videodb

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:

from dotenv import load_dotenv
load_dotenv(".env")

import videodb
conn = 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:

python << 'EOF'
from dotenv import load_dotenv
load_dotenv(".env")

import videodb
conn = videodb.connect()
coll = conn.get_collection()
print(f"Videos: {len(coll.get_videos())}")
EOF

Setup

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

1. Install SDK

pip install "videodb[capture]" python-dotenv

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

pip 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

# URL
video = coll.upload(url="https://example.com/video.mp4")

# YouTube
video = coll.upload(url="https://www.youtube.com/watch?v=VIDEO_ID")

# Local file
video = coll.upload(file_path="/path/to/video.mp4")

Transcript + subtitle

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

Search inside videos

from videodb.exceptions import InvalidRequestError

video.index_spoken_words(force=True)

# search() raises InvalidRequestError when no results are found.
# Always wrap in try/except and treat "No results found" as empty.
try:
    results = video.search("product demo")
    shots = results.get_shots()
    stream_url = results.compile()
except InvalidRequestError as e:
    if "No results found" in str(e):
        shots = []
    else:
        raise

Scene search

import re
from videodb import SearchType, IndexType, SceneExtractionType
from videodb.exceptions import InvalidRequestError

# index_scenes() has no force parameter — it raises an error if a scene
# index already exists. Extract the existing index ID from the error.
try:
    scene_index_id = video.index_scenes(
        extraction_type=SceneExtractionType.shot_based,
        prompt="Describe the visual content in this scene.",
    )
except Exception as e:
    match = re.search(r"id\s+([a-f0-9]+)", str(e))
    if match:
        scene_index_id = match.group(1)
    else:
        raise

# Use score_threshold to filter low-relevance noise (recommended: 0.3+)
try:
    results = video.search(
        query="person writing on a whiteboard",
        search_type=SearchType.semantic,
        index_type=IndexType.scene,
        scene_index_id=scene_index_id,
        score_threshold=0.3,
    )
    shots = results.get_shots()
    stream_url = results.compile()
except InvalidRequestError as e:
    if "No results found" in str(e):
        shots = []
    else:
        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

from videodb.timeline import Timeline
from videodb.asset import VideoAsset, TextAsset, TextStyle

timeline = Timeline(conn)
timeline.add_inline(VideoAsset(asset_id=video.id, start=10, end=30))
timeline.add_overlay(0, TextAsset(text="The End", duration=3, style=TextStyle(fontsize=36)))
stream_url = timeline.generate_stream()

Transcode video (resolution / quality change)

from videodb import TranscodeMode, VideoConfig, AudioConfig

# Change resolution, quality, or aspect ratio server-side
job_id = conn.transcode(
    source="https://example.com/video.mp4",
    callback_url="https://example.com/webhook",
    mode=TranscodeMode.economy,
    video_config=VideoConfig(resolution=720, quality=23, aspect_ratio="16:9"),
    audio_config=AudioConfig(mute=False),
)

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

from videodb import ReframeMode

# Always prefer reframing a short segment:
reframed = video.reframe(start=0, end=60, target="vertical", mode=ReframeMode.smart)

# Async reframe for full-length videos (returns None, result via webhook):
video.reframe(target="vertical", callback_url="https://example.com/webhook")

# Presets: "vertical" (9:16), "square" (1:1), "landscape" (16:9)
reframed = video.reframe(start=0, end=60, target="square")

# Custom dimensions
reframed = video.reframe(start=0, end=60, target={"width": 1280, "height": 720})

Generative media

image = coll.generate_image(
    prompt="a sunset over mountains",
    aspect_ratio="16:9",
)

Error handling

from videodb.exceptions import AuthenticationError, InvalidRequestError

try:
    conn = videodb.connect()
except AuthenticationError:
    print("Check your VIDEO_DB_API_KEY")

try:
    video = coll.upload(url="https://example.com/video.mp4")
except InvalidRequestError as e:
    print(f"Upload failed: {e}")

Common pitfalls

| Scenario | Error message | Solution |

|----------|--------------|----------|

| Indexing an already-indexed video | Spoken word index for video already exists | Use video.index_spoken_words(force=True) to skip if already indexed |

| Scene index already exists | Scene index with id XXXX already exists | Extract the existing scene_index_id from the error with re.search(r"id\s+([a-f0-9]+)", str(e)) |

| Search finds no matches | InvalidRequestError: No results found | Catch the exception and treat as empty results (shots = []) |

| Reframe times out | Blocks indefinitely on long videos | Use start/end to limit segment, or pass callback_url for async |

| Negative timestamps on Timeline | Silently produces broken stream | Always validate start >= 0 before creating VideoAsset |

| generate_video() / create_collection() fails | Operation not allowed or maximum limit | Plan-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

import json
import os
import time
from pathlib import Path

events_dir = Path(os.environ.get("VIDEODB_EVENTS_DIR", Path.home() / ".local" / "state" / "videodb"))
events_file = events_dir / "videodb_events.jsonl"
events = []

if events_file.exists():
    with events_file.open(encoding="utf-8") as handle:
        for line in handle:
            try:
                events.append(json.loads(line))
            except json.JSONDecodeError:
                continue

transcripts = [e["data"]["text"] for e in events if e.get("channel") == "transcript"]
cutoff = time.time() - 300
recent_visual = [
    e for e in events
    if e.get("channel") == "visual_index" and e["unix_ts"] > cutoff
]

Additional docs

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

• reference/api-reference.md ↗ - Complete VideoDB Python SDK API reference
• reference/search.md ↗ - In-depth guide to video search (spoken word and scene-based)
• reference/editor.md ↗ - Timeline editing, assets, and composition
• reference/streaming.md ↗ - HLS streaming and instant playback
• reference/generative.md ↗ - AI-powered media generation (images, video, audio)
• reference/rtstream.md ↗ - Live stream ingestion workflow (RTSP/RTMP)
• reference/rtstream-reference.md ↗ - RTStream SDK methods and AI pipelines
• reference/capture.md ↗ - Desktop capture workflow
• reference/capture-reference.md ↗ - Capture SDK and WebSocket events
• reference/use-cases.md ↗ - Common video processing patterns and examples

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

| Problem | VideoDB solution |

|---------|-----------------|

| Platform rejects video aspect ratio or resolution | video.reframe() or conn.transcode() with VideoConfig |

| Need to resize video for Twitter/Instagram/TikTok | video.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 video | AudioAsset on a Timeline |

| Need to add subtitles | video.add_subtitle() or CaptionAsset |

| Need to combine/trim clips | VideoAsset on a Timeline |

| Need to generate voiceover, music, or SFX | coll.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.