{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_songtell","slug":"songtell","name":"Songtell","type":"product","url":"https://songtell.com","page_url":"https://unfragile.ai/songtell","categories":["voice-audio"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_songtell__cap_0","uri":"capability://text.generation.language.ai.driven.lyric.semantic.interpretation.and.thematic.extraction","name":"ai-driven lyric semantic interpretation and thematic extraction","description":"Analyzes song lyrics using large language models to identify thematic patterns, emotional arcs, narrative structures, and symbolic meanings embedded in text. The system processes raw lyrics through prompt-engineered LLM chains that decompose meaning across multiple dimensions (metaphor, sentiment, storytelling structure, cultural context) and synthesizes interpretations into human-readable narratives. Architecture likely uses few-shot prompting with curated examples of high-quality lyric analysis to guide model outputs toward coherent, educationally-valuable interpretations rather than surface-level summaries.","intents":["I want to understand what a song is really about beyond the literal words","I need to explain the emotional journey and narrative arc of a track to someone else","I'm curious about the deeper themes and symbolism in lyrics I connect with","I want to discover patterns in how artists use metaphor and storytelling across their catalog"],"best_for":["Music students and educators seeking AI-assisted lyric annotation","TikTok-era listeners wanting quick, shareable lyrical breakdowns","Casual music fans exploring emotional resonance of songs they love","Content creators building music-related educational or entertainment content"],"limitations":["AI interpretations risk oversimplifying intentional ambiguity that artists deliberately embed; model may impose singular narrative on polysemous lyrics","Dependent on training data quality and LLM biases; interpretations may reflect model's cultural assumptions rather than artist's intent","No collaborative human expert verification; lacks musicologist or lyricist perspective to validate or contextualize AI readings","Performance degrades on non-English lyrics, slang-heavy or regionally-specific language, and contemporary idioms not well-represented in training data","Stateless analysis per song; no memory of user's previous interpretations or preferences to refine future analyses"],"requires":["Song lyrics in text format (sourced from licensed lyrics database or user input)","LLM API access (likely OpenAI GPT-3.5/4 or similar, requires API key and rate limits)","Internet connectivity for real-time LLM inference","Modern web browser (Chrome, Firefox, Safari, Edge 2020+)"],"input_types":["song title + artist name (for database lookup)","raw lyrics text (user-pasted or API-sourced)","optional user context (genre, mood, personal connection)"],"output_types":["structured interpretation (thematic summary, emotional arc, key symbols)","natural language narrative (paragraph-form lyric breakdown)","shareable formatted text (for social media export)","metadata tags (themes, emotions, narrative elements)"],"categories":["text-generation-language","music-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_1","uri":"capability://search.retrieval.song.database.lookup.and.lyric.retrieval.with.metadata.enrichment","name":"song database lookup and lyric retrieval with metadata enrichment","description":"Maintains or integrates with a licensed song database (likely Genius, AZLyrics, or similar API) to retrieve canonical lyrics, artist metadata, release dates, and genre classifications when a user searches by song title and artist. The system performs fuzzy matching on user input to handle misspellings and variations, caches frequently-accessed lyrics to reduce API calls, and enriches results with structured metadata (artist bio, album context, release year) that contextualizes the lyric analysis. Architecture likely uses a relational database for metadata with Redis or similar for lyric caching, plus fallback to user-provided lyrics if database lookup fails.","intents":["I want to analyze a song but don't want to manually copy-paste lyrics","I need to find the correct version of a song when multiple artists have covered it","I want context about the artist and album to better understand the song's background","I'm looking for songs by a specific artist to analyze multiple tracks at once"],"best_for":["Casual music listeners who want frictionless song lookup","Music educators building lesson plans around specific tracks","Content creators needing quick artist/album context for video scripts"],"limitations":["Database coverage gaps for obscure tracks, independent releases, and non-English music; users may need to manually paste lyrics for niche songs","Licensing restrictions on lyrics API may limit which songs are available (some artists/labels restrict API access)","Fuzzy matching may return incorrect song if multiple similar titles exist (e.g., 'Love' by multiple artists)","Metadata enrichment depends on database completeness; some artists may have incomplete or outdated bio information","API rate limits on lyrics service may cause delays during high-traffic periods"],"requires":["Integration with licensed lyrics API (Genius, AZLyrics, or similar) with valid API credentials","Database of song metadata (artist, album, release date, genre) — likely relational DB (PostgreSQL, MySQL)","Caching layer (Redis or Memcached) for frequently-accessed lyrics","Internet connectivity for real-time API calls to lyrics service"],"input_types":["song title (string, user-entered or search query)","artist name (string, user-entered or search query)","optional: album name or release year (for disambiguation)"],"output_types":["canonical lyrics text","artist metadata (name, bio, image, verified status)","album metadata (title, release date, cover art, genre)","track metadata (duration, explicit flag, chart position)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_2","uri":"capability://data.processing.analysis.emotional.sentiment.and.mood.classification.from.lyrics","name":"emotional sentiment and mood classification from lyrics","description":"Applies multi-label sentiment analysis and emotion classification models to lyrics to extract emotional dimensions (joy, sadness, anger, nostalgia, introspection, etc.) and mood tags. The system likely uses a fine-tuned transformer model (BERT, RoBERTa) trained on music-specific sentiment datasets or a pre-built emotion classification API, producing confidence scores for each emotion category. Results are aggregated across song sections (verse, chorus, bridge) to map emotional arcs and identify emotional peaks, enabling visualization of how mood evolves throughout the track.","intents":["I want to know what emotions this song is designed to evoke","I'm looking for songs that match my current mood or emotional state","I want to understand why I feel a certain way when listening to this track","I need to categorize songs by emotional tone for a playlist or study session"],"best_for":["Listeners seeking mood-based song discovery and playlist curation","Mental health or wellness apps integrating music recommendations","Music educators teaching emotional literacy and artistic expression","Content creators building mood-driven playlists or soundtracks"],"limitations":["Emotion classification is inherently subjective; model may misread irony, sarcasm, or intentionally contradictory lyrics (e.g., upbeat melody with sad lyrics)","Limited to emotions in training data; rare or culturally-specific emotional expressions may be misclassified or missed","No distinction between artist intent and listener perception; a song designed to be sad may be experienced as cathartic or empowering by different listeners","Confidence scores may be low for ambiguous or mixed-emotion lyrics, reducing reliability of mood classification","Performance degrades on slang, dialect, or non-standard English that wasn't well-represented in training data"],"requires":["Pre-trained emotion classification model (fine-tuned BERT, RoBERTa, or proprietary music emotion API)","Lyrics text as input (from database or user-provided)","Optional: section-level lyrics annotation (verse/chorus/bridge boundaries) for emotional arc mapping","GPU or inference server for real-time model inference (or API access to hosted model)"],"input_types":["lyrics text (full song or section-by-section)","optional: song metadata (genre, tempo, artist) as context for model"],"output_types":["emotion labels with confidence scores (e.g., 'sadness: 0.87, introspection: 0.72')","primary mood classification (single dominant emotion)","emotional arc visualization (mood over song duration)","mood tags for filtering/discovery (e.g., 'melancholic', 'uplifting', 'introspective')"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_3","uri":"capability://text.generation.language.shareable.interpretation.export.and.social.media.formatting","name":"shareable interpretation export and social media formatting","description":"Generates formatted, shareable versions of AI-generated lyric interpretations optimized for social media platforms (Twitter, Instagram, TikTok, Reddit). The system creates multiple export formats: plain text (for copy-paste), formatted cards with artist/song metadata and interpretation excerpt, quote-style graphics with typography, and platform-specific snippets (Twitter thread templates, Instagram caption templates, TikTok text overlay formats). Export pipeline includes URL shortening, hashtag suggestion based on song genre/mood, and optional watermarking with Songtell branding.","intents":["I want to share this song interpretation with friends on social media","I need a formatted graphic or card to post on Instagram with the interpretation","I want to start a Twitter thread discussing this song's meaning","I'm creating a TikTok about this song and need formatted text overlays"],"best_for":["TikTok-era listeners and content creators sharing music insights","Music educators distributing lyric analysis to students via social channels","Music bloggers and influencers creating shareable content","Community managers building engagement around music discussions"],"limitations":["Character limits on platforms (Twitter 280 chars, Instagram captions ~2200 chars) may truncate interpretations; system must intelligently summarize or split across multiple posts","Graphic generation requires design templates and font licensing; limited customization options may make exports feel generic or repetitive","Platform-specific formatting may break or render poorly on some devices (e.g., Instagram Stories text may be unreadable on small screens)","Watermarking and branding may reduce user willingness to share if it feels overly promotional","No built-in analytics to track shares or engagement; users can't measure impact of exported content"],"requires":["Completed AI interpretation (from lyric analysis capability)","Song metadata (artist, title, album art URL)","Template library for social media formats (Twitter, Instagram, TikTok, Reddit)","Optional: graphic design library (Canva API, custom image generation service) for card/graphic generation","URL shortening service (bit.ly, TinyURL) for sharing links back to full interpretation"],"input_types":["AI-generated interpretation text (from semantic interpretation capability)","song metadata (artist, title, album art, genre, mood tags)","optional: user-selected platform (Twitter, Instagram, TikTok, Reddit)","optional: custom hashtags or mentions"],"output_types":["plain text export (copy-paste ready)","formatted social media post (platform-specific)","graphic card (PNG/JPG with interpretation text and metadata)","Twitter thread template (numbered tweets with thread markers)","Instagram caption template (with hashtag suggestions)","TikTok text overlay format (short, punchy text for video overlay)","shareable URL (link back to full interpretation on Songtell)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_4","uri":"capability://automation.workflow.freemium.access.tier.management.with.feature.gating","name":"freemium access tier management with feature gating","description":"Implements a freemium business model with feature-based access control, likely using a subscription/authentication layer to gate premium features (unlimited analyses, advanced export formats, ad-free experience, API access). The system tracks user quota (analyses per day/month), stores user preferences and history, and serves ads or upsell prompts to free tier users. Architecture likely uses a user authentication service (Auth0, Firebase Auth), a subscription management system (Stripe, Paddle), and a feature flag service to conditionally enable/disable capabilities based on user tier.","intents":["I want to try the tool without paying to see if it's useful for me","I need unlimited access to analyze multiple songs for my music class","I want to remove ads and get a cleaner experience","I'm building a music app and need API access to Songtell's analysis"],"best_for":["Individual music listeners exploring the tool with low commitment","Music educators and institutions needing bulk analysis capacity","Developers integrating Songtell analysis into third-party apps","Content creators monetizing music-related content"],"limitations":["Free tier quota limits (e.g., 5 analyses/day) may frustrate power users and create friction for legitimate use cases","Ad-supported free tier may degrade user experience and drive churn if ads are intrusive or irrelevant","Paywall placement may feel aggressive if upsell prompts appear too frequently or at critical moments","API access likely restricted to premium tier, limiting developer adoption and ecosystem growth","No family or group plans; individual subscription model may be expensive for teams or classrooms"],"requires":["User authentication system (Auth0, Firebase Auth, or custom OAuth)","Subscription management platform (Stripe, Paddle, or custom billing)","Feature flag service (LaunchDarkly, Unleash, or custom) for tier-based access control","Ad network integration (Google AdSense, Mediavine, or similar) for free tier monetization","Database to track user quotas, subscription status, and usage metrics"],"input_types":["user email/password or social login credentials","subscription tier selection (free, pro, enterprise)","payment information (credit card, PayPal) for premium tiers"],"output_types":["user account with tier status and quota information","feature access matrix (which capabilities are available for user's tier)","usage dashboard (analyses used, quota remaining, renewal date)","billing information (subscription status, next billing date, payment method)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_5","uri":"capability://memory.knowledge.user.interpretation.history.and.personalization.tracking","name":"user interpretation history and personalization tracking","description":"Maintains a user-specific history of analyzed songs and generated interpretations, enabling personalization and discovery features. The system stores user analysis history (songs analyzed, interpretations generated, timestamps), user preferences (favorite genres, mood preferences, analysis depth), and implicit signals (which interpretations users engage with, which they share). This data is used to personalize future analyses (e.g., adjusting interpretation depth or focus based on user's past preferences), recommend similar songs, and surface trending interpretations within the user's network. Architecture likely uses a user profile database with relational storage for history and a recommendation engine (collaborative filtering or content-based) for personalization.","intents":["I want to see all the songs I've analyzed and revisit past interpretations","I want the tool to learn my music taste and tailor interpretations to my preferences","I want to discover new songs similar to ones I've already analyzed","I want to see what interpretations are trending among users with similar taste"],"best_for":["Regular users building a personal music analysis library","Music enthusiasts seeking personalized discovery recommendations","Casual listeners who want the tool to adapt to their taste over time","Community-driven music discussion platforms"],"limitations":["Privacy concerns: storing user analysis history and preferences requires clear data policies and GDPR/CCPA compliance","Cold start problem: new users have no history, so personalization is ineffective until sufficient data is collected","Recommendation quality depends on user base size; small user bases may produce poor recommendations due to sparse data","Implicit signals (engagement, shares) may be noisy or misleading; users may share interpretations they disagree with","Personalization may create filter bubbles, limiting exposure to diverse music genres or interpretations"],"requires":["User authentication system (to identify and track individual users)","Database to store user history (songs analyzed, interpretations, timestamps, engagement metrics)","User preference model (explicit preferences from settings, implicit signals from behavior)","Recommendation engine (collaborative filtering, content-based, or hybrid approach)","Privacy infrastructure (data encryption, retention policies, GDPR/CCPA compliance)"],"input_types":["user ID (from authentication system)","song analysis events (song analyzed, interpretation generated, timestamp)","engagement events (interpretation viewed, shared, liked, saved)","explicit preferences (favorite genres, mood preferences, analysis depth)"],"output_types":["user analysis history (list of songs analyzed with timestamps and interpretations)","personalized interpretation (adjusted tone/depth based on user preferences)","recommended songs (similar to user's past analyses)","trending interpretations (popular within user's taste community)","user profile (taste summary, favorite genres, analysis frequency)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_6","uri":"capability://text.generation.language.multi.language.lyric.analysis.with.translation.fallback","name":"multi-language lyric analysis with translation fallback","description":"Extends lyric analysis capabilities to non-English songs by either using multilingual LLM models (e.g., GPT-3.5/4 with multilingual training) or implementing a translation-then-analyze pipeline that translates lyrics to English before semantic interpretation. The system detects song language automatically (via language detection model or user input), routes to appropriate analysis model, and optionally preserves original-language context in the interpretation. For languages with limited LLM support, the system falls back to machine translation (Google Translate, DeepL) with quality warnings to users.","intents":["I want to analyze songs in languages I don't speak fluently","I'm curious about the meaning of non-English songs I listen to","I need to understand how meaning changes when songs are translated","I want to analyze my country's music without language barriers"],"best_for":["International music listeners exploring non-English music","Music educators teaching world music and cultural context","Multilingual users wanting analysis in their native language","Global music platforms serving diverse user bases"],"limitations":["Translation quality varies significantly by language pair; machine translation may lose nuance, idioms, and cultural context that are essential to meaning","Multilingual LLM models may have lower performance on non-English languages due to training data imbalance (more English data than other languages)","Lyric database coverage is heavily English-biased; non-English songs may not be in the database, requiring user-provided lyrics","Cultural context and wordplay may not translate; puns, rhyme schemes, and cultural references may be lost in translation","Performance degrades on low-resource languages (e.g., Icelandic, Swahili) where training data is sparse"],"requires":["Multilingual LLM model (GPT-3.5/4 with multilingual training, or separate models per language family)","Language detection model (langdetect, TextCat, or similar) to identify song language","Machine translation service (Google Translate, DeepL, Microsoft Translator) for fallback translation","Multilingual lyrics database or user-provided lyrics in original language","Optional: human translators or native speakers for quality assurance on high-value languages"],"input_types":["lyrics in non-English language (from database or user-provided)","optional: language code (ISO 639-1) if auto-detection fails","optional: user's preferred analysis language (e.g., analyze Spanish lyrics but explain in English)"],"output_types":["interpretation in user's preferred language","optional: side-by-side original and translated lyrics","optional: translation quality warning (if machine translation was used)","optional: cultural context notes (idioms, references, wordplay that may not translate)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_7","uri":"capability://data.processing.analysis.comparative.multi.song.interpretation.and.thematic.analysis","name":"comparative multi-song interpretation and thematic analysis","description":"Enables analysis of multiple songs in sequence to identify thematic patterns, stylistic evolution, and narrative arcs across an artist's discography or a curated playlist. The system analyzes each song individually, then applies cross-song comparison to extract common themes, emotional patterns, lyrical devices, and narrative threads. Results are presented as a thematic map showing how themes evolve over time, which songs share emotional or narrative DNA, and how an artist's songwriting has changed. Architecture likely uses a multi-step pipeline: individual song analysis → theme extraction → cross-song comparison (using embeddings or semantic similarity) → visualization.","intents":["I want to understand how an artist's themes and style have evolved across their albums","I'm curious about the narrative arc of a concept album or song cycle","I want to find thematic connections between songs that aren't obviously related","I'm studying an artist's songwriting and need to identify recurring motifs and patterns"],"best_for":["Music students and scholars analyzing artist discographies","Music journalists writing retrospectives or career analyses","Fans creating deep-dive content about their favorite artists","Educators teaching music history and artistic evolution"],"limitations":["Computational cost scales with number of songs; analyzing full discographies (100+ songs) may be slow or expensive","Cross-song comparison quality depends on individual song analysis quality; errors compound across multiple songs","Thematic patterns may be coincidental or superficial; system can't distinguish between intentional artistic choices and random similarities","No context about artist's life events, influences, or production timeline; thematic analysis may miss external factors driving changes","Visualization of thematic maps may be overwhelming for large discographies; users may struggle to extract actionable insights"],"requires":["Individual song analysis capability (from semantic interpretation)","Embedding model (Word2Vec, GloVe, or transformer embeddings) for semantic similarity comparison","Clustering or graph analysis algorithm to identify thematic groups","Visualization library (D3.js, Plotly, or similar) for thematic map rendering","Optional: timeline data (album release dates, artist biography) for contextual analysis"],"input_types":["list of song titles and artist names (or URLs to individual interpretations)","optional: album or playlist context (release dates, curator notes)","optional: analysis parameters (time period, thematic focus, comparison depth)"],"output_types":["thematic map (graph showing connections between songs and shared themes)","evolution timeline (how themes change across artist's career)","thematic clusters (groups of songs sharing similar themes or emotional tone)","comparative summary (narrative describing thematic patterns and evolution)","motif analysis (recurring lyrical devices, metaphors, or narrative structures)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_songtell__cap_8","uri":"capability://text.generation.language.lyric.annotation.and.collaborative.interpretation.markup","name":"lyric annotation and collaborative interpretation markup","description":"Allows users to highlight specific lyrics, add inline annotations, and create collaborative notes on interpretations. The system supports line-by-line markup where users can tag specific lyrics with themes, emotions, or questions, and optionally share annotations with other users or groups. The architecture likely uses a rich text editor with annotation support (similar to Genius annotations), a database to store user annotations with line-level references, and optional collaboration features (shared workspaces, comment threads on annotations). Annotations are indexed and searchable, enabling discovery of how different users interpret the same lyrics.","intents":["I want to highlight and annotate specific lyrics that stand out to me","I'm collaborating with classmates on a song analysis project and need to share notes","I want to see how other users interpret the same lyrics differently","I'm building a study guide for a song and need to organize my notes by theme"],"best_for":["Music students and educators collaborating on lyric analysis projects","Music scholars and researchers building annotated lyric databases","Community-driven music discussion platforms","Fans creating detailed liner notes or study guides"],"limitations":["Annotation quality depends on user expertise; non-expert annotations may be inaccurate or misleading","Spam and vandalism risk if annotations are publicly editable; requires moderation infrastructure","Collaborative features may create coordination overhead for small groups; not worth the complexity for solo users","Searchability of annotations depends on indexing quality; poor tagging may make annotations hard to discover","Privacy concerns if annotations are public; users may not want to share personal interpretations or questions"],"requires":["Rich text editor with annotation support (similar to Genius, Hypothesis, or custom implementation)","Database to store annotations with line-level references (song ID, line number, user ID, text, tags)","User authentication system (to identify annotation authors)","Optional: collaboration features (shared workspaces, permissions, comment threads)","Optional: moderation tools (flagging, deletion, user reputation) for public annotations","Search and indexing infrastructure (Elasticsearch or similar) for annotation discovery"],"input_types":["lyrics text with line-level references","user annotation text (free-form or structured with tags)","optional: theme/emotion tags for annotation","optional: collaboration settings (private, shared with group, public)"],"output_types":["annotated lyrics (with inline user notes and highlights)","annotation index (searchable collection of all annotations on a song)","collaborative workspace (shared annotations and discussion threads)","annotation export (formatted notes for study guides or papers)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Song lyrics in text format (sourced from licensed lyrics database or user input)","LLM API access (likely OpenAI GPT-3.5/4 or similar, requires API key and rate limits)","Internet connectivity for real-time LLM inference","Modern web browser (Chrome, Firefox, Safari, Edge 2020+)","Integration with licensed lyrics API (Genius, AZLyrics, or similar) with valid API credentials","Database of song metadata (artist, album, release date, genre) — likely relational DB (PostgreSQL, MySQL)","Caching layer (Redis or Memcached) for frequently-accessed lyrics","Internet connectivity for real-time API calls to lyrics service","Pre-trained emotion classification model (fine-tuned BERT, RoBERTa, or proprietary music emotion API)","Lyrics text as input (from database or user-provided)"],"failure_modes":["AI interpretations risk oversimplifying intentional ambiguity that artists deliberately embed; model may impose singular narrative on polysemous lyrics","Dependent on training data quality and LLM biases; interpretations may reflect model's cultural assumptions rather than artist's intent","No collaborative human expert verification; lacks musicologist or lyricist perspective to validate or contextualize AI readings","Performance degrades on non-English lyrics, slang-heavy or regionally-specific language, and contemporary idioms not well-represented in training data","Stateless analysis per song; no memory of user's previous interpretations or preferences to refine future analyses","Database coverage gaps for obscure tracks, independent releases, and non-English music; users may need to manually paste lyrics for niche songs","Licensing restrictions on lyrics API may limit which songs are available (some artists/labels restrict API access)","Fuzzy matching may return incorrect song if multiple similar titles exist (e.g., 'Love' by multiple artists)","Metadata enrichment depends on database completeness; some artists may have incomplete or outdated bio information","API rate limits on lyrics service may cause delays during high-traffic periods","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"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":"2026-05-24T12:16:33.096Z","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=songtell","compare_url":"https://unfragile.ai/compare?artifact=songtell"}},"signature":"RO7yBwWml0dTr2EY0gpIvhGZPbZ8GXugCsK6ZVees1gPJ3PfPK5pre6Y9z1+NPR0mIcbGRgeyQBs4cXbs/RbBg==","signedAt":"2026-06-21T04:14:17.861Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/songtell","artifact":"https://unfragile.ai/songtell","verify":"https://unfragile.ai/api/v1/verify?slug=songtell","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"}}