{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_trellis","slug":"trellis","name":"Trellis","type":"product","url":"https://www.readtrellis.com","page_url":"https://unfragile.ai/trellis","categories":["research-search"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_trellis__cap_0","uri":"capability://text.generation.language.ai.powered.text.summarization.with.configurable.depth","name":"ai-powered text summarization with configurable depth","description":"Generates abstractive summaries of selected text passages or full documents using language models, allowing users to specify summary length and detail level. The system processes highlighted or full-text content through an LLM pipeline that extracts key concepts and synthesizes them into coherent summaries without requiring manual note-taking or external tools.","intents":["I want to quickly understand the main points of a dense academic paper without reading every paragraph","I need to generate study notes automatically from textbook chapters","I want to compare my understanding against an AI-generated summary to identify gaps"],"best_for":["Students tackling dense academic or technical texts who need rapid comprehension","Researchers reviewing large volumes of papers and needing quick overviews","Non-native English readers who benefit from distilled, simplified versions of complex material"],"limitations":["Abstractive summarization may omit nuanced arguments or edge cases important to full understanding","Quality degrades on highly specialized or domain-specific texts where the LLM lacks training data","No user control over which sections are prioritized in the summary — algorithm-driven selection may miss user-relevant details"],"requires":["Active internet connection for LLM API calls","Text content in digital, selectable format (not scanned PDFs without OCR)","Sufficient document length (typically 200+ words for meaningful summaries)"],"input_types":["plain text","highlighted text selections","full document text"],"output_types":["text summary","structured bullet points"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_1","uri":"capability://text.generation.language.native.text.to.speech.with.playback.speed.control","name":"native text-to-speech with playback speed control","description":"Converts selected or full-document text to audio using text-to-speech synthesis with adjustable playback speeds (typically 0.5x to 2x), allowing asynchronous consumption of reading material during commuting, exercise, or multitasking. The system likely uses cloud-based TTS APIs (Google Cloud TTS, Azure Speech Services, or similar) with client-side playback controls and speed normalization.","intents":["I want to listen to academic papers while commuting or exercising without being tethered to a screen","I need to slow down playback to 0.75x speed to better process complex technical terminology","I want to accelerate playback to 1.5x speed to review familiar material quickly"],"best_for":["Commuters and mobile learners who consume content during transit or physical activity","Auditory learners who retain information better through listening than reading","Professionals with limited screen time who want to maintain reading habits"],"limitations":["Synthetic speech lacks prosody and emotional nuance of human narration, potentially reducing engagement for narrative content","Speed adjustment may reduce comprehension at extreme speeds (>1.75x) for complex material","No speaker selection or voice customization — limited to platform default voices","Requires continuous internet connection for cloud-based TTS; no offline caching of generated audio"],"requires":["Active internet connection for TTS API calls","Audio output device (speakers or headphones)","Browser or app with audio playback capabilities"],"input_types":["plain text","highlighted text selections","full document text"],"output_types":["audio stream","playback controls (play, pause, speed adjustment)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_2","uri":"capability://memory.knowledge.contextual.annotation.and.highlight.management","name":"contextual annotation and highlight management","description":"Provides an integrated annotation system allowing users to highlight text, add notes, and tag passages with metadata (e.g., 'key concept', 'question', 'definition') without fragmenting the reading experience. Annotations are stored in a structured format (likely JSON or database records) linked to document position and content, enabling retrieval, filtering, and export workflows.","intents":["I want to highlight important passages and add margin notes without switching to a separate note-taking app","I need to organize my highlights by category (e.g., 'definitions', 'examples', 'counterarguments') for later study","I want to export all my annotations from a document as a study guide or outline"],"best_for":["Students building study materials from reading assignments","Researchers extracting and organizing key findings across multiple papers","Educators preparing lecture notes from source materials"],"limitations":["Annotations are siloed within Trellis — no native export to Obsidian, Notion, or other knowledge management systems without manual copy-paste","No collaborative annotation features — annotations are per-user, limiting group study workflows","Highlight colors and styles are limited compared to physical margin notes or tools like Hypothesis","No version control or annotation history — edits overwrite previous versions without audit trail"],"requires":["Digital, selectable text (not scanned PDFs without OCR)","Trellis account with document access"],"input_types":["highlighted text selections","free-form text notes","metadata tags"],"output_types":["structured annotation records","filtered annotation lists","exported annotation documents"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_3","uri":"capability://text.generation.language.ai.generated.discussion.questions.and.comprehension.prompts","name":"ai-generated discussion questions and comprehension prompts","description":"Automatically generates targeted discussion questions and comprehension prompts based on document content using prompt engineering or fine-tuned LLMs. The system analyzes text structure, key concepts, and learning objectives to create questions at varying difficulty levels (recall, comprehension, analysis, synthesis) that guide deeper engagement with material.","intents":["I want to test my understanding of a chapter by answering AI-generated questions without manually creating study guides","I need discussion prompts to facilitate group study sessions or classroom discussions","I want to identify gaps in my comprehension by attempting questions before reviewing answers"],"best_for":["Students preparing for exams or assessments who need self-testing tools","Educators creating discussion materials for classes without manual question writing","Self-directed learners who want structured comprehension checks"],"limitations":["Generated questions may not align with instructor expectations or course learning objectives","No answer key or automated grading — users must self-evaluate or seek external feedback","Question quality varies by content domain; performs better on well-structured academic texts than on opinion pieces or creative writing","No customization of question difficulty or focus areas — algorithm-driven generation may miss user priorities"],"requires":["Digital text content with sufficient length and structure","Active internet connection for LLM API calls"],"input_types":["full document text","text selections"],"output_types":["structured question lists","question-answer pairs","difficulty-tagged prompts"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_4","uri":"capability://automation.workflow.document.aware.reading.interface.with.inline.ai.tools","name":"document-aware reading interface with inline ai tools","description":"Provides a unified reading environment that layers AI capabilities (summarization, TTS, annotation, questions) directly into the document view without requiring external tools or context switching. The interface likely uses a web-based document renderer (possibly PDF.js or similar) with embedded UI controls for each AI feature, maintaining reading state and document position across tool invocations.","intents":["I want a single interface where I can read, annotate, listen, and get AI help without juggling multiple apps","I need to maintain my reading progress and annotations across sessions without manual synchronization","I want quick access to AI tools (summarize, ask questions, generate notes) without leaving the reading context"],"best_for":["Students who want an all-in-one reading and study tool","Researchers managing multiple documents with consistent workflows","Educators who want to assign readings with built-in comprehension support"],"limitations":["Tightly coupled interface may feel overwhelming for users who only need one or two features","No offline reading capability — requires internet connection for document access and AI features","Limited customization of interface layout or tool visibility — one-size-fits-all design","Performance may degrade with very large documents (1000+ pages) due to rendering overhead"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","Active internet connection","Trellis account with document access"],"input_types":["PDF documents","web articles","plain text"],"output_types":["rendered document view","reading progress tracking","integrated annotation and note data"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_5","uri":"capability://data.processing.analysis.document.upload.and.format.normalization","name":"document upload and format normalization","description":"Accepts multiple document formats (PDF, DOCX, EPUB, web URLs, plain text) and normalizes them into a unified internal representation suitable for AI processing and rendering. The system likely uses format-specific parsers (PDFKit or similar for PDFs, pandoc-like converters for DOCX) and OCR for scanned documents, extracting text and metadata while preserving document structure.","intents":["I want to upload a PDF research paper and immediately start annotating and summarizing without format conversion","I need to process scanned textbook chapters that don't have selectable text","I want to add a web article to my reading list by pasting a URL"],"best_for":["Users with diverse document sources (academic papers, textbooks, web articles, personal notes)","Researchers who need to process large batches of documents in different formats","Students who receive reading materials in various formats from different instructors"],"limitations":["OCR quality on scanned documents varies by image quality and language; non-English text may have higher error rates","Complex PDF layouts (multi-column, sidebars, footnotes) may not parse correctly, leading to garbled text extraction","Large files (>50MB) may timeout or fail during upload; no streaming or chunked upload support","Formatting metadata (fonts, colors, emphasis) is lost during normalization — only text content is preserved"],"requires":["Document file or URL","File size typically <50MB","Supported format: PDF, DOCX, EPUB, plain text, or web URL"],"input_types":["PDF files","DOCX documents","EPUB ebooks","plain text files","web URLs"],"output_types":["normalized text content","extracted metadata (title, author, date)","document structure (chapters, sections)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_6","uri":"capability://memory.knowledge.reading.progress.tracking.and.session.persistence","name":"reading progress tracking and session persistence","description":"Maintains reading state (current page/position, scroll location, time spent) across sessions and devices, allowing users to resume reading without manual bookmarking. The system likely stores reading progress in a user database with timestamps and device identifiers, enabling cross-device synchronization and reading history analytics.","intents":["I want to start reading on my laptop and continue on my phone without losing my place","I need to track how much time I've spent reading each document for study planning","I want to see my reading history to revisit previously read documents"],"best_for":["Mobile-first readers who switch between devices frequently","Students tracking reading time for course requirements or self-assessment","Researchers managing reading workflows across multiple devices"],"limitations":["Cross-device synchronization may have latency (5-30 seconds) before progress updates on other devices","No offline reading — progress cannot be tracked without internet connection","Reading time analytics are basic (total time, sessions) without insights into comprehension or engagement","No integration with calendar or time-tracking tools for study planning"],"requires":["Trellis account with cloud sync enabled","Active internet connection for synchronization","Multiple devices with Trellis app/web access (for cross-device sync)"],"input_types":["reading position (page number, scroll offset)","session timestamps","device identifiers"],"output_types":["reading progress state","reading history list","time-spent analytics"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_trellis__cap_7","uri":"capability://search.retrieval.semantic.search.within.annotated.documents","name":"semantic search within annotated documents","description":"Enables full-text and semantic search across a user's library of documents and annotations, using keyword matching and embedding-based similarity search to find relevant passages. The system likely indexes documents and annotations using vector embeddings (from models like OpenAI's text-embedding-3 or similar) stored in a vector database, enabling queries like 'find all passages about machine learning ethics' across multiple documents.","intents":["I want to find all my notes about a specific concept across multiple documents without manually reviewing each one","I need to search for passages semantically similar to a query (e.g., 'explain bias in algorithms') even if exact keywords don't match","I want to discover connections between ideas across different readings"],"best_for":["Researchers building literature reviews who need to synthesize ideas across many papers","Students reviewing study materials across multiple courses or semesters","Educators preparing lectures by finding relevant examples across their reading library"],"limitations":["Semantic search quality depends on embedding model quality; may miss domain-specific nuances in specialized fields","Search results are ranked by relevance but not contextualized — no automatic summarization of why a passage matched the query","Indexing latency: new documents may take minutes to become searchable after upload","No advanced query syntax (boolean operators, field-specific search) — limited to natural language queries"],"requires":["Documents uploaded to Trellis with text content extracted","Active internet connection for search API calls","Sufficient library size (typically 5+ documents) for meaningful semantic search"],"input_types":["natural language search queries","keyword searches"],"output_types":["ranked list of matching passages","document references with excerpt context","relevance scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for LLM API calls","Text content in digital, selectable format (not scanned PDFs without OCR)","Sufficient document length (typically 200+ words for meaningful summaries)","Active internet connection for TTS API calls","Audio output device (speakers or headphones)","Browser or app with audio playback capabilities","Digital, selectable text (not scanned PDFs without OCR)","Trellis account with document access","Digital text content with sufficient length and structure","Modern web browser (Chrome, Firefox, Safari, Edge)"],"failure_modes":["Abstractive summarization may omit nuanced arguments or edge cases important to full understanding","Quality degrades on highly specialized or domain-specific texts where the LLM lacks training data","No user control over which sections are prioritized in the summary — algorithm-driven selection may miss user-relevant details","Synthetic speech lacks prosody and emotional nuance of human narration, potentially reducing engagement for narrative content","Speed adjustment may reduce comprehension at extreme speeds (>1.75x) for complex material","No speaker selection or voice customization — limited to platform default voices","Requires continuous internet connection for cloud-based TTS; no offline caching of generated audio","Annotations are siloed within Trellis — no native export to Obsidian, Notion, or other knowledge management systems without manual copy-paste","No collaborative annotation features — annotations are per-user, limiting group study workflows","Highlight colors and styles are limited compared to physical margin notes or tools like Hypothesis","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":"2026-05-24T12:16:33.648Z","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=trellis","compare_url":"https://unfragile.ai/compare?artifact=trellis"}},"signature":"eQNQnJyoeVb6Tj/khAE6YE2j4puUT0JVUFShY0DTdobbf+NiauqIapV8hYoLXXCQ75Llk379JTlv6vW+PNo3DA==","signedAt":"2026-06-20T14:05:38.494Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/trellis","artifact":"https://unfragile.ai/trellis","verify":"https://unfragile.ai/api/v1/verify?slug=trellis","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"}}