{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_sleep-ai","slug":"sleep-ai","name":"Sleep.ai","type":"product","url":"https://sleep.ai","page_url":"https://unfragile.ai/sleep-ai","categories":["text-writing"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_sleep-ai__cap_0","uri":"capability://data.processing.analysis.acoustic.pattern.snoring.detection","name":"acoustic-pattern-snoring-detection","description":"Analyzes ambient audio streams captured via device microphone to identify snoring acoustic signatures using machine learning models trained on snoring phoneme patterns. The system processes raw audio in real-time or batch mode, applies noise filtering to isolate snoring frequencies (typically 40-4000 Hz), and classifies detected events with confidence scoring. Detection works without requiring wearable sensors, relying instead on environmental microphone placement near the sleep area.","intents":["I want to know if I'm snoring on specific nights without manual logging","I need objective data on snoring frequency and intensity to discuss with a doctor","I want to track whether lifestyle changes are reducing my snoring episodes"],"best_for":["individuals with suspected mild-to-moderate snoring seeking baseline data","people wanting to quantify snoring before pursuing medical evaluation","users exploring non-invasive monitoring before sleep study referral"],"limitations":["Microphone placement sensitivity — requires consistent positioning within 1-2 meters of sleep area; suboptimal placement reduces detection accuracy by 15-30%","Cannot distinguish snoring from other similar sounds (e.g., heavy breathing, sleep apnea gasping) without additional physiological signals","Ambient noise interference in non-quiet environments reduces specificity; requires relatively quiet bedroom environment (<50 dB baseline)","No clinical validation against polysomnography gold standard; detection accuracy unknown for severe sleep apnea cases"],"requires":["Device with microphone (smartphone, tablet, or dedicated sleep monitor)","Consistent 6-8 hour nightly device placement near sleep area","Quiet bedroom environment with minimal background noise","Sleep.ai mobile app or web platform with audio permissions enabled"],"input_types":["raw audio stream (WAV, MP3, or platform-native codec)","continuous ambient recording (8+ hours per night)"],"output_types":["snoring event timestamps with confidence scores (0-100%)","snoring frequency distribution (events per hour)","intensity classification (mild/moderate/severe per event)"],"categories":["data-processing-analysis","audio-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_1","uri":"capability://data.processing.analysis.sleep.pattern.temporal.analysis","name":"sleep-pattern-temporal-analysis","description":"Aggregates nightly snoring detection events, audio quality metrics, and user-reported sleep data into temporal patterns using time-series analysis and statistical decomposition. The system identifies trends across days/weeks (e.g., Monday snoring worse than Friday), correlates snoring with reported sleep quality scores, and segments sleep into phases based on audio characteristics. Outputs visualizations and statistical summaries showing snoring distribution, variability, and trend direction.","intents":["I want to see if my snoring follows weekly or monthly patterns","I need to understand whether snoring correlates with my reported sleep quality","I want to track whether my snoring is improving, worsening, or stable over time"],"best_for":["users tracking snoring trends over weeks/months to assess intervention effectiveness","individuals identifying environmental or behavioral triggers (e.g., alcohol, sleep position)","people preparing data summaries for physician consultation"],"limitations":["Requires minimum 2-4 weeks of consistent nightly data for meaningful trend detection; insufficient data produces unreliable patterns","Correlation analysis cannot establish causation — temporal association with reported sleep quality does not prove snoring causes poor sleep","Seasonal or hormonal factors not captured without explicit user input; analysis assumes snoring is primary variable","Visualization and statistical output are descriptive only; no predictive modeling of future snoring severity"],"requires":["Minimum 14 consecutive nights of snoring detection data","Optional: user-reported sleep quality ratings (1-10 scale) for correlation analysis","Sleep.ai platform with data aggregation backend (requires cloud sync or local database)"],"input_types":["snoring event timestamps and confidence scores (from acoustic detection)","user sleep quality ratings (optional, 1-10 scale)","device metadata (date, time, ambient noise level)"],"output_types":["time-series charts (snoring events per night over 30+ days)","statistical summaries (mean, median, std dev of nightly snoring count)","correlation coefficients (snoring vs reported sleep quality)","trend indicators (improving/stable/worsening with confidence)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_2","uri":"capability://planning.reasoning.personalized.intervention.recommendation.engine","name":"personalized-intervention-recommendation-engine","description":"Generates tailored snoring mitigation recommendations by analyzing individual sleep patterns, detected snoring characteristics (frequency, intensity, timing), and user profile data (age, reported triggers, lifestyle factors). The system applies rule-based logic and machine learning scoring to rank interventions (positional therapy, nasal strips, sleep hygiene adjustments, medical referral) by estimated relevance and feasibility. Recommendations are prioritized based on evidence strength and user-specific factors rather than generic one-size-fits-all advice.","intents":["I want specific, personalized suggestions for reducing my snoring based on my data","I need to know which interventions are most likely to help me before trying them","I want to understand when I should see a doctor versus trying self-help approaches"],"best_for":["individuals with mild-to-moderate snoring seeking non-medical interventions first","users wanting to prioritize interventions by likelihood of personal effectiveness","people needing guidance on when to escalate to professional sleep medicine"],"limitations":["Recommendations are not clinically validated; no evidence that AI-generated suggestions outperform generic sleep hygiene advice or physician recommendations","Cannot diagnose underlying causes (sleep apnea, nasal obstruction, obesity) — recommendations assume primary snoring without serious pathology","Rule-based prioritization may miss individual factors not captured in user profile (e.g., medication side effects, undiagnosed conditions)","No feedback loop to measure whether user followed recommendations or achieved outcomes; system cannot learn from individual user results"],"requires":["Minimum 7-14 days of snoring detection data to establish baseline patterns","User profile data: age, sex, reported sleep position, alcohol/smoking habits, BMI (optional)","Access to recommendation knowledge base (internal database of intervention evidence)"],"input_types":["snoring pattern summary (frequency, intensity, timing distribution)","user profile (age, lifestyle factors, reported triggers)","user-reported sleep quality and symptoms"],"output_types":["ranked list of interventions (positional therapy, nasal strips, sleep hygiene, medical referral)","confidence/relevance score per intervention (0-100%)","brief explanation of why each intervention is recommended for this user","escalation guidance (when to see a sleep specialist)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_3","uri":"capability://data.processing.analysis.sleep.quality.correlation.analysis","name":"sleep-quality-correlation-analysis","description":"Correlates detected snoring events with user-reported sleep quality ratings and optional wearable/device metrics (heart rate variability, movement, sleep stage estimates) to surface relationships between snoring severity and perceived sleep outcomes. Uses statistical correlation and optional machine learning to weight which snoring characteristics (frequency, intensity, timing) most strongly associate with poor sleep quality in individual users. Outputs correlation coefficients, scatter plots, and narrative insights about snoring's impact on this specific user's sleep.","intents":["I want to understand how much my snoring is actually affecting my sleep quality","I need to know if reducing snoring will likely improve my sleep or if other factors are more important","I want to show my doctor data linking my snoring to poor sleep outcomes"],"best_for":["users seeking evidence that snoring is their primary sleep problem versus other factors","individuals preparing data for physician consultation to justify intervention","people wanting to quantify snoring's personal impact before investing in treatments"],"limitations":["Correlation does not imply causation — strong association between snoring and poor sleep quality does not prove snoring causes the poor sleep","Requires consistent user reporting of sleep quality; subjective ratings introduce bias and noise, reducing correlation reliability","Confounding variables not captured (stress, caffeine, exercise, room temperature) may explain both snoring and poor sleep quality","Sample size typically small (30-90 nights) for statistical significance; correlation estimates have wide confidence intervals","No causal inference capability — cannot determine whether treating snoring will improve sleep quality for this user"],"requires":["Minimum 30 consecutive nights of snoring detection data","User-reported sleep quality ratings (1-10 scale) for each night or morning","Optional: wearable or device metrics (heart rate, movement, sleep stage estimates) for richer correlation analysis"],"input_types":["snoring event counts and intensity per night","user sleep quality ratings (1-10 scale, reported daily)","optional wearable metrics (heart rate variability, movement count, sleep stage percentages)"],"output_types":["Pearson or Spearman correlation coefficient (snoring vs sleep quality)","scatter plot visualization (snoring events vs reported sleep quality)","confidence interval around correlation estimate","narrative summary (e.g., 'Moderate correlation (r=0.52) suggests snoring may contribute to your poor sleep, but other factors likely play a role')"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_4","uri":"capability://automation.workflow.multi.device.audio.synchronization.and.backup","name":"multi-device-audio-synchronization-and-backup","description":"Manages audio recording and snoring detection data across multiple user devices (smartphone, tablet, dedicated sleep monitor) with cloud synchronization and local backup. The system handles device-specific audio codec differences, timestamps across devices with potential clock drift, and ensures data consistency when users switch devices or record from multiple locations. Implements conflict resolution for overlapping recordings and provides fallback to local storage if cloud sync fails.","intents":["I want to record snoring data from my phone when traveling but sync it to my home device","I need my snoring data backed up in case my phone is lost or reset","I want to use multiple devices (phone + tablet) without losing data or creating duplicates"],"best_for":["users who travel frequently or use multiple devices for sleep tracking","individuals wanting cloud backup and offline-first capability","people concerned about data loss from device failure or reset"],"limitations":["Cloud synchronization introduces latency (5-30 seconds) before data is available on secondary devices; real-time cross-device access not supported","Requires internet connectivity for cloud sync; offline recording works but sync is delayed until connection restored","Device clock drift can cause timestamp misalignment across devices; requires periodic time synchronization","Audio codec differences between devices may require transcoding, adding ~100-500ms processing latency per recording","Conflict resolution for overlapping recordings is automatic but may discard data if both devices recorded simultaneously"],"requires":["Sleep.ai account with cloud storage enabled","Internet connectivity (WiFi or cellular) for cloud sync; offline recording supported","Device storage: minimum 500 MB free space per device for local buffering","Consistent device time synchronization (NTP or manual sync recommended)"],"input_types":["audio recordings from multiple devices (various codecs: AAC, MP3, WAV)","device metadata (device ID, timestamp, audio codec, sample rate)"],"output_types":["unified snoring detection data across all devices","consolidated timeline of snoring events (deduplicated)","sync status indicators (synced/pending/failed per device)","local backup files (encrypted, stored on device)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_5","uri":"capability://data.processing.analysis.privacy.preserving.on.device.audio.processing","name":"privacy-preserving-on-device-audio-processing","description":"Processes audio locally on user's device for snoring detection without transmitting raw audio to cloud servers, using on-device machine learning models (TensorFlow Lite, Core ML, or ONNX Runtime). The system extracts acoustic features (spectrograms, MFCCs) locally, runs inference on compressed models, and sends only metadata (snoring event timestamps, confidence scores) to cloud for aggregation and analysis. Raw audio is retained locally with optional encryption and automatic deletion after configurable retention period.","intents":["I want snoring detection without uploading my sleep audio to the cloud","I need privacy guarantees that my raw audio is never transmitted or stored remotely","I want to use Sleep.ai without worrying about audio being used for other purposes"],"best_for":["privacy-conscious users uncomfortable with cloud audio storage","users in regions with strict data protection regulations (GDPR, HIPAA compliance)","individuals wanting offline-first snoring detection with optional cloud sync"],"limitations":["On-device models are smaller and less accurate than cloud-based models; detection accuracy typically 5-15% lower than cloud alternatives","Device computational overhead reduces battery life by 10-20% on smartphones; requires periodic charging","Model updates require app updates; cannot push improved models to users without app release cycle (2-4 week lag vs cloud models updated in real-time)","Limited model capacity on mobile devices; cannot support complex multi-task models (e.g., snoring + sleep stage + apnea detection simultaneously)","Debugging and monitoring of model performance is difficult without telemetry; errors are harder to diagnose"],"requires":["Device with sufficient compute (ARM processor with NEON or equivalent, minimum 2GB RAM)","On-device ML runtime (TensorFlow Lite, Core ML, or ONNX Runtime) compatible with device OS","Local storage: minimum 100-200 MB for model files and audio buffer","Optional: encryption library (AES-256) for local audio encryption"],"input_types":["raw audio stream from device microphone (WAV, PCM, or platform-native codec)"],"output_types":["snoring event metadata (timestamps, confidence scores, intensity classification)","local audio file (encrypted, optional retention period)","sync payload to cloud (metadata only, no raw audio)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_6","uri":"capability://data.processing.analysis.sleep.position.inference.from.audio","name":"sleep-position-inference-from-audio","description":"Infers user's sleep position (supine, prone, left lateral, right lateral) during snoring episodes by analyzing audio characteristics and optional device motion data (accelerometer, gyroscope). The system uses acoustic patterns (snoring intensity and frequency vary by position) and motion signatures to estimate position without requiring wearable sensors. Outputs position-tagged snoring events and position-specific snoring statistics (e.g., 'snoring 3x worse in supine position').","intents":["I want to know if my snoring is worse in certain sleep positions","I need to understand whether positional therapy (e.g., side-sleeping) would help me","I want to track whether I'm maintaining side-sleeping position throughout the night"],"best_for":["users considering positional therapy as a snoring intervention","individuals wanting to understand position-specific snoring patterns","people tracking whether positional therapy interventions are effective"],"limitations":["Position inference from audio alone is unreliable (accuracy ~60-70%); requires device motion data for higher accuracy (80-85%)","Device placement affects motion data quality; phone under pillow or on nightstand produces different accelerometer signals","Cannot distinguish between brief position changes and sustained position; may misclassify short position shifts as primary position","Supine vs prone distinction is difficult from audio alone; lateral position detection is more reliable","No ground truth validation; position estimates are inferred, not measured, and cannot be verified without video or wearable sensors"],"requires":["Device with accelerometer and gyroscope (smartphone or dedicated sleep monitor)","Consistent device placement (e.g., under pillow or on nightstand) for reliable motion data","Minimum 7-14 nights of data for meaningful position-specific pattern analysis"],"input_types":["snoring event audio characteristics (frequency, intensity, duration)","device motion data (accelerometer X/Y/Z, gyroscope rotation rates)","device orientation (from compass or motion fusion)"],"output_types":["position-tagged snoring events (supine/prone/left lateral/right lateral per event)","position-specific snoring statistics (events per hour by position)","position distribution over night (% time in each position)","position-snoring correlation (e.g., 'snoring 2.5x worse in supine position')"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_7","uri":"capability://planning.reasoning.medical.escalation.decision.support","name":"medical-escalation-decision-support","description":"Flags snoring patterns that warrant professional medical evaluation (sleep specialist, ENT, primary care) based on severity thresholds, frequency patterns, and user-reported symptoms. The system applies clinical decision rules (e.g., snoring >5 nights/week + daytime sleepiness = possible sleep apnea) and compares user's snoring characteristics to population-level risk profiles. Generates escalation recommendations with reasoning (e.g., 'Your snoring frequency exceeds 80% of users; recommend sleep study evaluation') and provides guidance on next steps (sleep specialist referral, home sleep apnea test, polysomnography).","intents":["I want to know if my snoring is serious enough to see a doctor","I need guidance on whether I should get a sleep study or see a sleep specialist","I want to understand the difference between simple snoring and sleep apnea based on my data"],"best_for":["users with moderate-to-severe snoring patterns seeking medical guidance","individuals wanting to determine urgency of professional evaluation","people preparing for physician consultation with objective snoring data"],"limitations":["Decision rules are based on population-level evidence, not individual clinical assessment; cannot replace physician evaluation","Cannot diagnose sleep apnea or other sleep disorders; escalation flags are probabilistic risk indicators only","Thresholds may be overly conservative (high false-positive rate) or overly permissive (high false-negative rate) depending on calibration","User-reported symptoms (daytime sleepiness, witnessed apneas) are subjective and may be unreliable; system cannot verify symptom accuracy","No integration with medical records or prior diagnoses; recommendations assume no prior sleep medicine evaluation"],"requires":["Minimum 14-30 days of snoring detection data to establish baseline severity","Optional: user-reported symptoms (daytime sleepiness, witnessed apneas, morning headaches) for richer risk assessment","Access to clinical decision rule knowledge base (internal database of escalation thresholds)"],"input_types":["snoring frequency (events per night, nights per week)","snoring intensity distribution (mild/moderate/severe classification)","user-reported symptoms (daytime sleepiness scale, witnessed apneas, morning headaches)","user demographics (age, BMI, sex) for risk stratification"],"output_types":["escalation recommendation (no action / monitor / see primary care / see sleep specialist / urgent evaluation)","risk assessment (low/moderate/high probability of sleep apnea or other disorder)","reasoning explanation (which factors triggered escalation)","next steps guidance (sleep specialist referral, home sleep test, polysomnography, primary care consultation)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_8","uri":"capability://data.processing.analysis.intervention.effectiveness.tracking","name":"intervention-effectiveness-tracking","description":"Tracks changes in snoring patterns following user-initiated interventions (positional therapy, nasal strips, sleep hygiene changes, medical treatments) by comparing pre-intervention and post-intervention snoring metrics. The system allows users to log intervention start dates and types, then computes statistical comparisons (paired t-tests, effect sizes) between baseline and intervention periods. Outputs effectiveness summaries (e.g., 'Positional therapy reduced snoring by 40% over 2 weeks') and confidence intervals around effect estimates.","intents":["I want to know if the positional therapy pillow I bought is actually helping","I need to track whether my new sleep medication is reducing my snoring","I want to compare the effectiveness of different interventions I've tried"],"best_for":["users testing non-medical interventions (positional therapy, nasal strips, sleep hygiene) and wanting objective feedback","individuals tracking medication or treatment effectiveness over weeks/months","people comparing multiple interventions to identify which works best for them"],"limitations":["Requires minimum 2-4 weeks of pre-intervention baseline and 2-4 weeks of post-intervention data for reliable effect estimation; short-term changes may be noise","Placebo effect not controlled; user expectation bias may inflate perceived effectiveness","Confounding variables not controlled (e.g., weight loss, seasonal changes, stress reduction) may explain snoring changes independent of intervention","Multiple comparisons problem if user tests many interventions; statistical significance thresholds may be inflated","No causal inference; observed snoring reduction may be coincidental rather than caused by intervention"],"requires":["Minimum 14 days of baseline snoring data before intervention","User-logged intervention start date and type (positional therapy, nasal strips, medication, sleep hygiene change, etc.)","Minimum 14 days of post-intervention snoring data for effect estimation","Optional: user-reported compliance/adherence to intervention"],"input_types":["snoring event counts and intensity (pre-intervention baseline period)","intervention metadata (start date, type, description)","snoring event counts and intensity (post-intervention period)","optional user compliance rating (% nights intervention was used)"],"output_types":["pre-intervention snoring summary (mean, std dev, range)","post-intervention snoring summary (mean, std dev, range)","effect size (Cohen's d or percentage change)","statistical test result (paired t-test p-value, confidence interval)","narrative summary (e.g., 'Positional therapy reduced snoring by 40% (95% CI: 20-60%), p=0.02')"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_sleep-ai__cap_9","uri":"capability://text.generation.language.physician.shareable.report.generation","name":"physician-shareable-report-generation","description":"Generates clinical-grade PDF or web-based reports summarizing snoring patterns, trends, and analysis suitable for sharing with healthcare providers. Reports include snoring frequency/intensity statistics, temporal trends, correlation with sleep quality, position-specific patterns, and escalation risk assessment. Formatting follows clinical report conventions (summary, methods, results, interpretation) and includes disclaimers about limitations (not a diagnosis, not a substitute for professional evaluation). Supports customization (date range, metrics included) and secure sharing (encrypted links, password protection).","intents":["I want to show my doctor objective snoring data to support a sleep study referral","I need a professional-looking report to bring to my sleep specialist appointment","I want to share my snoring analysis with my doctor without manually explaining all the data"],"best_for":["users preparing for physician consultation with objective snoring data","individuals seeking sleep study referral and wanting to provide supporting evidence","people wanting to communicate snoring patterns to healthcare providers in standardized format"],"limitations":["Report is informational only; not a medical diagnosis and cannot replace professional sleep medicine evaluation","Physicians may not be familiar with AI-generated snoring reports; clinical credibility depends on provider's trust in Sleep.ai methodology","Report disclaimers may reduce perceived authority; some providers may discount AI-generated data vs polysomnography gold standard","Customization options may be limited; providers may request specific metrics or analyses not included in standard report template","Secure sharing mechanisms (encrypted links) require user to manage access; no audit trail of who accessed report"],"requires":["Minimum 14-30 days of snoring detection data for meaningful report","Optional: user-reported sleep quality ratings and symptoms for richer analysis","Sleep.ai account with report generation feature enabled","PDF generation library (e.g., ReportLab, wkhtmltopdf) or web rendering engine"],"input_types":["snoring detection data (events, intensity, timing)","sleep quality ratings (optional)","user demographics and symptom reports (optional)","user-selected date range and metrics to include"],"output_types":["PDF report (8-12 pages, clinical format)","web-based report (interactive, shareable via secure link)","summary statistics (snoring frequency, intensity, trends)","visualizations (time-series charts, position-specific analysis, correlation plots)","clinical interpretation and escalation recommendations"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Device with microphone (smartphone, tablet, or dedicated sleep monitor)","Consistent 6-8 hour nightly device placement near sleep area","Quiet bedroom environment with minimal background noise","Sleep.ai mobile app or web platform with audio permissions enabled","Minimum 14 consecutive nights of snoring detection data","Optional: user-reported sleep quality ratings (1-10 scale) for correlation analysis","Sleep.ai platform with data aggregation backend (requires cloud sync or local database)","Minimum 7-14 days of snoring detection data to establish baseline patterns","User profile data: age, sex, reported sleep position, alcohol/smoking habits, BMI (optional)","Access to recommendation knowledge base (internal database of intervention evidence)"],"failure_modes":["Microphone placement sensitivity — requires consistent positioning within 1-2 meters of sleep area; suboptimal placement reduces detection accuracy by 15-30%","Cannot distinguish snoring from other similar sounds (e.g., heavy breathing, sleep apnea gasping) without additional physiological signals","Ambient noise interference in non-quiet environments reduces specificity; requires relatively quiet bedroom environment (<50 dB baseline)","No clinical validation against polysomnography gold standard; detection accuracy unknown for severe sleep apnea cases","Requires minimum 2-4 weeks of consistent nightly data for meaningful trend detection; insufficient data produces unreliable patterns","Correlation analysis cannot establish causation — temporal association with reported sleep quality does not prove snoring causes poor sleep","Seasonal or hormonal factors not captured without explicit user input; analysis assumes snoring is primary variable","Visualization and statistical output are descriptive only; no predictive modeling of future snoring severity","Recommendations are not clinically validated; no evidence that AI-generated suggestions outperform generic sleep hygiene advice or physician recommendations","Cannot diagnose underlying causes (sleep apnea, nasal obstruction, obesity) — recommendations assume primary snoring without serious pathology","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"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":"2026-05-24T12:16:33.096Z","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=sleep-ai","compare_url":"https://unfragile.ai/compare?artifact=sleep-ai"}},"signature":"c6d+4aldsftIri51B0QhOAdR78SO3J7kpr3ZKzx2cgMY9VTSW/1KJYUf8Qh36tuF5LvpWBX/fOlEi3F+gjkDDQ==","signedAt":"2026-06-23T10:53:35.469Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/sleep-ai","artifact":"https://unfragile.ai/sleep-ai","verify":"https://unfragile.ai/api/v1/verify?slug=sleep-ai","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"}}