produce an actionable summary when it ends."
  • "Ingest this file and return a playable stream link."
  • "Index this folder and find every scene with people, return timestamps."
  • "Generate subtitles, burn them in, and add light background music."
  • "Connect this RTSP URL and alert when a person enters the zone."

Screen Recording (Desktop Capture)

Use ws_listener.py to capture WebSocket events during recording sessions. Desktop capture supports macOS only.

Quick Start

  1. Choose state dir: STATE_DIR="${VIDEODB_EVENTS_DIR:-$HOME/.local/state/videodb}"
  2. Start listener: VIDEODB_EVENTS_DIR="$STATE_DIR" python scripts/ws_listener.py --clear "$STATE_DIR" &
  3. Get WebSocket ID: cat "$STATE_DIR/videodb_ws_id"
  4. Run capture code (see reference/capture.md for the full workflow)
  5. Events written to: $STATE_DIR/videodb_events.jsonl

Use --clear whenever you start a fresh capture run so stale transcript and visual events do not leak into the new session.

Query Events

python
1import json 2import os 3import time 4from pathlib import Path 5 6events_dir = Path(os.environ.get("VIDEODB_EVENTS_DIR", Path.home() / ".local" / "state" / "videodb")) 7events_file = events_dir / "videodb_events.jsonl" 8events = [] 9 10if events_file.exists(): 11 with events_file.open(encoding="utf-8") as handle: 12 for line in handle: 13 try: 14 events.append(json.loads(line)) 15 except json.JSONDecodeError: 16 continue 17 18transcripts = [e["data"]["text"] for e in events if e.get("channel") == "transcript"] 19cutoff = time.time() - 300 20recent_visual = [ 21 e for e in events 22 if e.get("channel") == "visual_index" and e["unix_ts"] > cutoff 23]

Additional docs

Reference documentation is in the reference/ directory adjacent to this SKILL.md file. Use the Glob tool to locate it if needed.

Do not use ffmpeg, moviepy, or local encoding tools when VideoDB supports the operation. The following are all handled server-side by VideoDB — trimming, combining clips, overlaying audio or music, adding subtitles, text/image overlays, transcoding, resolution changes, aspect-ratio conversion, resizing for platform requirements, transcription, and media generation. Only fall back to local tools for operations listed under Limitations in reference/editor.md (transitions, speed changes, crop/zoom, colour grading, volume mixing).

When to use what

ProblemVideoDB solution
Platform rejects video aspect ratio or resolutionvideo.reframe() or conn.transcode() with VideoConfig
Need to resize video for Twitter/Instagram/TikTokvideo.reframe(target="vertical") or target="square"
Need to change resolution (e.g. 1080p → 720p)conn.transcode() with VideoConfig(resolution=720)
Need to overlay audio/music on videoAudioAsset on a Timeline
Need to add subtitlesvideo.add_subtitle() or CaptionAsset
Need to combine/trim clipsVideoAsset on a Timeline
Need to generate voiceover, music, or SFXcoll.generate_voice(), generate_music(), generate_sound_effect()

Provenance

Reference material for this skill is vendored locally under skills/videodb/reference/. Use the local copies above instead of following external repository links at runtime.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is videodb?

Perfect for Media Analysis Agents needing advanced video and audio processing capabilities. videodb is a media analysis AI agent skill that enables ingestion, transcription, and editing of video and audio content

How do I install videodb?

Run the command: npx killer-skills add affaan-m/everything-claude-code/videodb. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for videodb?

Key use cases include: Automating video content analysis for object detection and tracking, Generating searchable timelines and playable evidence links for video and audio files, Debugging live stream issues with real-time alerts and monitoring workflows.

Which IDEs are compatible with videodb?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for videodb?

Requires VIDEO_DB_API_KEY for authentication. Python environment necessary for execution. Dependent on videodb library for functionality.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add affaan-m/everything-claude-code/videodb. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use videodb immediately in the current project.

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