{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_essence-app","slug":"essence-app","name":"Essence App","type":"product","url":"https://www.theessence.app","page_url":"https://unfragile.ai/essence-app","categories":["app-builders"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_essence-app__cap_0","uri":"capability://data.processing.analysis.menstrual.cycle.phase.tracking.and.inference","name":"menstrual-cycle-phase-tracking-and-inference","description":"Tracks menstrual cycle phases (menstruation, follicular, ovulation, luteal) through user input or integration with cycle-tracking APIs, then infers current phase and predicts future phases using hormonal cycle models. The system maintains a temporal state machine that maps calendar dates to cycle phases and uses historical cycle length data to improve prediction accuracy for irregular cycles.","intents":["I want to know what phase of my cycle I'm in right now and plan my week accordingly","I need to predict my cycle phases for the next 3 months to schedule important events","I want to sync my productivity tool with my actual cycle data from another app like Clue or Flo"],"best_for":["menstruating individuals with regular or semi-regular cycles seeking biological self-awareness","women building personal productivity systems that account for hormonal variation","teams building cycle-aware wellness platforms"],"limitations":["Accuracy degrades significantly for users with PCOS, irregular cycles (>7 day variance), or hormonal contraceptive use that suppresses ovulation","Requires manual cycle start input or API integration; cannot infer cycle phase from biomarkers (temperature, LH surge) without additional hardware","Prediction accuracy drops beyond 2-3 months without continuous cycle data updates","No support for non-binary or trans menstruating individuals' cycle variations"],"requires":["User-provided menstrual cycle start date or historical cycle data (minimum 2-3 cycles for pattern detection)","Optional: API credentials for third-party cycle tracking apps (Clue, Flo, Apple Health, Google Fit)","Regular manual updates or automated sync to maintain prediction accuracy"],"input_types":["date (cycle start date)","structured data (cycle length history, flow intensity)","API integration (third-party cycle tracker data)"],"output_types":["structured data (current phase, phase dates, confidence score)","calendar visualization (phase timeline)","predictive data (upcoming phase dates)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_1","uri":"capability://planning.reasoning.cycle.phase.adaptive.task.recommendation.engine","name":"cycle-phase-adaptive-task-recommendation-engine","description":"Maps tasks and work types to optimal cycle phases based on hormonal research (e.g., high-focus analytical work during follicular/ovulation, creative brainstorming during luteal, rest during menstruation). Uses a task classification system and phase-to-capability mapping to recommend task prioritization and scheduling. The engine adjusts recommendations based on user feedback and self-reported energy/focus levels across phases.","intents":["I want to schedule my deep work and meetings around when I'm naturally most focused","I need to know what types of tasks I should prioritize this week based on my cycle phase","I want the app to suggest when to do creative work vs. analytical work based on my hormonal patterns"],"best_for":["knowledge workers (engineers, designers, writers) seeking to align task types with natural energy patterns","individuals with ADHD or executive function challenges who benefit from phase-aware task structuring","teams building cycle-aware project management tools"],"limitations":["Recommendations are based on population-level hormonal research; individual variation is significant and not all users experience predicted patterns","No machine learning personalization — recommendations don't adapt to individual user performance data across cycles","Lacks integration with actual task performance metrics (completion rate, quality) to validate whether phase-aligned scheduling improves outcomes","Cannot account for external stressors, sleep debt, or other confounding variables that override hormonal effects"],"requires":["Accurate cycle phase data (from capability 1)","Task input with optional categorization (work type, focus level required)","User feedback mechanism to report energy/focus levels (optional but improves recommendations)"],"input_types":["structured data (task name, task type/category, deadline, focus level required)","unstructured text (task description that system parses for implicit type)","user feedback (energy level, focus level, task completion success)"],"output_types":["structured recommendations (task priority, suggested phase, reasoning)","calendar visualization (tasks color-coded by recommended phase)","natural language explanation (why this task is recommended for this phase)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_2","uri":"capability://planning.reasoning.wellness.recommendation.personalization.by.cycle.phase","name":"wellness-recommendation-personalization-by-cycle-phase","description":"Generates personalized wellness recommendations (exercise type, intensity, nutrition focus, sleep targets, stress management) tailored to each cycle phase based on hormonal research. Uses a recommendation engine that maps phase-specific physiology (e.g., higher metabolism in luteal, better recovery in follicular) to specific wellness interventions. Tracks user adherence and self-reported outcomes to refine recommendations over time.","intents":["I want to know what type of exercise is best for my cycle phase right now","I need nutrition and supplement recommendations that match my hormonal state","I want personalized sleep and stress management guidance that accounts for my cycle"],"best_for":["fitness-conscious individuals seeking to optimize training around hormonal cycles","people managing hormonal symptoms (PMS, PMDD, cramps) through lifestyle interventions","wellness coaches and personal trainers building cycle-aware client programs"],"limitations":["Recommendations lack clinical validation — no randomized controlled trials demonstrating that cycle-synced wellness interventions outperform standard recommendations","Cannot provide medical-grade personalization without integration with biomarkers (heart rate variability, cortisol, progesterone levels)","Assumes user has capacity to modify exercise, nutrition, and sleep; doesn't account for socioeconomic constraints or access to resources","No integration with wearable data (Oura, Whoop, Apple Watch) to validate recommendations against actual physiological responses"],"requires":["Accurate cycle phase data (from capability 1)","Optional: user profile data (fitness level, dietary restrictions, sleep baseline, stress level)","Optional: wearable device integration for biometric validation"],"input_types":["structured data (cycle phase, fitness level, dietary preferences, health conditions)","user feedback (adherence to recommendations, self-reported outcomes, symptom severity)","optional biometric data (heart rate, sleep duration, activity level from wearables)"],"output_types":["structured recommendations (exercise type/intensity, nutrition focus, supplements, sleep target, stress technique)","natural language guidance (explanation of why this recommendation for this phase)","progress tracking (adherence rate, self-reported outcome improvements)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_3","uri":"capability://data.processing.analysis.symptom.tracking.and.pattern.detection","name":"symptom-tracking-and-pattern-detection","description":"Collects user-reported symptoms (cramps, bloating, mood changes, energy, focus, sleep quality) across cycle phases and detects patterns using time-series analysis and statistical correlation. Identifies which symptoms cluster in which phases, tracks severity trends over multiple cycles, and flags potential cycle-related conditions (PMDD, endometriosis indicators). Uses a symptom ontology to normalize user input and a temporal correlation engine to find phase-symptom associations.","intents":["I want to track my PMS symptoms and see which ones are most severe in which phase","I need to identify patterns in my mood, energy, and focus across multiple cycles","I want to know if my symptoms suggest a condition like PMDD or endometriosis that I should discuss with my doctor"],"best_for":["individuals managing cycle-related symptoms seeking self-awareness and pattern recognition","people with suspected PMDD or endometriosis looking for data to bring to healthcare providers","researchers studying cycle-symptom correlations in populations"],"limitations":["Self-reported symptom data is subjective and prone to recall bias; no objective biomarker validation","Pattern detection is correlational, not causal — cannot determine whether symptoms are cycle-driven or caused by external stressors, sleep, diet, or other factors","Cannot diagnose medical conditions; flagging potential PMDD/endometriosis requires clinical validation and is not a substitute for medical evaluation","Requires consistent symptom logging over 2-3 cycles to detect meaningful patterns; sparse data produces unreliable correlations"],"requires":["User symptom input (daily or phase-based logging)","Minimum 2-3 complete cycle data points for pattern detection","Optional: integration with cycle phase data (from capability 1) for automatic phase-symptom correlation"],"input_types":["structured data (symptom name, severity 1-10, phase, date)","unstructured text (symptom description that system parses)","optional biometric data (mood from wearables, sleep from sleep tracker)"],"output_types":["structured analysis (symptom-phase correlation, severity trends, pattern summary)","visualizations (symptom heatmap by phase, trend graphs over cycles)","natural language insights (e.g., 'Your cramps are most severe in menstrual phase, peaking on day 2')","health flags (potential PMDD/endometriosis indicators with disclaimer)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_4","uri":"capability://automation.workflow.cycle.aware.calendar.and.scheduling.integration","name":"cycle-aware-calendar-and-scheduling-integration","description":"Integrates cycle phase data into calendar systems (Google Calendar, Outlook, Apple Calendar) by creating phase-labeled events and color-coding days by cycle phase. Provides smart scheduling suggestions that flag suboptimal meeting/deadline placements (e.g., scheduling high-stakes presentations during low-energy luteal phase) and recommends rescheduling. Syncs with task recommendations (capability 2) to visualize task-phase alignment on calendar.","intents":["I want my calendar to show my cycle phases so I can see at a glance what phase I'm in","I want the app to warn me if I'm scheduling an important meeting during a low-energy phase","I want to see my tasks and meetings color-coded by cycle phase to plan my week better"],"best_for":["professionals managing complex schedules who want cycle awareness integrated into existing calendar workflows","individuals coordinating with teams where cycle-aware scheduling could reduce stress","people using multiple calendar systems (work, personal, shared) seeking unified cycle visualization"],"limitations":["Calendar integration requires OAuth permissions and API access; not all calendar systems support custom event properties or color-coding","Smart scheduling suggestions are advisory only — cannot automatically reschedule events without user permission due to calendar conflicts and team coordination","Privacy concern: cycle data in shared calendars or team scheduling systems may expose sensitive health information; requires careful permission management","No integration with meeting invitations or attendee availability; cannot optimize for multiple attendees' cycles"],"requires":["Accurate cycle phase data (from capability 1)","Calendar system API access (Google Calendar API, Microsoft Graph, CalDAV)","User permission to read/write calendar events","Optional: task data (from capability 2) for task-phase visualization"],"input_types":["structured data (cycle phase dates, task data, calendar events)","calendar event metadata (title, time, duration, importance level)"],"output_types":["calendar events (phase-labeled, color-coded by phase)","scheduling recommendations (warnings about suboptimal placements, rescheduling suggestions)","calendar visualizations (week/month view with phase overlay)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_5","uri":"capability://planning.reasoning.hr.recruiting.cycle.aware.candidate.matching","name":"hr-recruiting-cycle-aware-candidate-matching","description":"Applies cycle-aware insights to HR recruiting by analyzing candidate profiles and matching them to roles based on phase-aligned strengths (e.g., recommending analytical candidates for detail-oriented roles, creative candidates for brainstorming roles). Uses candidate skill data and phase-aware capability mapping to suggest optimal interview timing and team composition. Includes bias detection to flag when cycle-based recommendations might reinforce stereotypes.","intents":["I want to schedule interviews with candidates at times that play to their natural strengths","I want to build teams with complementary cycle-phase strengths for better collaboration","I want to ensure my hiring process doesn't inadvertently discriminate based on cycle-related assumptions"],"best_for":["HR teams and recruiters seeking to optimize hiring and team composition using cycle-aware insights","organizations building inclusive hiring practices that account for biological diversity","teams experimenting with cycle-aware team dynamics and collaboration"],"limitations":["Highly speculative and ethically fraught — no evidence that cycle-aware candidate matching improves hiring outcomes or team performance","Risk of reinforcing gender stereotypes (e.g., 'women in follicular phase are better at X') despite good intentions","Requires candidate consent to collect cycle data; privacy and discrimination risks are significant","No validation that cycle-aware team composition actually improves collaboration or productivity","Potential legal liability under employment discrimination laws if cycle data is used in hiring decisions"],"requires":["Candidate profile data (skills, experience, optional cycle phase data with explicit consent)","Role requirements and skill mapping","Bias detection framework to flag discriminatory recommendations"],"input_types":["structured data (candidate skills, experience, cycle phase if provided)","role requirements (key skills, team dynamics needed)"],"output_types":["candidate-role matching scores (with phase-aware component)","interview timing recommendations","team composition suggestions","bias flags (warnings about potential discriminatory recommendations)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_6","uri":"capability://safety.moderation.cycle.data.privacy.and.consent.management","name":"cycle-data-privacy-and-consent-management","description":"Manages sensitive cycle health data with privacy-first architecture including granular consent controls, data encryption at rest and in transit, and audit logging for all data access. Implements role-based access control for features that share cycle data (calendar integration, HR recruiting) and provides data export/deletion capabilities. Uses differential privacy techniques to anonymize cycle data for analytics while preserving individual insights.","intents":["I want to control exactly what cycle data is shared with my calendar, HR system, or other integrations","I need to delete all my cycle data from the app and ensure it's not retained","I want to see an audit log of who accessed my cycle data and when"],"best_for":["individuals concerned about privacy of sensitive health data","organizations handling employee cycle data seeking HIPAA/GDPR compliance","users in jurisdictions with strict health data privacy regulations"],"limitations":["Privacy controls are only as strong as the underlying infrastructure; no guarantee against data breaches or unauthorized access","Differential privacy techniques reduce data utility — anonymized data may not support personalized recommendations","Third-party integrations (calendar, HR systems) may have weaker privacy controls; data privacy is only as strong as the weakest integration","Audit logging adds overhead and storage requirements; may impact app performance"],"requires":["Secure data storage infrastructure (encrypted database, secure key management)","HTTPS/TLS for all data transmission","Privacy policy and terms of service clearly explaining data handling","Optional: HIPAA/GDPR compliance certifications"],"input_types":["user consent preferences (which features can access which data)","access requests (data export, deletion)"],"output_types":["consent confirmation (what data is shared with which features)","audit logs (access history with timestamps and user/system identifiers)","data exports (user's complete cycle data in portable format)","deletion confirmation (verification that data has been removed)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_7","uri":"capability://data.processing.analysis.multi.cycle.trend.analysis.and.forecasting","name":"multi-cycle-trend-analysis-and-forecasting","description":"Analyzes productivity, wellness, and symptom data across multiple menstrual cycles (3+ cycles) to identify individual patterns and trends using time-series decomposition and statistical modeling. Forecasts future cycle phases, expected symptom severity, and predicted productivity patterns with confidence intervals. Detects anomalies (unusual symptom severity, phase length changes) that may indicate health changes. Uses ARIMA or exponential smoothing models for phase-length forecasting and regression models for symptom-phase relationships.","intents":["I want to see how my productivity and symptoms have changed over the past 6 months","I want to forecast my cycle phases and expected energy levels for the next 3 months","I want to know if my cycle length or symptom patterns have changed significantly"],"best_for":["individuals seeking long-term cycle pattern understanding and self-awareness","people tracking health changes over time (e.g., after medication changes, lifestyle changes)","researchers analyzing population-level cycle patterns"],"limitations":["Requires consistent data collection over 3+ cycles (minimum 3 months); sparse or inconsistent data produces unreliable forecasts","Forecasting accuracy degrades beyond 2-3 months due to natural cycle variability and external factors","Statistical models assume cycle stationarity; cannot account for major life changes (stress, illness, medication) that alter patterns","Anomaly detection may produce false positives if thresholds are not carefully calibrated to individual baselines"],"requires":["Historical cycle data spanning minimum 3 complete cycles (3 months)","Consistent symptom and productivity logging across cycles","Optional: external data (stress level, sleep, exercise) to improve forecasting accuracy"],"input_types":["structured time-series data (cycle phase dates, symptom severity, productivity scores, wellness metrics)","optional external data (stress, sleep, exercise, medication changes)"],"output_types":["trend analysis (cycle length trends, symptom severity trends, productivity trends over time)","forecasts (predicted cycle phases, symptom severity, productivity for next 1-3 months with confidence intervals)","anomaly alerts (significant deviations from individual baseline patterns)","visualizations (trend graphs, forecast plots with uncertainty bands)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_essence-app__cap_8","uri":"capability://text.generation.language.natural.language.cycle.insights.generation","name":"natural-language-cycle-insights-generation","description":"Generates natural language summaries and insights from cycle data using templated text generation and LLM-based summarization. Converts structured cycle, symptom, and productivity data into readable narratives (e.g., 'Your follicular phase is typically 10 days long and your most productive phase for creative work'). Uses rule-based templates for common insights and optional LLM integration for personalized narrative generation. Provides weekly/monthly summaries and actionable recommendations in conversational tone.","intents":["I want a weekly summary of my cycle phase, symptoms, and productivity in plain English","I want personalized insights about my cycle patterns explained in a way I can understand","I want actionable recommendations for this week based on my cycle phase and recent patterns"],"best_for":["users preferring narrative insights over data visualizations","individuals seeking conversational health coaching integrated with cycle data","non-technical users who find charts and statistics overwhelming"],"limitations":["LLM-based generation may hallucinate or produce inaccurate insights if training data is limited; requires careful prompt engineering and validation","Template-based generation is limited to predefined insight types; cannot generate novel insights beyond templates","Natural language summaries may oversimplify complex patterns or mask important nuances in the data","No fact-checking mechanism to validate generated insights against medical literature"],"requires":["Structured cycle, symptom, and productivity data (from capabilities 1-4)","Optional: LLM API access (OpenAI, Anthropic, or local model) for advanced summarization","Insight templates for common patterns"],"input_types":["structured data (cycle phase, symptoms, productivity scores, wellness metrics)"],"output_types":["natural language summaries (weekly/monthly cycle and productivity summaries)","personalized insights (pattern descriptions, trend explanations)","actionable recommendations (specific suggestions for the current week/phase)","conversational explanations (why certain patterns are observed)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["User-provided menstrual cycle start date or historical cycle data (minimum 2-3 cycles for pattern detection)","Optional: API credentials for third-party cycle tracking apps (Clue, Flo, Apple Health, Google Fit)","Regular manual updates or automated sync to maintain prediction accuracy","Accurate cycle phase data (from capability 1)","Task input with optional categorization (work type, focus level required)","User feedback mechanism to report energy/focus levels (optional but improves recommendations)","Optional: user profile data (fitness level, dietary restrictions, sleep baseline, stress level)","Optional: wearable device integration for biometric validation","User symptom input (daily or phase-based logging)","Minimum 2-3 complete cycle data points for pattern detection"],"failure_modes":["Accuracy degrades significantly for users with PCOS, irregular cycles (>7 day variance), or hormonal contraceptive use that suppresses ovulation","Requires manual cycle start input or API integration; cannot infer cycle phase from biomarkers (temperature, LH surge) without additional hardware","Prediction accuracy drops beyond 2-3 months without continuous cycle data updates","No support for non-binary or trans menstruating individuals' cycle variations","Recommendations are based on population-level hormonal research; individual variation is significant and not all users experience predicted patterns","No machine learning personalization — recommendations don't adapt to individual user performance data across cycles","Lacks integration with actual task performance metrics (completion rate, quality) to validate whether phase-aligned scheduling improves outcomes","Cannot account for external stressors, sleep debt, or other confounding variables that override hormonal effects","Recommendations lack clinical validation — no randomized controlled trials demonstrating that cycle-synced wellness interventions outperform standard recommendations","Cannot provide medical-grade personalization without integration with biomarkers (heart rate variability, cortisol, progesterone levels)","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":"2026-05-24T12:16:30.284Z","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=essence-app","compare_url":"https://unfragile.ai/compare?artifact=essence-app"}},"signature":"bl7TxIMztnqAtKVI7sB1OoDGFJOV1N0FHqNvpJqmljL7g/x4Z3vmuwB/ssEkIQWC+scFAK4CdNmTHZzgyGQxCw==","signedAt":"2026-06-21T10:13:37.247Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/essence-app","artifact":"https://unfragile.ai/essence-app","verify":"https://unfragile.ai/api/v1/verify?slug=essence-app","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"}}