Results for “channel-metadata”

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Multi-format channel name parsing for KINTSUGI CHANNELNAMES.txt files

Extracts structured channel data from YouTube search results via the BrowserAct API, including subscriber counts, verification status, and channel descriptions.

Demystifying CLIP Data

Search YouTube videos, get channel info, fetch video details and transcripts using YouTube Data API v3 via MCP server or yt-dlp fallback.

Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.

Extracts structured channel-level and video detail data from a YouTube channel via the BrowserAct API, including metrics like views, likes, comments, and subscriber count.

Use when running QA Lab channel message flow evidence.

Use OpenMetadata SDK and APIs to build integrations, connectors, and automations. Use when querying metadata, creating custom properties, building ingestion pipelines, automating governance workflows, or integrating OpenMetadata with other systems.

Extracts YouTube transcripts, searches videos and channels, browses playlists, and monitors channel uploads via the TranscriptAPI service.

Extract structured video metrics and channel information from YouTube by keyword search using the BrowserAct API, with no CAPTCHA or IP restrictions.

Use OpenMetadata UI for data discovery, governance, and collaboration. Use when searching for data assets, managing descriptions and ownership, creating glossary terms, setting up data contracts, or tracking data insights and KPIs.

Extracts complete metadata from a TikTok video page by reading SSR-embedded data, including author profile, engagement stats, music info, hashtags, and slideshow images.

Extracts structured data from YouTube search results, including videos, shorts, channels, and playlists, using the BrowserAct API.

Configure and manage data quality tests, profiling, alerts, and incidents in OpenMetadata. Use when setting up quality tests, configuring profiler workflows, creating observability alerts, or triaging data quality incidents.

Streams change data capture from Google Cloud Spanner into Redpanda or Kafka using Redpanda Connect's gcp_spanner_cdc input, with partition-aware watermarked delivery and support for Redpanda Enterprise features.

YouTube SERP Scout for agents. Search top-ranking videos, channels, and trends for content research and competitor tracking. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

Turns strategy-first Madden NFL YouTube videos into structured, game-ready notes covering meta snapshot, formations, plays, adjustments, and provenance, using transcripts and a curated source list.

Generates a sequence of fullscreen video frames with cinematic visuals, auto-play, and metadata for Remotion/Hyperframes rendering.

Extracts selected metadata from a DICOM file and flags standard-tag PHI presence. Not for anonymization or clinical use.

Resolves, generates, and operates on media assets (audio, images, icons, logos, voice, color grades, LUTs) for HyperFrames projects, using a local cache and the HeyGen CLI for free-usage catalog search and TTS.

Encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, and build multi-step media processing pipelines using Transloadit's cloud infrastructure.

Build video analytics pipelines using NVIDIA DeepStream SDK 9.0 with Python pyservicemaker API, including GStreamer-based video processing, TensorRT inference integration, object detection/tracking, and Kafka/message broker integration.

Compute fully-loaded cost-to-serve per channel, channel ROI under cash/LTV/marginal lenses, and optimal channel mix subject to strategic constraints for quarterly channel reviews.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Extracts YouTube video transcripts and metadata in batch via the BrowserAct API, using search keywords and date filters.

Search YouTube SERP results through AISA for video research, channel discovery, trend checking, and competitor tracking. Use when: you need ranked YouTube results for a query, optionally filtered by country or language.

Convert single-channel TIF directories to pyramidal OME-TIFF with SubIFDs

Meta-Analysis Master with Data Integrity, Effect Size, Error Prevention & Sensitivity Multi-gate validation and workflow orchestration for meta-analysis. Absorbed C6 (Data Integrity Guard), C7 (Error Prevention Engine), B3 (Effect Size Extractor), E5 (Sensitivity Analysis - Meta) capabilities Triggers: meta-analysis, pooled effect, heterogeneity, forest plot, funnel plot, Hedges g, data integrity, effect size extraction, sensitivity analysis

Kinetics-400: A Large Video Understanding Dataset