{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-ayush0chaudhary--blurr","slug":"ayush0chaudhary--blurr","name":"blurr","type":"workflow","url":"https://heypanda.org","page_url":"https://unfragile.ai/ayush0chaudhary--blurr","categories":["automation"],"tags":["accessibility","agent","ai-assistant","ai-automation","app","automation","browser-use","doubao","mobile-use","open-doubao","operator","voice-assistant"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-ayush0chaudhary--blurr__cap_0","uri":"capability://automation.workflow.voice.command.driven.ui.automation.with.wake.word.activation","name":"voice-command-driven ui automation with wake-word activation","description":"Blurr implements a multi-layer voice activation system combining manual tap-based triggering via DeltaSymbolView, persistent wake-word detection using Picovoice engine in EnhancedWakeWordService, and Android RoleManager integration for default assistant role. Voice input is captured, transcribed via speech-to-text, and routed to the conversational agent service which interprets natural language intent and triggers the AI agent execution framework. The system maintains always-on listening capability without requiring explicit app focus.","intents":["I want to trigger complex multi-step tasks on my phone using only voice commands without touching the screen","I need the phone to respond to a custom wake word and execute tasks hands-free","I want voice commands to work even when the app is in the background or another app is active"],"best_for":["accessibility-focused users with mobility constraints","developers building voice-first mobile automation workflows","teams implementing hands-free device operation for enterprise scenarios"],"limitations":["Wake-word detection runs continuously, consuming ~5-8% CPU baseline on modern devices","Speech recognition accuracy depends on ambient noise levels and microphone quality","Picovoice engine requires on-device model loading, adding ~50MB to app size","Voice commands must be interpreted through LLM context window, limiting task complexity to ~4000 token descriptions"],"requires":["Android 8.0+ (API level 26) for Accessibility Service APIs","Microphone permission (RECORD_AUDIO)","Picovoice SDK integration with access key","Google Gemini API key for LLM-based intent interpretation","Text-to-speech engine (system TTS or integrated alternative)"],"input_types":["audio stream (voice commands)","natural language text (transcribed speech)","UI state from accessibility tree"],"output_types":["action sequences (touch, scroll, text input)","voice responses (TTS output)","task execution logs"],"categories":["automation-workflow","voice-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_1","uri":"capability://data.processing.analysis.screen.state.perception.via.accessibility.tree.extraction","name":"screen-state perception via accessibility tree extraction","description":"Blurr's perception layer leverages Android's AccessibilityService to read the complete UI hierarchy (AccessibilityNodeInfo tree) from the currently visible screen, extracting semantic information about interactive elements, text content, and layout structure. This accessibility tree is serialized into a structured representation that the LLM can reason about, enabling the agent to understand which buttons, text fields, and interactive components are available without relying on image recognition or OCR. The system captures both the visual state and the semantic meaning of UI elements.","intents":["I need the AI agent to understand what's currently on screen and identify clickable elements programmatically","I want to extract structured information about UI elements without parsing pixel-level image data","I need the agent to navigate complex nested UI hierarchies (dialogs, menus, nested lists) by understanding element relationships"],"best_for":["developers building cross-app automation agents on Android","accessibility tool builders requiring semantic UI understanding","teams automating workflows across apps with dynamic UI layouts"],"limitations":["AccessibilityService requires explicit user permission and cannot be granted programmatically","Some apps explicitly block accessibility tree access for security/DRM reasons (banking apps, games with anti-cheat)","Accessibility tree extraction adds ~200-500ms latency per screen read depending on UI complexity","Custom UI frameworks or non-standard Android views may not expose semantic information to accessibility tree","Tree depth is limited; deeply nested UI hierarchies (>15 levels) may be truncated by Android framework"],"requires":["Android 5.0+ (API level 21) for AccessibilityService","ACCESSIBILITY_SERVICE permission declared and enabled by user","Target app must expose UI elements via AccessibilityNodeInfo (standard Android components do this automatically)","Sufficient device memory to parse large accessibility trees (complex apps may have 500+ nodes)"],"input_types":["AccessibilityNodeInfo tree from Android framework","screen dimensions and display metrics","current window state and focus information"],"output_types":["structured JSON representation of UI hierarchy","element coordinates and bounds","semantic labels and content descriptions","interactive element types and properties"],"categories":["data-processing-analysis","perception-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_10","uri":"capability://data.processing.analysis.firebase.analytics.and.crash.reporting.with.usage.tracking","name":"firebase analytics and crash reporting with usage tracking","description":"Blurr integrates Firebase Analytics to track user behavior, task execution patterns, and feature usage. Firebase Crashlytics captures runtime errors and exceptions, providing crash reports and stack traces for debugging. The system logs key events (task execution, permission grants, subscription changes) to Firebase for analytics. This data enables the developers to understand user behavior, identify bugs, and optimize the product. Firebase also provides real-time dashboards for monitoring app health and user engagement.","intents":["I want to understand how users are using the app and which features are most popular","I need to track crashes and errors to improve app stability","I want to monitor task execution success rates and identify problematic workflows"],"best_for":["app developers building data-driven products","teams optimizing user engagement and retention","builders debugging production issues and crashes"],"limitations":["Firebase analytics adds network overhead and battery drain (background data transmission)","Privacy-sensitive users may disable analytics; data is not representative of all users","Crash reports are anonymized; cannot directly identify affected users without additional logging","Firebase data retention is limited (typically 90 days for free tier); long-term trend analysis requires data export","Analytics events must be carefully designed; poorly chosen events generate noise and waste quota"],"requires":["Firebase project setup with Analytics enabled","Firebase SDK integrated into app","INTERNET permission for data transmission","Google Analytics account for dashboard access","Event schema definition (which events to track)"],"input_types":["user events (task execution, permission grant, subscription change)","runtime exceptions and crashes","performance metrics (latency, success rate)","user properties (subscription status, language, device)"],"output_types":["analytics dashboards","crash reports with stack traces","user behavior insights","performance metrics and trends"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_11","uri":"capability://data.processing.analysis.local.data.persistence.with.encrypted.storage.for.sensitive.information","name":"local data persistence with encrypted storage for sensitive information","description":"Blurr stores user data locally using Android's persistence mechanisms (likely SharedPreferences for simple data, Room database for complex data structures). Sensitive information (API keys, authentication tokens, user preferences) is encrypted using Android's EncryptedSharedPreferences or similar encryption libraries. The system manages data lifecycle (creation, update, deletion) and handles data migration across app versions. Local storage enables offline operation for certain features and reduces dependency on cloud services.","intents":["I want my automation workflows and preferences to persist across app sessions","I need sensitive data (API keys, tokens) to be encrypted and protected from unauthorized access","I want the app to work offline for basic features without requiring cloud connectivity"],"best_for":["developers building privacy-focused mobile apps","teams implementing offline-first architectures","builders storing sensitive user data securely"],"limitations":["Local storage is vulnerable to device compromise; rooted devices can access encrypted data","Storage capacity is limited by device storage; large datasets (>1GB) may cause performance issues","Data migration across app versions requires careful schema management; breaking changes can cause data loss","Encryption/decryption adds CPU overhead; large data operations may cause UI lag","No built-in sync mechanism; data changes on one device don't sync to other devices","Uninstalling the app deletes all local data; no automatic backup"],"requires":["Android 4.0+ (API level 14) for SharedPreferences","Android 5.0+ (API level 21) for Room database","EncryptedSharedPreferences library for encryption","Data schema definition (what data to store, structure)","Migration logic for schema changes"],"input_types":["user data (preferences, workflows, settings)","sensitive information (API keys, tokens)","application state (conversation history, task status)"],"output_types":["persisted data on device","encryption status","data migration logs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_12","uri":"capability://automation.workflow.multi.app.workflow.orchestration.with.cross.app.context.preservation","name":"multi-app workflow orchestration with cross-app context preservation","description":"Blurr enables automation workflows that span multiple applications, maintaining context and state as the agent navigates between apps. The system detects app transitions (via AccessibilityService), preserves task context across app boundaries, and adapts the UI perception and action execution to each app's specific interface. This allows complex workflows like 'open email, find message from John, extract phone number, open contacts, add new contact with that number' where the agent must understand context across three different apps. The agent maintains a unified task model that abstracts away app-specific details.","intents":["I want to automate workflows that require switching between multiple apps","I need the agent to maintain context and task progress as it moves between apps","I want to extract data from one app and use it in another app automatically"],"best_for":["users automating complex workflows spanning multiple apps","developers building cross-app automation agents","teams implementing mobile RPA solutions"],"limitations":["App transitions introduce latency; waiting for app launch and UI rendering adds 2-5 seconds per transition","Some apps block accessibility tree access, breaking the agent's perception in those apps","Context preservation requires careful state management; losing context mid-workflow causes failures","App-specific UI patterns (custom widgets, non-standard navigation) require specialized handling","No standardized inter-app communication; agent must infer data flow from UI interactions","Workflow complexity grows exponentially with number of apps; 5+ app workflows are difficult to manage"],"requires":["AccessibilityService for cross-app UI perception","App transition detection logic","Unified task model that abstracts app-specific details","Context preservation mechanism (conversation history, extracted data)","App-specific UI pattern recognition"],"input_types":["multi-app workflow specification","app transition events","cross-app data extraction rules","context preservation requirements"],"output_types":["workflow execution status","extracted data from multiple apps","cross-app context state","app transition logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_13","uri":"capability://automation.workflow.error.recovery.and.fallback.mechanisms.for.failed.actions","name":"error recovery and fallback mechanisms for failed actions","description":"Blurr implements robust error handling that detects when actions fail (element not found, action timed out, unexpected UI state) and attempts recovery. The system includes fallback strategies: retry with adjusted timing, alternative action paths (e.g., using menu instead of direct button), and user escalation (asking user for help or manual intervention). Error detection works by comparing expected UI state (from LLM reasoning) with actual UI state (from accessibility tree) after each action. The system logs errors for debugging and learns from failures to improve future action selection.","intents":["I want the agent to recover from transient failures (network delays, UI animation timing) without giving up","I need the system to detect when an action didn't work and try an alternative approach","I want detailed error logs to understand why workflows failed"],"best_for":["developers building reliable automation agents","teams implementing production-grade mobile RPA","builders creating fault-tolerant automation systems"],"limitations":["Error detection is heuristic-based; may not catch all failure modes","Fallback strategies are limited; some failures have no good recovery path","Retry logic can cause infinite loops if not carefully designed","User escalation requires UI for user interaction; not suitable for fully autonomous workflows","Error recovery adds latency; each retry adds 1-3 seconds to task execution","Learning from failures requires manual analysis; no automatic improvement mechanism"],"requires":["Error detection logic (comparing expected vs actual UI state)","Fallback strategy definitions (retry, alternative paths, escalation)","Retry logic with exponential backoff","Error logging and analysis system","User escalation UI (dialog, notification)"],"input_types":["expected UI state (from LLM reasoning)","actual UI state (from accessibility tree)","action specification","error context (previous attempts, failure reasons)"],"output_types":["error detection results","recovery action (retry, fallback, escalate)","error logs with context","recovery success/failure status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_2","uri":"capability://planning.reasoning.llm.driven.multi.step.task.planning.and.action.selection","name":"llm-driven multi-step task planning and action selection","description":"Blurr integrates Google Gemini API as the reasoning engine that receives the current screen state (accessibility tree), user intent (voice command), and task history, then generates the next action to execute. The LLM operates in an agentic loop: it analyzes the current UI state, reasons about the user's goal, selects the most appropriate action (tap, scroll, type, etc.), and provides structured action output that the execution layer interprets. The system maintains conversation context across multiple turns, allowing the agent to handle multi-step workflows that require understanding previous actions and adapting to screen changes.","intents":["I want the AI to understand complex, multi-step user intents and break them into executable actions","I need the agent to adapt its behavior based on screen changes and unexpected UI states","I want the system to maintain context across multiple actions to handle workflows like 'open email, find message from John, reply with approval'"],"best_for":["developers building intelligent mobile automation agents","teams implementing context-aware task automation workflows","builders creating adaptive UI interaction systems that handle dynamic app states"],"limitations":["LLM inference adds 1-3 second latency per action decision, limiting real-time responsiveness","Gemini API calls consume quota and incur per-request costs; high-frequency automation can become expensive","Context window limits task complexity; very long workflows (>20 steps) may exceed token limits and lose early context","LLM hallucination can cause incorrect action selection (e.g., tapping non-existent elements); requires error recovery mechanisms","Requires internet connectivity for API calls; offline operation not supported","Prompt engineering is critical; poorly structured prompts lead to inconsistent action selection"],"requires":["Google Gemini API key with appropriate quota","Network connectivity for API calls","Structured prompt system that encodes UI state, intent, and action schema","Error handling for API failures, rate limiting, and timeout scenarios","Token counting logic to manage context window (Gemini 1.5 supports 1M tokens but practical limit ~100k for latency)"],"input_types":["current screen state (accessibility tree JSON)","user intent (natural language voice command or text)","action history (previous actions and their outcomes)","task context (goals, constraints, user preferences)"],"output_types":["structured action specification (action type, target element, parameters)","reasoning explanation (why this action was selected)","confidence score (how certain the agent is about this action)","task completion status (done, in-progress, error)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_3","uri":"capability://automation.workflow.programmatic.touch.gesture.and.ui.interaction.execution","name":"programmatic touch gesture and ui interaction execution","description":"Blurr's action execution layer translates LLM-generated action specifications into native Android UI automation commands via the AccessibilityService API. The system supports multiple interaction primitives: single/multi-touch taps at specific coordinates, swipe/scroll gestures with configurable velocity and direction, text input via keyboard simulation, and long-press interactions. Actions are queued and executed sequentially with timing controls to allow UI animations to complete between actions. The execution layer includes error detection (checking if expected UI changes occurred after an action) and fallback mechanisms for failed interactions.","intents":["I need to programmatically tap buttons, scroll lists, and type text into fields without manual intervention","I want to execute complex gesture sequences (swipe, pinch, multi-touch) that native automation APIs don't directly support","I need reliable action execution with automatic retry and error recovery when UI elements don't respond as expected"],"best_for":["developers building cross-app automation workflows on Android","teams implementing accessibility features that require programmatic UI interaction","builders creating mobile RPA (Robotic Process Automation) solutions"],"limitations":["AccessibilityService has rate limits; rapid-fire actions (>10 per second) may be dropped by Android framework","Gesture execution is coordinate-based; if UI layout changes between perception and action, taps may hit wrong elements","Some apps explicitly block AccessibilityService interactions for security reasons (banking, payment apps)","Text input via keyboard simulation doesn't work with custom input methods or IME-dependent fields","Multi-touch gestures are limited to 2-finger interactions; complex pinch/rotate patterns require custom implementation","Action execution timing is non-deterministic; animations and network delays can cause timing-dependent failures"],"requires":["Android 5.0+ (API level 21) for AccessibilityService","ACCESSIBILITY_SERVICE permission enabled by user","Target app must not explicitly block accessibility interactions","Accurate element coordinates from perception layer (accessibility tree bounds)","Timing configuration tuned for target app's animation speeds"],"input_types":["action specification (type, target coordinates, parameters)","element bounds and layout information","timing configuration (delays, animation durations)","gesture parameters (swipe direction, velocity, duration)"],"output_types":["execution status (success, failure, timeout)","actual UI changes observed after action","error details if action failed","timing metrics (action latency, response time)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_4","uri":"capability://automation.workflow.trigger.based.automation.with.scheduled.and.event.driven.execution","name":"trigger-based automation with scheduled and event-driven execution","description":"Blurr provides a trigger system that decouples task definition from execution, allowing users to define automation rules that execute based on time schedules, app launch events, or system state changes. Triggers are stored in local persistence (likely SharedPreferences or Room database based on Android patterns), with a TriggerMonitoring service that continuously checks trigger conditions and invokes the agent execution framework when conditions are met. The system supports multiple trigger types: cron-like scheduling, app-specific triggers (when app X launches), and state-based triggers (when device reaches certain conditions). This enables background automation without requiring constant user interaction.","intents":["I want to automate recurring tasks (daily email check, weekly report generation) without manual triggering","I need to execute automation workflows when specific apps launch or system events occur","I want to set up background automation that runs even when the app is not in focus"],"best_for":["users automating repetitive daily tasks (email, messaging, data entry)","teams implementing scheduled mobile RPA workflows","developers building event-driven automation systems on Android"],"limitations":["Android background execution is heavily restricted; triggers may not fire reliably if app is killed or device is in deep sleep","Doze mode (Android 6.0+) can delay trigger execution by hours; requires WorkManager or similar for reliable scheduling","Trigger monitoring service consumes battery; continuous polling adds 2-5% battery drain depending on trigger frequency","Trigger storage is local only; no cloud sync means triggers are lost if app is uninstalled","Complex trigger conditions (multiple AND/OR logic) require custom evaluation; system may not support arbitrary boolean expressions","Freemium model likely limits number of active triggers (based on 'Task Limits' mentioned in architecture)"],"requires":["Android 5.0+ (API level 21) for background service execution","SCHEDULE_EXACT_ALARM or SCHEDULE_EXACT_ALARM_CLOCK permission for time-based triggers","Local storage mechanism (SharedPreferences, Room database, or file-based)","Background service (likely using WorkManager or JobScheduler for reliability)","Trigger condition evaluation logic (cron parser, event matcher, state checker)"],"input_types":["trigger definition (type, schedule, conditions)","task specification (automation workflow to execute)","system events (app launch, time tick, state change)","user configuration (trigger parameters, enable/disable flags)"],"output_types":["trigger execution events","execution logs and history","trigger status (active, paused, failed)","scheduling metadata (next execution time, last execution time)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_5","uri":"capability://memory.knowledge.conversational.context.management.across.multi.turn.interactions","name":"conversational context management across multi-turn interactions","description":"Blurr maintains conversation state across multiple voice commands and agent actions, storing interaction history (user intents, agent actions, screen states, outcomes) that the LLM can reference for context-aware decision making. The ConversationalAgentService manages this context, passing relevant history to the Gemini API with each new request. The system implements context window management to stay within token limits while preserving recent and important interactions. This enables the agent to understand references like 'reply to the last message' or 'undo that action' by maintaining awareness of previous interactions.","intents":["I want to give follow-up commands that reference previous actions ('undo that', 'do the same thing in another app')","I need the agent to remember context from earlier in the conversation to handle multi-turn workflows","I want the system to learn from previous interactions and adapt its behavior based on what worked before"],"best_for":["users executing complex multi-step workflows that require context awareness","developers building conversational automation agents","teams implementing adaptive automation that learns from interaction history"],"limitations":["Context window is finite; very long conversations (>50 turns) will lose early context due to token limits","Context storage is in-memory by default; context is lost when app is killed or device reboots","Large context windows increase LLM latency; each API call must process all previous interactions","Context management requires careful prompt engineering to ensure LLM focuses on relevant history","No explicit memory persistence; context is not saved across app sessions unless explicitly implemented"],"requires":["Conversation history storage (in-memory list or database)","Token counting logic to manage context window size","Prompt template that includes conversation history in structured format","Mechanism to prune old context while preserving important interactions","Session management to handle app lifecycle (pause/resume)"],"input_types":["user voice commands (natural language)","previous interaction history (actions, outcomes, screen states)","current task context (goals, constraints)","system state (current screen, app focus)"],"output_types":["contextual action specifications","conversation history logs","context summary (key facts, previous decisions)","task execution status with context awareness"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_6","uri":"capability://text.generation.language.speech.to.text.transcription.with.real.time.audio.processing","name":"speech-to-text transcription with real-time audio processing","description":"Blurr captures audio from the device microphone and converts it to text using Android's built-in speech recognition APIs (likely SpeechRecognizer or Google Speech-to-Text). The system handles audio streaming, noise filtering, and transcription with configurable language support. Real-time processing allows the agent to begin reasoning about user intent as soon as speech is recognized, without waiting for the user to finish speaking. The transcribed text is then passed to the conversational agent service for intent interpretation.","intents":["I want to speak commands naturally without typing, with automatic transcription to text","I need real-time speech recognition that doesn't require waiting for silence to process","I want support for multiple languages and accents in voice commands"],"best_for":["accessibility-focused users who cannot type","users in hands-free scenarios (driving, cooking, etc.)","developers building voice-first mobile applications"],"limitations":["Speech recognition accuracy varies with ambient noise, accent, and speech rate","Real-time transcription requires continuous network access if using cloud-based recognition","On-device speech recognition models are smaller and less accurate than cloud alternatives","Language support depends on Android version and installed language packs","Transcription latency (typically 1-3 seconds) adds to overall action execution time","Background noise filtering is limited; noisy environments degrade accuracy significantly"],"requires":["Android 4.1+ (API level 16) for SpeechRecognizer","RECORD_AUDIO permission","Google Play Services for cloud-based recognition (optional but recommended for accuracy)","Language pack for target language installed on device","Microphone hardware on device"],"input_types":["audio stream from device microphone","language preference (locale)","recognition parameters (max results, partial results)"],"output_types":["transcribed text","confidence scores for alternatives","partial transcription (for real-time feedback)","language detection results"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_7","uri":"capability://text.generation.language.text.to.speech.voice.feedback.with.natural.language.responses","name":"text-to-speech voice feedback with natural language responses","description":"Blurr generates spoken responses to user commands using Android's TextToSpeech API, converting agent reasoning and task status into natural language audio output. The system supports multiple voices, speech rates, and languages. Responses are synthesized in real-time and played through device speakers, providing immediate audio feedback about task progress and completion. The TTS system integrates with the conversational agent to provide contextual responses (e.g., 'I found 3 emails from John' instead of just 'task complete').","intents":["I want voice feedback about task progress and completion without looking at the screen","I need the agent to speak responses in natural language, not just beeps or notifications","I want customizable voice characteristics (speed, pitch, language) for accessibility"],"best_for":["accessibility users who rely on audio feedback","hands-free automation scenarios where visual feedback is unavailable","voice-first interaction paradigms"],"limitations":["TTS quality varies by device and installed TTS engine; some devices have poor default voices","Synthesizing long responses (>100 words) adds 2-5 second latency","TTS output interrupts other audio (music, calls, notifications); no audio mixing","Language support depends on installed TTS engine; not all languages available on all devices","Custom voice synthesis (emotional tone, emphasis) not supported by standard Android TTS","TTS output volume is device-dependent; no programmatic volume control in standard API"],"requires":["Android 4.0+ (API level 14) for TextToSpeech","TTS engine installed on device (usually pre-installed)","Language data for target language","Speaker or audio output capability","INTERNET permission if using cloud-based TTS"],"input_types":["text to synthesize (agent responses, status messages)","language/locale preference","voice parameters (speed, pitch, language)"],"output_types":["audio stream (synthesized speech)","playback status (playing, completed, error)","timing information (duration, completion time)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_8","uri":"capability://safety.moderation.permission.and.capability.detection.with.user.consent.management","name":"permission and capability detection with user consent management","description":"Blurr implements a comprehensive permission system that detects required capabilities (microphone, accessibility service, location, contacts, etc.) and guides users through permission granting via OnboardingPermissionsActivity. The system checks permission status at runtime and provides clear explanations of why each permission is needed. The permission management system is integrated with the freemium model, potentially restricting certain capabilities based on subscription level. The system respects Android's runtime permission model (API 23+) and handles permission denial gracefully.","intents":["I want to understand what permissions the app needs and why before granting them","I need the system to handle permission denial gracefully and suggest workarounds","I want to manage which capabilities are enabled based on my privacy preferences"],"best_for":["privacy-conscious users who want transparency about data access","developers building permission-aware automation systems","teams implementing accessibility features with proper consent flows"],"limitations":["Some permissions (accessibility service, default assistant role) require manual enabling in system settings; cannot be granted programmatically","Permission denial breaks core functionality; no graceful degradation for missing accessibility service","Android permission model is binary; cannot request partial permissions (e.g., microphone access only for specific apps)","Permission revocation by user can happen at any time; system must handle runtime permission loss","Some permissions (location, contacts) may be restricted by device policies or parental controls"],"requires":["Android 6.0+ (API level 23) for runtime permissions","Manifest declarations for all required permissions","OnboardingPermissionsActivity UI for permission requests","Runtime permission checking logic","Graceful error handling for permission denial"],"input_types":["required capability list","user permission preferences","system permission status"],"output_types":["permission grant status","capability availability flags","user consent records","permission denial reasons"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-ayush0chaudhary--blurr__cap_9","uri":"capability://automation.workflow.freemium.task.execution.with.quota.management.and.billing.integration","name":"freemium task execution with quota management and billing integration","description":"Blurr implements a freemium business model with task execution limits for free users and unlimited access for paid subscribers. The system tracks task execution count, enforces quota limits, and prevents execution when limits are exceeded. Billing integration (likely via Google Play Billing Library) handles subscription management, payment processing, and subscription status verification. The quota system is enforced at the agent execution layer, checking remaining quota before executing tasks. Firebase integration likely tracks usage metrics for analytics and quota enforcement.","intents":["I want to try the automation features with limited free usage before committing to a paid subscription","I need transparent quota tracking so I know how many tasks I can execute","I want to upgrade to unlimited usage when I exceed my free quota"],"best_for":["freemium app developers implementing usage-based monetization","teams building subscription-based automation services","builders creating trial-to-paid conversion funnels"],"limitations":["Quota enforcement is client-side; determined users can bypass limits by modifying app state","Task counting logic must be precise; ambiguity about what constitutes a 'task' leads to user confusion","Quota resets (daily, monthly) require server-side coordination; client-side resets are unreliable","Billing integration adds complexity and potential failure points (payment processing, subscription verification)","Free users may experience degraded service (slower LLM inference, lower priority) if not explicitly implemented","Quota limits may frustrate users and drive churn if set too aggressively"],"requires":["Google Play Billing Library for subscription management","Firebase integration for quota tracking and analytics","Subscription product configuration in Google Play Console","Quota enforcement logic at agent execution layer","User authentication to track quota per user","Server-side quota verification for security"],"input_types":["user subscription status","task execution request","quota configuration (limits, reset period)","billing event (subscription change, payment)"],"output_types":["quota status (remaining tasks, reset time)","execution permission (allowed/denied)","billing status (active subscription, expiration)","usage analytics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Android 8.0+ (API level 26) for Accessibility Service APIs","Microphone permission (RECORD_AUDIO)","Picovoice SDK integration with access key","Google Gemini API key for LLM-based intent interpretation","Text-to-speech engine (system TTS or integrated alternative)","Android 5.0+ (API level 21) for AccessibilityService","ACCESSIBILITY_SERVICE permission declared and enabled by user","Target app must expose UI elements via AccessibilityNodeInfo (standard Android components do this automatically)","Sufficient device memory to parse large accessibility trees (complex apps may have 500+ nodes)","Firebase project setup with Analytics enabled"],"failure_modes":["Wake-word detection runs continuously, consuming ~5-8% CPU baseline on modern devices","Speech recognition accuracy depends on ambient noise levels and microphone quality","Picovoice engine requires on-device model loading, adding ~50MB to app size","Voice commands must be interpreted through LLM context window, limiting task complexity to ~4000 token descriptions","AccessibilityService requires explicit user permission and cannot be granted programmatically","Some apps explicitly block accessibility tree access for security/DRM reasons (banking apps, games with anti-cheat)","Accessibility tree extraction adds ~200-500ms latency per screen read depending on UI complexity","Custom UI frameworks or non-standard Android views may not expose semantic information to accessibility tree","Tree depth is limited; deeply nested UI hierarchies (>15 levels) may be truncated by Android framework","Firebase analytics adds network overhead and battery drain (background data transmission)","builder identity is not verified yet","artifact is still pending review"],"rank_breakdown":{"adoption":0.22757424256906228,"quality":0.25,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.35,"weights":{"adoption":0.2,"quality":0.25,"ecosystem":0.1,"match_graph":0.4,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"pending_review","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2026-01-13T13:53:06Z"},"community":{"stars":907,"forks":128,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ayush0chaudhary--blurr","compare_url":"https://unfragile.ai/compare?artifact=ayush0chaudhary--blurr"}},"signature":"gzwU8n6dc+2ftZaWzWtkWE7L+3H25UnEPYSaC6/3u1ub5yqFgr0ExOZx5bZ6mL84ROD8CWJozyrJ0/xYssUQCg==","signedAt":"2026-06-20T18:19:23.240Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ayush0chaudhary--blurr","artifact":"https://unfragile.ai/ayush0chaudhary--blurr","verify":"https://unfragile.ai/api/v1/verify?slug=ayush0chaudhary--blurr","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"}}