{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_cleft","slug":"cleft","name":"Cleft","type":"product","url":"https://www.cleftnotes.com","page_url":"https://unfragile.ai/cleft","categories":["text-writing"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_cleft__cap_0","uri":"capability://data.processing.analysis.local.device.speech.to.text.transcription.with.privacy.isolation","name":"local-device speech-to-text transcription with privacy isolation","description":"Converts spoken audio into text using on-device speech recognition models that never transmit audio data to external servers. The implementation leverages browser-native Web Speech API or local inference engines (likely ONNX Runtime or TensorFlow Lite) to perform acoustic-to-phoneme mapping and language modeling entirely within the user's device sandbox, eliminating cloud transmission overhead and ensuring audio payloads remain under user control.","intents":["I want to record voice notes without my audio being sent to cloud servers","I need transcription that respects GDPR/HIPAA compliance requirements for sensitive content","I want to use voice capture in offline environments without internet dependency","I need to ensure my meeting recordings never leave my device for security reasons"],"best_for":["Privacy-conscious professionals handling sensitive information (legal, medical, financial)","Teams in regulated industries requiring data residency compliance","Solo knowledge workers in low-bandwidth or offline environments","Researchers and academics protecting confidential research data"],"limitations":["Transcription accuracy typically 85-92% vs 95%+ for cloud solutions like Otter.ai due to smaller local models","No real-time speaker diarization or multi-speaker identification without additional processing","Language support limited to models bundled locally; adding new languages requires app updates","Latency for processing longer audio segments (10+ minutes) may exceed cloud solutions due to device CPU constraints"],"requires":["Modern browser with Web Speech API support (Chrome 25+, Edge 79+, Safari 14.1+) or Electron runtime","Minimum 2GB available device RAM for model inference","Microphone hardware with OS-level permissions granted","No internet required but optional for cloud backup features"],"input_types":["audio/wav","audio/mp3","audio/webm","real-time microphone stream"],"output_types":["plain text","structured markdown with timestamps"],"categories":["data-processing-analysis","privacy-first"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_1","uri":"capability://text.generation.language.voice.to.markdown.structural.formatting.with.semantic.parsing","name":"voice-to-markdown structural formatting with semantic parsing","description":"Transforms raw transcribed text into semantically structured markdown by detecting natural speech patterns (pauses, emphasis, topic shifts) and converting them into markdown syntax (headers, lists, bold/italic, code blocks). The system likely uses NLP-based sentence segmentation, keyword extraction, and heuristic rules to infer document structure from spoken discourse patterns, outputting valid markdown that integrates directly with note-taking ecosystems.","intents":["I want my voice notes automatically formatted as markdown without manual editing","I need meeting notes structured with headers and bullet points from natural speech","I want code snippets or technical terms automatically formatted as code blocks","I need to paste transcribed notes directly into Obsidian or Notion without reformatting"],"best_for":["Markdown-native knowledge workers using Obsidian, Logseq, or Roam Research","Developers capturing technical notes and code snippets during brainstorming","Researchers organizing literature notes with hierarchical structure","Students creating study guides from lecture recordings"],"limitations":["Structural inference relies on heuristics; complex nested hierarchies may require manual adjustment","No context awareness of domain-specific terminology; technical terms may be incorrectly formatted","Ambiguous speech patterns (e.g., 'dash' vs actual list item) may produce incorrect markdown syntax","No support for markdown extensions (tables, footnotes, LaTeX) beyond basic syntax"],"requires":["Completed transcription output from speech-to-text module","Target markdown editor or note-taking app with markdown support","No additional API keys or external dependencies"],"input_types":["raw transcribed text","transcribed text with timing metadata"],"output_types":["markdown text","markdown with embedded metadata (timestamps, speaker labels)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_2","uri":"capability://data.processing.analysis.real.time.transcription.with.live.editing.and.correction","name":"real-time transcription with live editing and correction","description":"Provides streaming transcription output as the user speaks, displaying partial results that update incrementally as new audio frames are processed. The implementation uses a streaming speech recognition pipeline (likely attention-based RNN or Conformer architecture) that processes audio chunks and emits intermediate hypotheses, allowing users to see text appear in real-time and make corrections before finalizing the note.","intents":["I want to see my words appear as I speak to verify accuracy in real-time","I need to correct transcription errors immediately during recording rather than after","I want live preview of how my voice notes will be formatted as markdown","I need to stop recording when I see the transcription is accurate enough"],"best_for":["Users who prefer immediate feedback during voice capture","Professionals recording sensitive content who want to verify accuracy before saving","Non-native speakers who benefit from seeing text to confirm pronunciation clarity","Users in noisy environments who can adjust microphone position based on real-time feedback"],"limitations":["Streaming models typically have 2-5% lower accuracy than full-audio models due to lack of future context","Real-time correction UI adds complexity; undo/redo for streaming edits requires state management overhead","Latency between speech and text appearance typically 500ms-2s depending on device performance","Correction edits may not propagate correctly to markdown formatting if structure was already inferred"],"requires":["Device capable of running streaming inference (minimum 1GB RAM, dual-core CPU)","Continuous microphone input stream","UI framework supporting incremental text updates (React, Vue, or native)"],"input_types":["real-time audio stream","audio chunks (typically 100-400ms frames)"],"output_types":["streaming text with confidence scores","partial markdown with live updates"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_3","uri":"capability://tool.use.integration.multi.format.note.export.with.ecosystem.integration","name":"multi-format note export with ecosystem integration","description":"Exports transcribed and formatted notes to multiple target formats and platforms including markdown files, Obsidian vault integration, Notion API sync, and plain text. The system implements format-specific adapters that handle platform-specific metadata (Obsidian frontmatter, Notion block structure, Notion database properties) and provides direct API integrations or file-based exports depending on the target platform.","intents":["I want to automatically sync my voice notes to my Obsidian vault","I need to push transcribed notes directly to Notion without manual copy-paste","I want to export notes as markdown files for archival or backup","I need to integrate voice notes into my existing note-taking workflow without switching apps"],"best_for":["Knowledge workers using Obsidian, Logseq, or Roam as primary note systems","Teams using Notion for collaborative documentation and knowledge bases","Researchers maintaining markdown-based research archives","Users wanting to avoid vendor lock-in by exporting to portable formats"],"limitations":["Notion integration requires API token management and may have rate limits (3 requests/second)","Obsidian sync requires local vault path configuration; no cloud-based vault sync without third-party services","Format conversion may lose metadata or custom formatting when exporting to incompatible platforms","No real-time bidirectional sync; exports are one-way from Cleft to target platform"],"requires":["Target platform account (Notion, Obsidian vault, etc.)","API credentials for cloud platforms (Notion API token)","Local file system access for markdown file exports","Network connectivity for cloud platform syncs"],"input_types":["formatted markdown notes","notes with metadata (timestamps, tags, speaker labels)"],"output_types":["markdown files (.md)","Obsidian vault entries with frontmatter","Notion database entries with properties","plain text files"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_4","uri":"capability://search.retrieval.local.note.search.and.retrieval.with.full.text.indexing","name":"local note search and retrieval with full-text indexing","description":"Indexes transcribed notes locally using a full-text search engine (likely SQLite FTS or similar embedded solution) to enable fast keyword-based retrieval without cloud indexing. The system builds an inverted index of note content, timestamps, and metadata, allowing users to search across all captured notes with sub-second latency entirely on their device.","intents":["I want to search across all my voice notes to find a specific topic or decision","I need to retrieve notes from a meeting that happened weeks ago without scrolling","I want to find all notes mentioning a specific person or project name","I need to organize notes by tags or metadata for easy discovery"],"best_for":["Users accumulating large volumes of voice notes (100+ notes) who need efficient retrieval","Researchers and academics building personal knowledge bases from lecture recordings","Professionals tracking decisions and action items across multiple meetings","Teams using shared note repositories who need collaborative search without cloud indexing"],"limitations":["Search accuracy depends on transcription quality; OCR errors or mishearings reduce findability","Full-text indexing adds storage overhead (~20-30% of original note size for index data)","No semantic search or similarity matching; only keyword-based retrieval","Index rebuild required after bulk imports, which may take seconds to minutes depending on note volume"],"requires":["Local storage with sufficient space for index (typically 50MB-500MB for 1000+ notes)","Embedded database engine (SQLite, RocksDB, or equivalent)","No network connectivity required"],"input_types":["transcribed note text","note metadata (timestamps, tags, source)"],"output_types":["search results with relevance ranking","note excerpts with highlighted matches","filtered note lists by tag or date range"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_5","uri":"capability://data.processing.analysis.speaker.identification.and.multi.speaker.note.organization","name":"speaker identification and multi-speaker note organization","description":"Detects and labels different speakers in multi-speaker audio (meetings, interviews, group discussions) by analyzing voice characteristics and assigning speaker labels to transcribed segments. The implementation likely uses speaker embedding models (x-vectors or similar) to cluster voice patterns and assign consistent speaker IDs, then organizes note content by speaker for easier reference and attribution.","intents":["I want to know who said what in a meeting recording without manual labeling","I need to organize meeting notes by speaker for action item assignment","I want to extract quotes attributed to specific people from group discussions","I need to track which team member made which decision or commitment"],"best_for":["Meeting participants capturing multi-speaker discussions and interviews","Journalists and researchers recording interviews with multiple subjects","Teams needing to assign action items based on who committed to them","Legal professionals documenting depositions or witness statements"],"limitations":["Speaker identification accuracy degrades with 4+ simultaneous speakers or heavy background noise","Requires speaker enrollment or training data; cold-start performance on unknown speakers is ~70-80% accurate","Cannot distinguish between speakers with similar voice characteristics (twins, similar accents)","Overlapping speech causes speaker label ambiguity; no built-in speaker separation"],"requires":["Multi-channel or mono audio with distinct speaker segments","Minimum 30 seconds of speech per speaker for reliable identification","Optional: pre-enrollment of known speakers for improved accuracy"],"input_types":["multi-speaker audio","transcribed text with timing information"],"output_types":["transcribed text with speaker labels","markdown notes organized by speaker","speaker-indexed note summaries"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_6","uri":"capability://data.processing.analysis.timestamp.based.note.navigation.and.playback.synchronization","name":"timestamp-based note navigation and playback synchronization","description":"Maintains precise timestamp mappings between transcribed text segments and original audio, enabling users to click on any note text to jump to that point in the recording. The implementation stores segment-level timing metadata (start/end timestamps for each sentence or phrase) and provides playback controls synchronized with note content, allowing users to verify transcription accuracy by reviewing the original audio.","intents":["I want to click on a note segment and hear the exact audio that was transcribed","I need to verify if the transcription is accurate by listening to the original recording","I want to share a specific moment from a meeting by linking to a timestamp","I need to create a transcript with audio references for legal or compliance purposes"],"best_for":["Users verifying transcription accuracy for important meetings or interviews","Legal and compliance professionals creating auditable transcripts with source references","Journalists and researchers documenting interviews with audio evidence","Teams needing to resolve disputes about what was said by referencing original audio"],"limitations":["Timestamp accuracy depends on speech recognition model; streaming models may have ±500ms drift","Requires storing original audio files alongside notes, increasing storage requirements by 5-10MB per hour of audio","Playback synchronization adds UI complexity; seeking to specific timestamps may have 1-2 second latency","No support for audio editing or trimming; users must manage audio files separately"],"requires":["Original audio file stored locally or accessible via file path","Audio playback capability (browser audio API or native player)","Timestamp metadata from transcription pipeline"],"input_types":["transcribed text with segment timestamps","original audio file"],"output_types":["interactive transcript with clickable timestamps","timestamp-linked note segments","shareable audio clip references"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cleft__cap_7","uri":"capability://automation.workflow.offline.first.note.capture.with.automatic.sync.on.reconnection","name":"offline-first note capture with automatic sync on reconnection","description":"Enables voice note capture and transcription entirely offline, storing notes locally and automatically syncing to cloud platforms (Notion, Obsidian Sync, etc.) when network connectivity is restored. The implementation uses local-first architecture with conflict-free replicated data types (CRDTs) or similar patterns to handle offline edits and ensure consistency when syncing, allowing users to work without interruption regardless of connectivity.","intents":["I want to record voice notes in areas without internet (flights, remote locations, underground)","I need my notes to sync automatically when I regain connectivity without manual action","I want to edit notes offline and have changes merge with cloud versions without conflicts","I need to work in environments where network access is unreliable or restricted"],"best_for":["Remote workers and travelers in areas with unreliable connectivity","Professionals in restricted environments (hospitals, secure facilities) with intermittent network access","Users in regions with limited bandwidth who want to minimize data transmission","Teams using distributed note-taking systems requiring offline-first architecture"],"limitations":["Offline edits to notes may conflict with cloud changes; conflict resolution requires user intervention or CRDT-based merging","Sync may take minutes to hours depending on note volume and network speed when connectivity is restored","No real-time collaboration while offline; changes from other users won't appear until sync completes","Storage overhead for conflict-free replication metadata adds 10-20% to local note size"],"requires":["Local storage with sufficient capacity for offline notes (typically 100MB-1GB)","Network connectivity for initial setup and periodic sync (not required for capture)","Cloud platform account for sync targets (Notion, Obsidian Sync, etc.)"],"input_types":["voice audio (offline capture)","manual note edits (offline editing)"],"output_types":["local note files","synced cloud entries after reconnection"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Modern browser with Web Speech API support (Chrome 25+, Edge 79+, Safari 14.1+) or Electron runtime","Minimum 2GB available device RAM for model inference","Microphone hardware with OS-level permissions granted","No internet required but optional for cloud backup features","Completed transcription output from speech-to-text module","Target markdown editor or note-taking app with markdown support","No additional API keys or external dependencies","Device capable of running streaming inference (minimum 1GB RAM, dual-core CPU)","Continuous microphone input stream","UI framework supporting incremental text updates (React, Vue, or native)"],"failure_modes":["Transcription accuracy typically 85-92% vs 95%+ for cloud solutions like Otter.ai due to smaller local models","No real-time speaker diarization or multi-speaker identification without additional processing","Language support limited to models bundled locally; adding new languages requires app updates","Latency for processing longer audio segments (10+ minutes) may exceed cloud solutions due to device CPU constraints","Structural inference relies on heuristics; complex nested hierarchies may require manual adjustment","No context awareness of domain-specific terminology; technical terms may be incorrectly formatted","Ambiguous speech patterns (e.g., 'dash' vs actual list item) may produce incorrect markdown syntax","No support for markdown extensions (tables, footnotes, LaTeX) beyond basic syntax","Streaming models typically have 2-5% lower accuracy than full-audio models due to lack of future context","Real-time correction UI adds complexity; undo/redo for streaming edits requires state management overhead","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":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=cleft","compare_url":"https://unfragile.ai/compare?artifact=cleft"}},"signature":"yTmjVurgGVhJ9ypl9oR5QR2Zp1pB2vC7iN4H6he3ALP/PZAKCJ4QW8a7+qhuvpIVuid023gzWpWKKcbHYBQzCA==","signedAt":"2026-06-20T00:11:16.401Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cleft","artifact":"https://unfragile.ai/cleft","verify":"https://unfragile.ai/api/v1/verify?slug=cleft","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"}}