{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_voxweave","slug":"voxweave","name":"Voxweave","type":"product","url":"https://voxweave.xyz","page_url":"https://unfragile.ai/voxweave","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_voxweave__cap_0","uri":"capability://data.processing.analysis.youtube.video.content.extraction.and.transcription","name":"youtube video content extraction and transcription","description":"Automatically retrieves and processes YouTube video content by integrating with YouTube's API or transcript service to extract full or partial transcripts without requiring manual upload or linking. The system likely uses YouTube Data API v3 to fetch video metadata and captions, then normalizes transcript formatting across different caption sources (auto-generated, manual, multiple languages) into a unified text representation for downstream processing.","intents":["I need to get the full transcript of a YouTube video without manually copying it","I want to extract captions from a video that has multiple language options","I need to retrieve video metadata alongside the transcript for context"],"best_for":["Content researchers who need rapid access to video transcripts","Educators building curriculum materials from educational videos","Knowledge workers processing dozens of videos weekly"],"limitations":["Depends on YouTube's transcript availability — videos without captions or auto-generated transcripts cannot be processed","Auto-generated captions may contain errors, especially for technical terminology or non-English content","YouTube API rate limits apply; processing large video batches may hit quota constraints","Cannot extract visual information (charts, diagrams, on-screen text) — text-only extraction"],"requires":["YouTube video URL or video ID","Internet connectivity to access YouTube's API","Video must be publicly accessible or user must have permission to view it"],"input_types":["YouTube URL","YouTube video ID"],"output_types":["Plain text transcript","Structured transcript with timestamps","Video metadata (title, duration, channel)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_1","uri":"capability://text.generation.language.abstractive.video.summarization.with.context.preservation","name":"abstractive video summarization with context preservation","description":"Transforms full video transcripts into concise, multi-level summaries using advanced NLP models (likely transformer-based abstractive summarization) that preserve semantic meaning and key insights rather than extracting keyword phrases. The system likely employs hierarchical summarization — first identifying key segments or topics within the transcript, then generating abstractive summaries at multiple granularity levels (headline, paragraph, full summary), ensuring nuance and context are retained across compression ratios.","intents":["I need a 2-3 sentence summary capturing the core insight without watching the full video","I want a structured summary with key points broken into sections matching the video's logical flow","I need summaries that preserve technical nuance, not just keyword extraction"],"best_for":["Researchers processing educational or technical videos where context matters","Students building study notes from lecture recordings","Podcast enthusiasts who need structured takeaways from long-form content"],"limitations":["Summarization quality degrades on highly specialized or domain-specific content (medical, legal, advanced mathematics) where the model lacks training data","Cannot infer visual context — if key information is conveyed through on-screen graphics, summaries will be incomplete","Abstractive summarization may hallucinate or misinterpret ambiguous statements in the source transcript","No user feedback loop to correct or refine summaries — quality is deterministic based on model weights"],"requires":["Complete or near-complete transcript from video","Sufficient context length (typically 512-4096 tokens) for the summarization model to operate effectively"],"input_types":["Plain text transcript","Transcript with timestamps"],"output_types":["Headline summary (1-2 sentences)","Paragraph summary (3-5 sentences)","Structured summary with key points and sections","JSON with hierarchical summary levels"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_2","uri":"capability://text.generation.language.multi.language.transcript.normalization.and.processing","name":"multi-language transcript normalization and processing","description":"Handles transcripts across multiple languages by normalizing formatting, detecting language automatically, and optionally translating or processing non-English content. The system likely uses language detection models (e.g., fastText or transformer-based classifiers) to identify transcript language, then applies language-specific NLP pipelines for tokenization, segmentation, and summarization, with optional machine translation to English for users who prefer English summaries.","intents":["I want to summarize a video in a language other than English","I need transcripts from multilingual videos processed consistently","I want to translate a non-English video summary to English for my team"],"best_for":["International teams consuming content across multiple languages","Researchers studying non-English educational or technical content","Global organizations needing multilingual content extraction"],"limitations":["Translation quality varies significantly by language pair — less common language combinations may produce poor translations","Language detection can fail on code-mixed or heavily accented transcripts","Summarization models may be less effective in non-English languages if trained primarily on English data","Character encoding issues may arise with non-Latin scripts if not properly handled"],"requires":["Transcript in supported language (likely 20-50 major languages)","Language detection model or explicit language specification"],"input_types":["Transcript in any supported language","Language code (optional, for explicit specification)"],"output_types":["Normalized transcript in original language","Summary in original language","Translated summary in target language (if translation enabled)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_3","uri":"capability://data.processing.analysis.timestamp.aware.summary.segmentation.and.navigation","name":"timestamp-aware summary segmentation and navigation","description":"Maps summary sections back to specific timestamps in the original video, enabling users to jump directly to relevant segments. The system likely uses alignment algorithms (sequence matching or attention-based mapping) to correlate summary sentences with transcript segments, preserving timestamp metadata through the summarization pipeline so users can navigate the video by summary structure rather than scrubbing linearly.","intents":["I want to jump to the part of the video that discusses a specific topic from the summary","I need to know exactly when in the video a key point is mentioned","I want to create a table of contents for the video based on the summary structure"],"best_for":["Educators creating timestamped lecture notes","Researchers building detailed reference materials from videos","Content creators extracting clips from longer videos"],"limitations":["Timestamp alignment accuracy depends on transcript quality — poor auto-generated captions lead to misaligned timestamps","Abstractive summarization may compress multiple non-contiguous segments into one summary point, making 1:1 timestamp mapping impossible","Requires transcript with timestamp metadata — videos with only plain text transcripts cannot be processed"],"requires":["Transcript with timestamp information (HH:MM:SS format)","Alignment algorithm (likely sequence matching or neural attention)"],"input_types":["Transcript with timestamps","Summary with section boundaries"],"output_types":["Summary with embedded timestamps","JSON with summary sections and corresponding video ranges","Clickable table of contents with timestamp links"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_4","uri":"capability://data.processing.analysis.structured.insight.extraction.with.topic.hierarchies","name":"structured insight extraction with topic hierarchies","description":"Extracts and organizes key insights, arguments, and topics from video content into hierarchical structures (e.g., main topics → subtopics → supporting points) using topic modeling or semantic clustering. The system likely uses techniques like Latent Dirichlet Allocation (LDA), BERTopic, or transformer-based clustering to identify thematic coherence in the transcript, then organizes extracted insights into a tree structure that reflects the video's conceptual hierarchy rather than linear transcript order.","intents":["I want to see all the main topics covered in the video organized hierarchically","I need to extract key arguments and supporting evidence from a lecture or presentation","I want to identify which topics are emphasized most heavily in the video"],"best_for":["Students building comprehensive study guides from lectures","Researchers mapping conceptual relationships in educational content","Content analysts identifying thematic patterns across multiple videos"],"limitations":["Topic extraction quality depends on transcript coherence — rambling or poorly-structured videos produce shallow hierarchies","Unsupervised topic modeling may create unintuitive groupings without domain knowledge","Cannot distinguish between main topics and tangential discussions without additional context or metadata","Hierarchy depth is limited by the amount of content — short videos may not support deep hierarchies"],"requires":["Transcript with sufficient length (typically 1000+ tokens) for meaningful topic modeling","Topic modeling algorithm (LDA, BERTopic, or transformer-based clustering)"],"input_types":["Plain text transcript","Transcript with segment boundaries (optional)"],"output_types":["Hierarchical topic tree (JSON or nested structure)","Topic labels with associated transcript excerpts","Topic importance scores or emphasis metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_5","uri":"capability://automation.workflow.batch.video.processing.and.queue.management","name":"batch video processing and queue management","description":"Enables processing of multiple YouTube videos in sequence or parallel, with queue management, progress tracking, and batch result export. The system likely implements a job queue (Redis, RabbitMQ, or similar) that accepts multiple video URLs, distributes processing tasks across worker processes, tracks completion status, and aggregates results for bulk export in formats like CSV or JSON.","intents":["I need to summarize 50 videos from a playlist without processing them one-by-one","I want to schedule batch processing to run overnight and get results in the morning","I need to export summaries for all videos in a course as a single file"],"best_for":["Educators processing entire course video libraries","Researchers analyzing large video datasets","Content teams managing bulk summarization workflows"],"limitations":["Batch processing introduces latency — individual results are not immediately available","API rate limits may throttle batch processing speed, especially for large batches","No real-time progress updates in basic implementations — users may not know processing status","Batch failures (e.g., one video fails) may require manual retry logic"],"requires":["Job queue infrastructure (Redis, RabbitMQ, or cloud equivalent)","Worker processes for parallel processing","Storage for intermediate and final results"],"input_types":["List of YouTube URLs","CSV or JSON with video metadata","Playlist URL (if supported)"],"output_types":["CSV with summaries for all videos","JSON with structured results","Downloadable archive with individual summary files"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_6","uri":"capability://tool.use.integration.summary.export.and.integration.with.note.taking.systems","name":"summary export and integration with note-taking systems","description":"Exports summaries in multiple formats (Markdown, HTML, PDF, plain text) and integrates with popular note-taking platforms (Notion, Obsidian, OneNote, Evernote) via API or direct export. The system likely implements format converters and OAuth-based integrations to enable one-click export of summaries directly into users' existing knowledge management systems, preserving formatting and metadata.","intents":["I want to export a summary as a Markdown file for my note-taking system","I need to automatically send summaries to my Notion database","I want to create a PDF of the summary with proper formatting for printing or sharing"],"best_for":["Students integrating video summaries into study systems","Researchers building knowledge bases from video content","Teams collaborating on shared note systems"],"limitations":["Export format quality varies — complex formatting may not translate perfectly across platforms","OAuth integrations require user authentication and may break if platform APIs change","Metadata preservation is platform-dependent — some systems may lose timestamps or hierarchical structure","Large summaries may exceed platform-specific size limits (e.g., Notion API field limits)"],"requires":["Export format library (Markdown, HTML, PDF generators)","OAuth credentials for integrated platforms (if using direct integration)","API access to target note-taking platforms"],"input_types":["Summary object (internal representation)","Target export format (markdown, pdf, html, etc.)","Target platform (notion, obsidian, onenote, etc.)"],"output_types":["Markdown file","HTML file","PDF document","Direct platform integration (Notion page, Obsidian note, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_7","uri":"capability://text.generation.language.custom.summarization.style.and.tone.configuration","name":"custom summarization style and tone configuration","description":"Allows users to customize summary output by specifying desired style (academic, casual, technical, executive), tone (formal, conversational, analytical), and detail level (headline, paragraph, comprehensive). The system likely uses prompt engineering or fine-tuned models with style-specific parameters to generate summaries matching user preferences, rather than producing a single canonical summary for each video.","intents":["I want summaries written in academic style for my research paper","I need casual, conversational summaries for my study group","I want executive summaries that focus on actionable insights, not background"],"best_for":["Researchers with specific academic writing requirements","Teams with diverse summarization needs (students vs. executives)","Content creators adapting summaries for different audiences"],"limitations":["Style customization quality depends on model capability — some styles may be poorly executed","Conflicting style requirements (e.g., 'very detailed' + 'very brief') may produce suboptimal results","No user feedback loop to refine style preferences — customization is static per request","Increased latency for custom summarization compared to standard summaries"],"requires":["Summarization model with style/tone control capability","Style parameter definitions (academic, casual, technical, etc.)","Detail level specifications"],"input_types":["Transcript","Style preference (enum or string)","Tone preference (enum or string)","Detail level (enum: headline/paragraph/comprehensive)"],"output_types":["Styled summary matching user preferences","Multiple style variants for comparison"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_voxweave__cap_8","uri":"capability://safety.moderation.video.content.quality.assessment.and.reliability.scoring","name":"video content quality assessment and reliability scoring","description":"Analyzes video content to assess credibility, expertise level, and potential bias, providing users with confidence scores for summary reliability. The system likely uses heuristics based on speaker credentials (if available), citation density, claim verification against knowledge bases, and language patterns associated with misinformation, producing a reliability score that indicates how much users should trust the summary.","intents":["I want to know if this video is from a credible source before relying on its information","I need to assess the expertise level of the speaker for academic purposes","I want to identify potential bias or misinformation in the video content"],"best_for":["Students evaluating source credibility for academic work","Researchers assessing information quality from video sources","Fact-checkers and journalists verifying video claims"],"limitations":["Reliability scoring is heuristic-based and may produce false positives/negatives","Cannot verify claims without access to external fact-checking databases or knowledge bases","Speaker credential verification requires metadata that may not be available from YouTube","Bias detection is imperfect and may reflect model biases rather than actual content bias","No ground truth for validation — reliability scores are estimates, not definitive assessments"],"requires":["Video metadata (channel info, speaker credentials if available)","Transcript content for analysis","Optional: external knowledge bases or fact-checking APIs for claim verification"],"input_types":["Video metadata","Transcript","Optional: speaker information"],"output_types":["Reliability score (0-100 or similar scale)","Credibility assessment (high/medium/low)","Bias indicators","Confidence level in assessment"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["YouTube video URL or video ID","Internet connectivity to access YouTube's API","Video must be publicly accessible or user must have permission to view it","Complete or near-complete transcript from video","Sufficient context length (typically 512-4096 tokens) for the summarization model to operate effectively","Transcript in supported language (likely 20-50 major languages)","Language detection model or explicit language specification","Transcript with timestamp information (HH:MM:SS format)","Alignment algorithm (likely sequence matching or neural attention)","Transcript with sufficient length (typically 1000+ tokens) for meaningful topic modeling"],"failure_modes":["Depends on YouTube's transcript availability — videos without captions or auto-generated transcripts cannot be processed","Auto-generated captions may contain errors, especially for technical terminology or non-English content","YouTube API rate limits apply; processing large video batches may hit quota constraints","Cannot extract visual information (charts, diagrams, on-screen text) — text-only extraction","Summarization quality degrades on highly specialized or domain-specific content (medical, legal, advanced mathematics) where the model lacks training data","Cannot infer visual context — if key information is conveyed through on-screen graphics, summaries will be incomplete","Abstractive summarization may hallucinate or misinterpret ambiguous statements in the source transcript","No user feedback loop to correct or refine summaries — quality is deterministic based on model weights","Translation quality varies significantly by language pair — less common language combinations may produce poor translations","Language detection can fail on code-mixed or heavily accented transcripts","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.117Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=voxweave","compare_url":"https://unfragile.ai/compare?artifact=voxweave"}},"signature":"UnS9xd8mQ6MC+qbeS1SNZ05h8Ct6aX/kIRx9WjD+xKWfvty9GpH8Gbc0JB08uH+RhRqtCo1KT1jmboASxwBHBg==","signedAt":"2026-06-21T15:42:16.448Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/voxweave","artifact":"https://unfragile.ai/voxweave","verify":"https://unfragile.ai/api/v1/verify?slug=voxweave","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"}}