{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_screentime","slug":"screentime","name":"Screentime","type":"product","url":"https://screentime.monitup.com","page_url":"https://unfragile.ai/screentime","categories":["automation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_screentime__cap_0","uri":"capability://data.processing.analysis.app.usage.pattern.tracking.and.aggregation","name":"app-usage-pattern-tracking-and-aggregation","description":"Continuously monitors and logs application usage across the user's device(s) by hooking into OS-level process/window tracking APIs (likely using accessibility frameworks on macOS/Windows or usage stats APIs on mobile), aggregating raw telemetry into time-series data indexed by app, category, and timestamp. The system normalizes heterogeneous app metadata (app names, bundle IDs, window titles) into a unified taxonomy to enable cross-device pattern analysis.","intents":["I want to see exactly how much time I spend in each app without manually tracking it","I need historical app usage data to identify trends in my distraction patterns over weeks or months","I want to understand which apps are stealing my focus during work hours vs. leisure time"],"best_for":["Remote workers and knowledge workers seeking objective data on their digital habits","Productivity-conscious individuals who want quantified metrics before attempting behavior change"],"limitations":["Requires OS-level permissions (accessibility access on macOS, usage stats on Android/iOS) which users may revoke, breaking data continuity","Window-title-based tracking on desktop can misattribute time if multiple apps share similar window names or if users keep apps open but inactive","Mobile app tracking may undercount usage for apps running in background or split-screen scenarios depending on OS version","No built-in deduplication of duplicate app entries across devices, requiring manual taxonomy alignment"],"requires":["macOS 10.12+ or Windows 10+ or iOS 12+ or Android 5.0+","Accessibility permissions granted to Screentime app","Active internet connection for cloud sync (if multi-device support enabled)","Continuous background process execution (may impact battery on mobile)"],"input_types":["OS-level process/window events","Device accessibility APIs","Usage stats snapshots from OS"],"output_types":["time-series JSON with app name, duration, timestamp","aggregated usage summaries (daily, weekly, monthly)","structured app metadata (category, bundle ID, icon)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_1","uri":"capability://planning.reasoning.behavioral.pattern.analysis.with.ai.insights","name":"behavioral-pattern-analysis-with-ai-insights","description":"Applies machine learning (likely clustering, anomaly detection, or time-series forecasting models) to the aggregated usage data to identify behavioral patterns such as distraction cycles, peak productivity windows, app-switching frequency, and correlation between app usage and time-of-day or day-of-week. The system generates natural-language insights by mapping detected patterns to a rule-based or LLM-powered recommendation engine that contextualizes findings relative to the user's stated goals.","intents":["I want AI to tell me WHEN I'm most productive and which apps are actually helping vs. hurting that","I need to understand my distraction triggers—what sequences of apps lead me down rabbit holes","I want personalized suggestions for how to restructure my day based on my actual behavior patterns"],"best_for":["Knowledge workers who want data-driven insights beyond raw metrics","Users seeking to understand causality in their digital habits, not just correlation"],"limitations":["Pattern detection accuracy depends on data volume; users with <1 week of history will receive generic or unreliable insights","No transparency on whether recommendations are template-based heuristics or truly personalized ML outputs; unclear if system learns user preferences over time","Requires sufficient behavioral variance to detect patterns; users with highly consistent routines may receive redundant insights","Cannot distinguish between intentional, goal-aligned app usage and distraction without explicit user labeling of app categories/goals","Seasonal or event-driven behavior changes (e.g., project deadlines, vacation) may be misinterpreted as anomalies rather than context-dependent patterns"],"requires":["Minimum 7 days of continuous usage data for reliable pattern detection","User-defined productivity goals or app categorization (work vs. distraction) for contextualized insights","Internet connectivity for cloud-based ML inference (if not running locally)"],"input_types":["time-series app usage data","user-defined app categories or goals","optional: calendar events, task lists for context"],"output_types":["natural-language insight summaries","structured pattern metadata (peak hours, distraction triggers, app correlations)","actionable recommendations with rationale"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_10","uri":"capability://automation.workflow.focus.mode.scheduling.with.context.awareness","name":"focus-mode-scheduling-with-context-awareness","description":"Allows users to define recurring or one-time focus blocks (e.g., 'Monday-Friday 9am-12pm', 'during calendar events tagged #deepwork') with automatic enforcement of blocking rules, notification suppression, and do-not-disturb activation. The system integrates with calendar data to automatically detect focus-time-compatible windows and can suggest optimal focus blocks based on detected productivity patterns (e.g., 'you're most productive 10am-12pm, so we recommend a focus block then').","intents":["I want to automatically enforce strict focus rules during specific time blocks without manual activation","I need focus blocks that automatically adapt to my calendar (e.g., no focus block during meetings)","I want the system to suggest optimal focus times based on when I'm actually most productive"],"best_for":["Users with structured schedules who can commit to recurring focus blocks","Knowledge workers with calendar-driven workflows (meetings, sprints, deadlines)"],"limitations":["Requires accurate calendar data; if calendar is incomplete or out-of-date, focus blocks may conflict with actual meetings","No built-in conflict resolution if a focus block overlaps with an urgent meeting or task; users may feel trapped","Suggestion engine for optimal focus times is likely rule-based (e.g., 'suggest 2-hour blocks in the morning'); no personalization based on individual productivity patterns","No integration with task management to prioritize focus blocks for high-priority work","Focus block enforcement is all-or-nothing; no granular control (e.g., 'allow work apps but block social media')"],"requires":["Calendar integration (Google Calendar, Outlook, etc.)","User-defined focus block schedule or calendar event tags"],"input_types":["calendar events and availability","user-defined focus block schedule","optional: productivity pattern data for suggestions"],"output_types":["active focus blocks with enforcement","automatic blocking rule activation during focus time","suggested focus blocks based on productivity patterns"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_2","uri":"capability://automation.workflow.distraction.app.blocking.with.scheduling","name":"distraction-app-blocking-with-scheduling","description":"Implements OS-level or middleware-based app blocking that prevents execution or foreground access to user-designated distraction apps during specified time windows (e.g., 9am-12pm work blocks). The system likely uses process termination, window-focus interception, or notification suppression depending on OS capabilities; scheduling logic supports recurring patterns (weekdays only, specific hours) and can be triggered manually or by detected behavioral patterns from the AI analysis engine.","intents":["I want to automatically block social media apps during my designated focus hours without relying on willpower","I need to enforce app blocking rules that adapt to my detected distraction patterns, not just static schedules","I want to whitelist certain apps while blocking others, with exceptions for emergencies"],"best_for":["Remote workers with weak self-discipline who need hard technical enforcement rather than soft nudges","Teams or managers seeking to enforce productivity policies on company devices"],"limitations":["Requires elevated OS permissions (admin/root) which users may be unwilling to grant; can be circumvented by disabling the app or rebooting into safe mode","No built-in emergency override mechanism; users may feel trapped if they legitimately need a blocked app (e.g., messaging for urgent work communication)","Blocking granularity is limited to app-level; cannot block specific features within an app (e.g., YouTube app but only block the feed, not uploads)","No integration with calendar or task-management systems to automatically adjust blocking rules based on meeting context","Relies on user compliance to define blocking rules; no AI-driven automatic rule suggestion based on detected distraction patterns"],"requires":["Administrator/root access on the device","macOS 10.12+ or Windows 10+ or iOS 12+ or Android 5.0+","Screentime app running continuously in background"],"input_types":["user-defined app blocklist","scheduling rules (time windows, recurrence patterns)","optional: AI-detected distraction patterns for auto-rule generation"],"output_types":["block enforcement events (app prevented from launching)","user notifications when blocked app is accessed","audit log of blocked attempts"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_3","uri":"capability://planning.reasoning.productivity.goal.definition.and.tracking","name":"productivity-goal-definition-and-tracking","description":"Provides a UI for users to define productivity goals (e.g., 'spend <2 hours/day on social media', 'maintain 4 hours of uninterrupted focus work daily') and maps these goals to app categories and time thresholds. The system continuously evaluates actual usage against goal thresholds, generating progress metrics and alerts when users exceed limits; goals can be time-bound (daily, weekly) and support exceptions or grace periods.","intents":["I want to set a specific target for how much time I should spend in work apps vs. distraction apps","I need to track my progress toward productivity goals and get alerted when I'm off track","I want to define different goals for different days or contexts (e.g., stricter limits on weekends)"],"best_for":["Goal-oriented individuals who respond well to quantified targets and progress tracking","Users who want to gamify productivity with metrics and achievement milestones"],"limitations":["Goals are static once defined; no built-in learning to adjust targets based on historical feasibility (e.g., if user consistently exceeds a goal, system doesn't suggest raising the threshold)","No integration with external goal-tracking systems (OKRs, Jira, Notion) to align digital habits with broader work objectives","Alert fatigue risk if goals are too aggressive; no adaptive alert throttling based on user response patterns","Cannot distinguish between intentional goal-aligned usage and accidental overage; no context-aware goal relaxation for legitimate work scenarios"],"requires":["User-defined goal thresholds (time limits, app categories)","Continuous usage data collection to evaluate progress"],"input_types":["goal definition (app category, time limit, recurrence)","usage data time-series"],"output_types":["progress metrics (% of goal achieved)","alerts when thresholds exceeded","historical goal performance summaries"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_4","uri":"capability://data.processing.analysis.multi.device.usage.synchronization.and.aggregation","name":"multi-device-usage-synchronization-and-aggregation","description":"Aggregates usage data from multiple devices (phone, tablet, laptop) into a unified dashboard, allowing users to see total screen time across all devices and identify which devices contribute most to distraction. The system synchronizes blocking rules and goals across devices so that a blocking rule defined on desktop automatically applies to mobile, and maintains a consistent app taxonomy across heterogeneous platforms (iOS, Android, macOS, Windows).","intents":["I want to see my total screen time across all my devices, not just one","I need to apply the same focus rules to my phone and laptop so I can't just switch devices to circumvent blocking","I want to understand which device is my biggest distraction source"],"best_for":["Multi-device users (phone + laptop + tablet) who want holistic productivity insights","Users seeking to prevent device-switching as a distraction workaround"],"limitations":["Requires cloud sync infrastructure; offline devices will have stale data until reconnection, creating gaps in cross-device insights","App taxonomy normalization across platforms is imperfect; same app may have different names or bundle IDs on iOS vs. Android, requiring manual mapping","Blocking rule synchronization has latency; a rule defined on desktop may take minutes to propagate to mobile, creating a window for circumvention","No built-in conflict resolution if user defines conflicting rules on different devices (e.g., different blocking schedules for the same app)","Privacy/security risk of centralizing usage data from all devices in cloud; requires robust encryption and access controls"],"requires":["Screentime app installed on all devices to be synchronized","User account and cloud sync enabled","Internet connectivity on all devices","Consistent app taxonomy mapping across platforms (manual setup or auto-detection)"],"input_types":["usage data from multiple devices","blocking rules and goals defined on any device","app metadata from heterogeneous platforms"],"output_types":["unified usage dashboard aggregating all devices","cross-device blocking rule enforcement","synchronized goal progress across devices"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_5","uri":"capability://planning.reasoning.distraction.trigger.identification.and.correlation.analysis","name":"distraction-trigger-identification-and-correlation-analysis","description":"Uses time-series analysis and correlation detection to identify sequences of apps that typically precede distraction episodes (e.g., 'opening Slack → checking email → browsing news' is a common distraction cascade). The system builds a directed graph of app transitions and applies statistical significance testing to identify non-random patterns; results are surfaced as 'distraction triggers' with confidence scores and recommendations to break the chain.","intents":["I want to know what app sequences typically lead me down distraction rabbit holes","I need to identify the first app in a distraction cascade so I can block it before the chain starts","I want to understand the causal chain of my distractions, not just which apps I use most"],"best_for":["Users with complex, multi-app distraction patterns who need to understand causality","Individuals seeking to break distraction chains by intervening at the trigger point"],"limitations":["Requires substantial historical data (weeks to months) to detect statistically significant patterns; early users will see unreliable or no triggers","Cannot distinguish between intentional app sequences (e.g., checking Slack for work) and distraction cascades without explicit user labeling","Correlation does not imply causation; the system may identify spurious patterns (e.g., 'opening Slack at 3pm always leads to distraction' when the real cause is afternoon fatigue, not Slack)","Trigger detection is static; no online learning to adapt as user behavior changes or as they successfully break distraction chains","No integration with contextual data (calendar, tasks, meetings) to explain why certain app sequences are sometimes productive and sometimes distracting"],"requires":["Minimum 2-4 weeks of continuous usage data for reliable trigger detection","Optional: user labeling of distraction episodes for supervised learning"],"input_types":["time-series app usage with precise timestamps","optional: user-labeled distraction episodes"],"output_types":["distraction trigger sequences (app chains with confidence scores)","correlation analysis (which app pairs frequently co-occur)","recommendations to break trigger chains"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_6","uri":"capability://tool.use.integration.productivity.workflow.integration.and.action.suggestions","name":"productivity-workflow-integration-and-action-suggestions","description":"Integrates with external productivity tools (calendar, task managers, email) via APIs or webhooks to contextualize app usage within the user's actual work (e.g., 'you spent 3 hours in Slack during your focused work block scheduled in Outlook'). The system generates actionable suggestions tied to specific workflows, such as 'block Slack during your 2-hour deep work block on Tuesday' or 'schedule a 15-minute email check at 3pm instead of constant checking', and can automatically create calendar blocks or task reminders to implement suggestions.","intents":["I want my app blocking and focus rules to automatically adapt to my calendar and scheduled work blocks","I need suggestions that are tied to my actual tasks and meetings, not generic productivity advice","I want to automatically create focus time blocks in my calendar based on when I'm most productive"],"best_for":["Knowledge workers with structured calendars and task management systems","Teams using shared calendars and task tracking (Jira, Asana, Notion) who want to align digital habits with project deadlines"],"limitations":["Requires API access to external tools (calendar, task manager); not all tools expose sufficient APIs for deep integration","No built-in conflict resolution if calendar events conflict with blocking rules (e.g., a meeting scheduled during a focus block)","Suggestions are generated by rule-based heuristics or LLM; no feedback loop to learn which suggestions users actually implement and find valuable","Privacy risk of sharing calendar and task data with Screentime; requires explicit user consent and secure data handling","Assumes calendar and task data are accurate and up-to-date; stale or incomplete data will generate irrelevant suggestions"],"requires":["Integration with at least one external tool (Google Calendar, Outlook, Jira, Asana, Notion, etc.)","API keys or OAuth tokens for external services","User permission to read calendar and task data"],"input_types":["calendar events and scheduled blocks","task metadata (priority, deadline, project)","app usage data contextualized with calendar/task data"],"output_types":["context-aware action suggestions","automatically created calendar blocks or task reminders","productivity metrics tied to specific projects or tasks"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_7","uri":"capability://automation.workflow.real.time.distraction.alerts.and.nudges","name":"real-time-distraction-alerts-and-nudges","description":"Monitors active app usage in real-time and triggers alerts or nudges when the user enters a detected distraction pattern or exceeds a goal threshold. Alerts can be configured as notifications, sounds, or haptic feedback, and can include contextual information (e.g., 'You've been in social media for 45 minutes; your goal is 30 minutes/day'). The system supports 'snooze' functionality to defer alerts and learns user response patterns to optimize alert timing and frequency.","intents":["I want to be alerted in real-time when I'm about to exceed my productivity goals","I need gentle nudges to break distraction chains before they escalate, not just blocking","I want to customize alert frequency and intensity based on my preferences"],"best_for":["Users who respond well to real-time feedback and gentle nudges rather than hard blocking","Individuals seeking to build awareness of their distraction patterns through repeated, contextual reminders"],"limitations":["Alert fatigue risk if thresholds are too aggressive or alerts are too frequent; no built-in adaptive throttling based on user response","Alerts require the app to be running and monitoring in real-time, consuming CPU and battery resources","No integration with focus modes or do-not-disturb settings; alerts may interrupt important work","Snooze functionality can be abused to indefinitely defer alerts; no built-in limit on snooze count or duration","Effectiveness depends on user compliance; alerts are only useful if users act on them, and there's no mechanism to enforce action"],"requires":["Screentime app running continuously in foreground or background","Notification permissions granted to the app","Goal thresholds or distraction patterns defined"],"input_types":["real-time app usage data","goal thresholds and distraction patterns","user alert preferences (frequency, intensity, channels)"],"output_types":["real-time notifications or alerts","contextual nudge messages","alert response logs (dismissed, snoozed, acted upon)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_8","uri":"capability://data.processing.analysis.productivity.insights.dashboard.and.reporting","name":"productivity-insights-dashboard-and-reporting","description":"Provides a web or mobile dashboard that visualizes app usage patterns, goal progress, distraction triggers, and AI-generated insights in charts, heatmaps, and summary cards. The system supports multiple time granularities (hourly, daily, weekly, monthly) and allows users to drill down into specific time periods or apps for detailed analysis. Reports can be exported as PDFs or shared with managers/coaches for accountability or feedback.","intents":["I want to see a visual summary of my productivity trends and how I'm progressing toward my goals","I need to drill down into specific days or apps to understand what happened during a distraction spike","I want to export a report to share with my manager or coach for feedback on my productivity habits"],"best_for":["Visual learners who respond well to charts and metrics","Users seeking accountability through shared reporting with managers or coaches","Individuals who want to track long-term productivity trends"],"limitations":["Dashboard design can obscure important insights if visualizations are poorly chosen (e.g., pie charts for time-series data)","Drill-down functionality may be limited; users may not be able to filter by arbitrary dimensions (e.g., 'show me app usage during meetings')","Report export is static; no built-in ability to schedule recurring reports or set up automated sharing","Privacy risk if reports are shared with managers; no built-in controls to redact sensitive app usage (e.g., personal apps during work hours)","No comparative analytics (e.g., 'your productivity is X% better than last week') which could provide motivation"],"requires":["Historical usage data (minimum 1 day for basic dashboard, 1 week for meaningful trends)","Web browser or mobile app for dashboard access"],"input_types":["aggregated usage data","goal definitions and progress","distraction patterns and insights"],"output_types":["interactive dashboard with charts and heatmaps","exportable PDF reports","shareable summary cards"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_screentime__cap_9","uri":"capability://data.processing.analysis.app.categorization.and.custom.taxonomy.management","name":"app-categorization-and-custom-taxonomy-management","description":"Allows users to manually categorize apps (work, distraction, health, social, etc.) and create custom categories tailored to their workflow. The system uses these categories to aggregate usage metrics, define goals, and generate insights (e.g., 'total work app usage', 'distraction time'). Categories can be hierarchical (e.g., 'work > communication > Slack') and support rules like 'block all apps in the distraction category during focus hours'.","intents":["I want to group apps into meaningful categories so I can set goals and blocking rules at the category level, not per-app","I need to create custom categories that match my specific workflow (e.g., 'client work', 'internal meetings', 'deep focus')","I want to see aggregate metrics for app categories, not just individual apps"],"best_for":["Users with diverse app ecosystems who need flexible categorization","Teams with custom app stacks (internal tools, specialized software) that don't fit standard categories"],"limitations":["Manual categorization is labor-intensive and error-prone; users may miscategorize apps or forget to categorize new apps","No built-in suggestion engine to auto-categorize apps based on app metadata or user behavior; users must manually assign every app","Hierarchical categories add complexity; unclear how the system handles conflicts (e.g., if an app is in both 'work' and 'distraction')","Categories are user-specific; no sharing of category taxonomies across teams or organizations, leading to inconsistency","No versioning or audit trail for category changes; users may lose track of why an app was categorized a certain way"],"requires":["User-defined app categories","Ability to assign apps to categories (manual UI or bulk import)"],"input_types":["app metadata (name, bundle ID, icon)","user-defined category names and hierarchy"],"output_types":["categorized app inventory","aggregate usage metrics by category","category-based goals and blocking rules"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["macOS 10.12+ or Windows 10+ or iOS 12+ or Android 5.0+","Accessibility permissions granted to Screentime app","Active internet connection for cloud sync (if multi-device support enabled)","Continuous background process execution (may impact battery on mobile)","Minimum 7 days of continuous usage data for reliable pattern detection","User-defined productivity goals or app categorization (work vs. distraction) for contextualized insights","Internet connectivity for cloud-based ML inference (if not running locally)","Calendar integration (Google Calendar, Outlook, etc.)","User-defined focus block schedule or calendar event tags","Administrator/root access on the device"],"failure_modes":["Requires OS-level permissions (accessibility access on macOS, usage stats on Android/iOS) which users may revoke, breaking data continuity","Window-title-based tracking on desktop can misattribute time if multiple apps share similar window names or if users keep apps open but inactive","Mobile app tracking may undercount usage for apps running in background or split-screen scenarios depending on OS version","No built-in deduplication of duplicate app entries across devices, requiring manual taxonomy alignment","Pattern detection accuracy depends on data volume; users with <1 week of history will receive generic or unreliable insights","No transparency on whether recommendations are template-based heuristics or truly personalized ML outputs; unclear if system learns user preferences over time","Requires sufficient behavioral variance to detect patterns; users with highly consistent routines may receive redundant insights","Cannot distinguish between intentional, goal-aligned app usage and distraction without explicit user labeling of app categories/goals","Seasonal or event-driven behavior changes (e.g., project deadlines, vacation) may be misinterpreted as anomalies rather than context-dependent patterns","Requires accurate calendar data; if calendar is incomplete or out-of-date, focus blocks may conflict with actual meetings","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":"2026-05-24T12:16:33.095Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=screentime","compare_url":"https://unfragile.ai/compare?artifact=screentime"}},"signature":"GOgeRr9jLgyzNTNBeAvy51p9txsa5iC1ArL1iXQjOVbQTAzMj+LMfNzinNCRNiUn/8m3iheNNdb6utSUtm6GCw==","signedAt":"2026-06-22T06:45:23.372Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/screentime","artifact":"https://unfragile.ai/screentime","verify":"https://unfragile.ai/api/v1/verify?slug=screentime","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"}}