See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
metadata
{"origin":"ECC"}
allowed-tools
Read Grep Glob Bash(python:*)
argument-hint
[task description]
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
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 enprojectnment variables:
from dotenv import load_dotenv
load_dotenv(".env")
import videodb
conn = videodb.connect()
This reads VIDEO_DB_API_KEY from:
Enprojectnment (if already exported)
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:
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"instr(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))
ifmatch:
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"instr(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)
Run capture code (see reference/capture.md for the full workflow)
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.enprojectn.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.
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"
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.
