{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-samuraigpt--ai-youtube-shorts-generator","slug":"samuraigpt--ai-youtube-shorts-generator","name":"AI-Youtube-Shorts-Generator","type":"cli","url":"https://www.vadoo.tv/ai-youtube-shorts-generator","page_url":"https://unfragile.ai/samuraigpt--ai-youtube-shorts-generator","categories":["video-generation","data-pipelines"],"tags":["ai-video-generator","artificial-intelligence","image-to-video","image-to-video-generation","shorts","shorts-maker","sora-video","sora-video-ai","stable-diffusion","text-to-image","text-to-video","text-to-video-generation","video-diffusion","video-editing","video-generation","video-generator","youtube-shorts"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_0","uri":"capability://automation.workflow.youtube.video.download.and.local.caching","name":"youtube video download and local caching","description":"Automatically downloads full-length YouTube videos using yt-dlp or similar library, storing them locally for subsequent processing. Handles authentication, format selection, and metadata extraction in a single operation, enabling offline processing without repeated network calls. The YoutubeDownloader component manages the download lifecycle and integrates with the transcription pipeline.","intents":["I want to process a YouTube video without manually downloading it first","I need to extract and cache video content for batch processing multiple URLs","I want to preserve video metadata (title, duration, upload date) during download"],"best_for":["content creators automating shorts generation from their own YouTube channels","teams building batch video processing pipelines","developers prototyping video analysis workflows"],"limitations":["No support for age-restricted or private videos without authentication","Download speed limited by network bandwidth and YouTube rate limiting","Requires sufficient local disk space for full video storage (1-10GB+ for long-form content)","No built-in resume capability for interrupted downloads"],"requires":["Python 3.7+","yt-dlp or youtube-dl library","FFmpeg installed and in system PATH","Sufficient disk space (minimum 5GB recommended)","Network connectivity to YouTube"],"input_types":["YouTube URL (string)","Video ID (string)"],"output_types":["MP4 video file (local path)","Video metadata (JSON: title, duration, upload_date, channel)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_1","uri":"capability://data.processing.analysis.speech.to.text.transcription.with.timestamp.alignment","name":"speech-to-text transcription with timestamp alignment","description":"Converts video audio to text using OpenAI's Whisper model, generating word-level timestamps that map each transcribed segment back to specific video frames. The transcription output includes confidence scores and speaker diarization hints, enabling precise temporal mapping for highlight detection. Handles multiple audio formats and automatically extracts audio from video containers using FFmpeg.","intents":["I need accurate transcripts with exact timestamps for identifying where interesting moments occur in the video","I want to match GPT-4 analysis back to specific video segments for cropping","I need to handle videos with background noise or multiple speakers"],"best_for":["content creators with educational or interview-style videos","teams processing podcasts or webinars into shorts","developers building timestamp-aware video analysis systems"],"limitations":["Whisper accuracy varies by audio quality; background noise reduces precision to 70-85% word error rate","Timestamp alignment has ±500ms margin of error at segment boundaries","No native speaker diarization; requires post-processing to distinguish multiple speakers","Processing time is ~1/6 of video duration (10-minute video takes ~100 seconds)"],"requires":["Python 3.7+","OpenAI API key with Whisper access","FFmpeg for audio extraction","openai Python package (version 0.27+)","Audio file or video with audio track"],"input_types":["MP4/MOV/WebM video file","WAV/MP3/AAC audio file","Video file path (string)"],"output_types":["Transcription text (string)","Timestamped segments (JSON: [{text, start_time, end_time, confidence}, ...])","Full transcript with word-level timing"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_2","uri":"capability://planning.reasoning.gpt.4.powered.highlight.detection.and.segment.ranking","name":"gpt-4 powered highlight detection and segment ranking","description":"Analyzes full video transcripts using GPT-4 to identify the most engaging, shareable segments based on content relevance, emotional impact, and audience appeal. The system sends the complete transcript to GPT-4 with a structured prompt requesting segment timestamps and engagement scores, then ranks results by predicted virality. This enables semantic understanding of content quality rather than simple keyword matching or silence detection.","intents":["I want AI to identify which parts of my video are most interesting to viewers","I need to automatically rank multiple potential shorts by engagement potential","I want to extract highlights that match specific themes or topics from long-form content"],"best_for":["content creators with diverse video topics (education, entertainment, news)","teams managing large video libraries needing automated curation","developers building content recommendation systems"],"limitations":["GPT-4 API costs scale with transcript length (~$0.03-0.10 per video depending on length)","Highlight quality depends on prompt engineering; generic prompts produce generic results","No context about target audience; cannot optimize for specific demographics","Latency of 5-15 seconds per API call; not suitable for real-time processing","Token limit of 8,000 tokens may truncate very long transcripts (>2 hours)"],"requires":["Python 3.7+","OpenAI API key with GPT-4 access","openai Python package (version 0.27+)","Full video transcript (text)","Structured prompt template"],"input_types":["Full transcript (string, max 8000 tokens)","Prompt template (string with placeholders)","Optional: target audience context (string)"],"output_types":["Highlighted segments (JSON: [{start_time, end_time, reason, engagement_score}, ...])","Ranked list of top N segments","Engagement analysis (structured text)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_3","uri":"capability://image.visual.face.detection.and.speaker.tracking.across.video.frames","name":"face detection and speaker tracking across video frames","description":"Detects human faces in video frames using OpenCV with pre-trained Haar Cascade or DNN-based face detection models, then tracks face position and size across consecutive frames to maintain speaker focus during cropping. The system builds a spatial map of face locations throughout the video, enabling intelligent cropping that keeps speakers centered in the 9:16 vertical frame. Handles multiple faces and tracks the primary speaker based on face size and screen time.","intents":["I want to automatically keep the speaker centered when cropping to vertical format","I need to track where people are positioned in each frame to avoid cutting off heads","I want to handle videos with multiple speakers and focus on the primary one"],"best_for":["content creators with interview, podcast, or presentation-style videos","teams automating vertical video production at scale","developers building smart video cropping systems"],"limitations":["Face detection fails on extreme angles (>60 degrees), low light (<50 lux), or small faces (<50 pixels)","Haar Cascade detection has ~5-10% false positive rate; DNN models are more accurate but 3-5x slower","No face recognition; cannot distinguish between different speakers across scenes","Tracking breaks on scene cuts or when speaker exits frame; requires re-initialization","Processing adds ~100-200ms per frame (30fps video = 3-6 seconds overhead per minute)"],"requires":["Python 3.7+","OpenCV 4.5+","Pre-trained face detection model (Haar Cascade or DNN weights)","Video file with visible faces","Sufficient CPU/GPU for real-time frame processing"],"input_types":["MP4/MOV video file","Video frame sequence (array of images)","Face detection model path (string)"],"output_types":["Face bounding boxes per frame (JSON: [{frame_id, x, y, width, height, confidence}, ...])","Face tracking trajectory (spatial coordinates over time)","Primary speaker identification (frame ranges)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_4","uri":"capability://image.visual.intelligent.vertical.format.cropping.with.speaker.aware.framing","name":"intelligent vertical format cropping with speaker-aware framing","description":"Crops video segments from 16:9 (or other aspect ratios) to 9:16 vertical format while keeping detected speakers centered and in-frame. The system uses the face tracking data to calculate optimal crop windows that maximize speaker visibility while minimizing empty space. Applies smooth pan/zoom transitions between crop windows to avoid jarring frame shifts, and handles edge cases where speakers move outside the vertical frame boundary.","intents":["I want to automatically convert landscape video to vertical format without cutting off speakers","I need smooth transitions when the speaker moves across the frame","I want to maximize the speaker's screen presence in the vertical frame"],"best_for":["content creators optimizing videos for YouTube Shorts, TikTok, Instagram Reels","teams automating vertical video production from landscape source material","developers building smart video editing pipelines"],"limitations":["Cropping quality degrades if speaker moves rapidly (>100 pixels per frame); may require slower playback","Pan/zoom transitions add processing time (~50-100ms per transition)","Cannot recover content lost in original 16:9 framing; limited to what's already in frame","Aspect ratio conversion may create black bars if source is narrower than 9:16","Requires accurate face detection; fails gracefully to center crop if faces not detected"],"requires":["Python 3.7+","OpenCV 4.5+","FFmpeg for video encoding","Face tracking data (from face detection capability)","Source video file"],"input_types":["Source video file (MP4/MOV)","Face tracking coordinates (JSON)","Segment timestamps (start_time, end_time)","Target aspect ratio (default 9:16)"],"output_types":["Cropped video file (MP4, 9:16 aspect ratio)","Crop window trajectory (JSON: [{frame_id, crop_x, crop_y, crop_width, crop_height}, ...])","Transition metadata (pan/zoom parameters)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_5","uri":"capability://automation.workflow.multi.segment.video.composition.and.concatenation","name":"multi-segment video composition and concatenation","description":"Combines multiple cropped video segments into a single output file, handling transitions, audio synchronization, and metadata preservation. The system uses FFmpeg's concat demuxer to join segments without re-encoding (when possible), applies fade transitions between clips, and ensures audio remains synchronized throughout. Supports adding intro/outro sequences, watermarks, and metadata tags for platform-specific optimization.","intents":["I want to combine multiple highlighted segments into a single short video","I need to add transitions and effects between segments","I want to preserve audio quality while joining video clips"],"best_for":["content creators producing polished shorts from multiple highlights","teams automating batch video production with consistent formatting","developers building video assembly pipelines"],"limitations":["Segment concatenation requires matching codec/resolution; mismatches trigger re-encoding (adds 2-5 minutes per video)","Fade transitions require brief re-encoding overlap (~1-2 seconds per transition)","Audio sync drift can occur if segments have different frame rates; requires normalization","No built-in support for complex effects (color grading, filters); limited to basic transitions","Output file size scales linearly with segment count and duration"],"requires":["Python 3.7+","FFmpeg 4.0+","Cropped video segments (MP4 files)","Matching codec/resolution across segments (or re-encoding tolerance)","Sufficient disk space for output file"],"input_types":["Array of cropped video file paths (strings)","Segment order (array of indices)","Transition type (string: 'fade', 'cut', 'dissolve')","Optional: intro/outro video files"],"output_types":["Composite video file (MP4, H.264 codec)","Composition metadata (JSON: segment order, transitions, duration)","Platform-optimized variants (YouTube, TikTok, Instagram formats)"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_6","uri":"capability://automation.workflow.end.to.end.pipeline.orchestration.with.error.handling","name":"end-to-end pipeline orchestration with error handling","description":"Coordinates the entire workflow from YouTube URL input to final vertical short output, managing state transitions between components, handling failures gracefully, and providing progress tracking. The main.py script implements a sequential pipeline that chains together download → transcription → highlight detection → face tracking → cropping → composition, with checkpointing to resume from failures. Includes logging, error recovery, and optional manual intervention points.","intents":["I want to process a video from URL to finished short with a single command","I need visibility into which step is running and how long it will take","I want the pipeline to recover from transient failures (API timeouts, network issues)"],"best_for":["solo developers building automated content production systems","teams running batch video processing jobs","non-technical content creators using pre-configured pipelines"],"limitations":["No distributed processing; single-threaded execution limits throughput to ~1-2 videos per hour","Checkpointing is basic; no transaction-like rollback for partial failures","Error messages may be cryptic for non-technical users; requires debugging knowledge","No built-in monitoring/alerting; failures only visible in logs","Pipeline is sequential; cannot parallelize independent steps (e.g., multiple video downloads)"],"requires":["Python 3.7+","All dependencies from previous capabilities (FFmpeg, OpenCV, OpenAI API key)","Configuration file with API keys and parameters","Sufficient disk space for intermediate files (2-3x source video size)"],"input_types":["YouTube URL (string)","Configuration object (JSON: api_keys, output_path, processing_options)","Optional: manual override parameters"],"output_types":["Final vertical short video (MP4)","Processing log (text file with timestamps)","Intermediate artifacts (transcript, highlight list, face tracking data)","Metadata file (JSON: source_url, processing_time, segment_info)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_7","uri":"capability://automation.workflow.configurable.processing.parameters.and.output.optimization","name":"configurable processing parameters and output optimization","description":"Exposes tunable parameters for each pipeline stage (highlight detection sensitivity, face detection confidence threshold, crop margin, transition duration, output resolution), enabling users to optimize for their specific content type and platform requirements. Configuration is managed through a JSON/YAML file or command-line arguments, with sensible defaults for common use cases (YouTube Shorts, TikTok, Instagram Reels). Supports platform-specific output presets that automatically adjust resolution, bitrate, and aspect ratio.","intents":["I want to adjust how aggressive the highlight detection is for my content type","I need to optimize output quality for a specific platform (YouTube vs TikTok)","I want to fine-tune face detection sensitivity for my video quality"],"best_for":["power users optimizing pipeline behavior for specific content domains","teams managing multiple content types with different requirements","developers integrating the tool into larger systems"],"limitations":["Parameter tuning requires experimentation; no automated optimization","Some parameters have non-obvious effects (e.g., face detection confidence vs false positives)","No validation of parameter combinations; invalid configs may fail silently","Documentation of parameter effects may be incomplete","Changes require pipeline restart; no hot-reload capability"],"requires":["Python 3.7+","Configuration file (JSON or YAML format)","Understanding of parameter semantics"],"input_types":["Configuration file (JSON/YAML)","Command-line arguments (key=value pairs)","Environment variables (for sensitive data like API keys)"],"output_types":["Processed configuration object (JSON)","Output video optimized for target platform","Configuration validation report"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-samuraigpt--ai-youtube-shorts-generator__cap_8","uri":"capability://automation.workflow.batch.processing.with.queue.management.and.progress.tracking","name":"batch processing with queue management and progress tracking","description":"Enables processing multiple YouTube videos in sequence with a job queue, progress tracking, and optional parallelization. The system maintains a queue of URLs, processes them sequentially (or in parallel with worker threads), and provides real-time progress updates including estimated time remaining. Supports resuming interrupted batch jobs and generating summary reports of successes/failures.","intents":["I want to process 50 videos overnight without manual intervention","I need to know which videos succeeded and which failed","I want to resume a batch job that was interrupted halfway through"],"best_for":["content creators with large video libraries","teams running automated content production at scale","developers building video processing services"],"limitations":["Sequential processing limits throughput; parallelization requires careful resource management","No distributed processing across machines; limited to single-machine resources","Queue persistence is basic; loss of process means loss of queue state","API rate limiting (OpenAI, YouTube) may throttle batch processing","Memory usage scales with batch size; large batches may require external queue (Redis, RabbitMQ)"],"requires":["Python 3.7+","All pipeline dependencies","Batch input file (CSV/JSON with URLs)","Sufficient disk space for all intermediate files"],"input_types":["Batch file (CSV/JSON: [{url, config_overrides}, ...])","Queue configuration (max_workers, retry_policy)","Optional: resume checkpoint file"],"output_types":["Array of output video files","Batch summary report (JSON: total_processed, succeeded, failed, avg_processing_time)","Per-video logs and metadata","Checkpoint file for resuming interrupted batches"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.7+","yt-dlp or youtube-dl library","FFmpeg installed and in system PATH","Sufficient disk space (minimum 5GB recommended)","Network connectivity to YouTube","OpenAI API key with Whisper access","FFmpeg for audio extraction","openai Python package (version 0.27+)","Audio file or video with audio track","OpenAI API key with GPT-4 access"],"failure_modes":["No support for age-restricted or private videos without authentication","Download speed limited by network bandwidth and YouTube rate limiting","Requires sufficient local disk space for full video storage (1-10GB+ for long-form content)","No built-in resume capability for interrupted downloads","Whisper accuracy varies by audio quality; background noise reduces precision to 70-85% word error rate","Timestamp alignment has ±500ms margin of error at segment boundaries","No native speaker diarization; requires post-processing to distinguish multiple speakers","Processing time is ~1/6 of video duration (10-minute video takes ~100 seconds)","GPT-4 API costs scale with transcript length (~$0.03-0.10 per video depending on length)","Highlight quality depends on prompt engineering; generic prompts produce generic results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5617831097253791,"quality":0.43,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-04-22T08:03:35.846Z","last_commit":"2026-04-19T20:55:38Z"},"community":{"stars":3233,"forks":554,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=samuraigpt--ai-youtube-shorts-generator","compare_url":"https://unfragile.ai/compare?artifact=samuraigpt--ai-youtube-shorts-generator"}},"signature":"VpyBP8JmDKA/kksg+5eSFlNbyL1kyPo/z6wlBQAZrKt0wEHmpRBOANOjxF2GyuU7BXAaa9iin1vd8YN5vpRPAQ==","signedAt":"2026-06-21T07:36:11.286Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/samuraigpt--ai-youtube-shorts-generator","artifact":"https://unfragile.ai/samuraigpt--ai-youtube-shorts-generator","verify":"https://unfragile.ai/api/v1/verify?slug=samuraigpt--ai-youtube-shorts-generator","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}