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app.json
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{
"expo": {
"name": "rnr-test",
"slug": "rnr-test",
"name": "powr",
"slug": "powr",
"version": "1.0.0",
"orientation": "portrait",
"icon": "./assets/images/icon.png",

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# POWR Database Architecture Diagrams
## 1. Entity Relationship Diagram
This diagram shows the core database structure and relationships between tables. The design supports both raw Nostr event storage and processed data for efficient querying.
Key Features:
- Raw Nostr event storage in `nostr_events`
- Processed exercise data in `exercise_definitions`
- Dependency tracking in `incomplete_templates`
- Efficient tag indexing in `event_tags`
```mermaid
erDiagram
nostr_events ||--o{ event_tags : contains
nostr_events ||--o| exercise_definitions : processes
nostr_events ||--o| incomplete_templates : tracks
nostr_events {
string id PK
string pubkey
number kind
string raw_event
number created_at
number received_at
}
event_tags {
string event_id FK
string name
string value
number index
}
exercise_definitions {
string event_id FK
string title
string equipment
string format
string format_units
}
incomplete_templates {
string template_id FK
number missing_exercise_count
string missing_exercises
}
```
## 2. Event Processing Flow
This diagram illustrates how different types of Nostr events (Exercise Definitions, Workout Templates, and Workout Records) are processed, validated, and stored.
Key Features:
- Event type differentiation
- Validation process
- Dependency checking
- Storage paths for complete/incomplete data
```mermaid
flowchart TB
subgraph Input
A[New Nostr Event] --> B{Event Type}
end
B -->|kind 33401| C[Exercise Definition]
B -->|kind 33402| D[Workout Template]
B -->|kind 33403| E[Workout Record]
subgraph Processing
C --> F[Validate Event]
D --> F
E --> F
F --> G{Valid?}
G -->|No| H[Reject Event]
G -->|Yes| I[Store Raw Event]
I --> J[Process Event]
J --> K{Dependencies?}
K -->|Missing| L[Store as Incomplete]
K -->|Complete| M[Store Processed Data]
L --> N[Queue Missing Events]
end
subgraph Storage
M --> O[(NostrEvents)]
M --> P[(ProcessedData)]
L --> Q[(IncompleteQueue)]
end
```
## 3. Query and Cache Flow
This sequence diagram shows how data is retrieved, utilizing the LRU cache for performance and handling template dependencies.
Key Features:
- Cache hit/miss handling
- Template dependency resolution
- Efficient data retrieval paths
- Incomplete template handling
- Solid line with arrow (->>) means "makes a request to"
- Dashed line (-->) means "returns data to"
```mermaid
sequenceDiagram
participant C as Client
participant Cache as LRU Cache
participant DB as SQLite
participant Q as Query Builder
C->>Q: Client asks Query Builder for templates
Q->>Cache: Query Builder first checks if data is in cache
alt Cache Hit
Cache-->>C: Quickly Returns Data found in Cache
else Cache Miss: Data not in cache, must query DB
Q->>DB: Query Database
DB-->>Q: Raw Results
Q->>Q: Process Raw Data
Q->>Cache: Store in Cache
Q-->>C: Return Results
end
Note over C,DB: Template Dependency Resolution
C->>Q: Template Request
Q->>DB: Fetch Template
DB-->>Q: Template Data
Q->>DB: Check Dependencies
alt Missing Dependencies
DB-->>Q: Missing Exercises
Q->>C: Return Incomplete
else Complete
DB-->>Q: All Dependencies
Q->>C: Return Complete Template
end
```
The second part shows template dependency resolution:
- Template request process
- Dependency checking
- Different responses based on whether all exercises exist
The key learning points:
- Cache is checked first to improve performance
- Database is only queried if necessary
- Results are cached for future use
- Dependencies are verified before returning complete templates
## 4. Component Architecture
This diagram shows the overall application architecture, including service layers and future Nostr integration points.
Key Features:
- Clear layer separation
- Service interactions
- Cache management
- Future Nostr integration points
```mermaid
graph TB
subgraph UI Layer
A[Library Screen]
B[Exercise Form]
C[Template Form]
end
subgraph Service Layer
D[Library Service]
E[Event Processor]
F[Cache Manager]
end
subgraph Data Layer
G[(SQLite)]
H[Query Builder]
I[Event Validators]
end
subgraph Future Nostr
J[Relay Manager]
K[Event Publisher]
L[Sync Manager]
end
A --> D
B --> D
C --> D
D --> E
D --> F
E --> I
E --> G
F --> G
F --> H
H --> G
D -.-> J
D -.-> K
D -.-> L
```
## Implementation Notes
These diagrams represent the core architecture of POWR's database implementation. Key considerations:
1. **Data Flow**
- All Nostr events are stored in raw form
- Processed data is stored separately for efficiency
- Cache layer improves read performance
- Dependency tracking ensures data integrity
2. **Scalability**
- Modular design allows for future expansion
- Clear separation of concerns
- Efficient query patterns
- Prepared for Nostr integration
3. **Performance**
- LRU caching for frequent queries
- Optimized indexes for common operations
- Efficient dependency resolution
- Batch processing capability

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# POWR Database Implementation Design Document
## Problem Statement
Implement a SQLite database that supports local-first fitness tracking while enabling seamless Nostr protocol integration. The system must handle exercise definitions (NIP-33401), workout templates (NIP-33402), and workout records (NIP-33403) while managing event dependencies, caching, and efficient querying.
## Requirements
### Functional Requirements
- Store and process Nostr events (33401, 33402, 33403)
- Handle incomplete workout templates with missing exercise references
- Support both local and Nostr-sourced content
- Enable efficient content querying and filtering
- Track template completeness and dependencies
- Manage event replacements and updates
### Non-Functional Requirements
- Query response time < 100ms
- Support offline-first operations
- Handle concurrent write operations safely
- Efficient storage for device constraints
- Maintain data integrity with event dependencies
## Design Decisions
### 1. Event Storage Strategy
Store both raw Nostr events and processed data:
- Raw events for perfect relay replication
- Processed data for efficient querying
- Separate incomplete template tracking
- Tag indexing for fast lookups
Rationale:
- Maintains Nostr protocol compliance
- Enables efficient local operations
- Supports dependency tracking
- Facilitates sync operations
### 2. Dependency Management
Track missing exercise references:
- Store incomplete templates
- Track missing dependencies
- Enable background fetching
- Allow filtering by completeness
Rationale:
- Better user experience
- Data integrity
- Eventual consistency
- Clear status tracking
## Technical Design
### Core Schema
```typescript
interface DatabaseSchema {
// Raw Nostr event storage
nostr_events: {
id: string; // 32-bytes hex
pubkey: string; // 32-bytes hex
kind: number; // 33401 | 33402 | 33403
raw_event: string; // Full JSON event
created_at: number; // Unix timestamp
received_at: number; // Local timestamp
};
// Processed exercise definitions
exercise_definitions: {
event_id: string; // Reference to nostr_events
title: string;
equipment: string;
format: string; // JSON stringified
format_units: string; // JSON stringified
};
// Template dependency tracking
incomplete_templates: {
template_id: string; // Reference to nostr_events
missing_exercise_count: number;
missing_exercises: string; // JSON stringified
};
// Tag indexing
event_tags: {
event_id: string; // Reference to nostr_events
name: string; // Tag name
value: string; // Tag value
index: number; // Position in tag array
};
}
```
### Event Validators
```typescript
interface EventValidator {
validateEvent(event: NostrEvent): ValidationResult;
processEvent(event: NostrEvent): ProcessedEvent;
}
class ExerciseDefinitionValidator implements EventValidator {
// Implementation for kind 33401
}
class WorkoutTemplateValidator implements EventValidator {
// Implementation for kind 33402
}
```
## Implementation Plan
### Phase 1: Core Event Handling
1. Basic schema implementation
2. Event validation and processing
3. Tag indexing system
4. Basic CRUD operations
### Phase 2: Template Dependencies
1. Incomplete template tracking
2. Missing exercise handling
3. Background fetching system
4. Template status management
### Phase 3: Query Optimization
1. Implement efficient indexes
2. Query caching system
3. Common query patterns
4. Performance monitoring
## Testing Strategy
### Unit Tests
- Event validation
- Data processing
- CRUD operations
- Tag indexing
### Integration Tests
- Template dependency handling
- Event synchronization
- Cache operations
- Query performance
### Performance Tests
- Query response times
- Write operation latency
- Cache efficiency
- Storage utilization
## Security Considerations
- Event signature validation
- Input sanitization
- Access control
- Data integrity
## Future Considerations
### Potential Enhancements
- Advanced caching strategies
- Relay management
- Batch operations
- Compression options
### Known Limitations
- SQLite concurrent access
- Dependency completeness
- Cache memory constraints
- Initial sync performance
## Dependencies
### Runtime Dependencies
- expo-sqlite
- @react-native-async-storage/async-storage
### Development Dependencies
- Jest for testing
- SQLite tooling
- TypeScript
## Query Examples
### Template Queries
```typescript
// Get templates by author
async getTemplatesByPubkey(
pubkey: string,
options: {
includeIncomplete?: boolean;
limit?: number;
since?: number;
}
): Promise<ProcessedTemplate[]>
// Get template with exercises
async getTemplateWithExercises(
templateId: string
): Promise<CompleteTemplate>
```
### Exercise Queries
```typescript
// Search exercises
async searchExercises(
params: SearchParams
): Promise<ProcessedExerciseDefinition[]>
// Get exercise history
async getExerciseHistory(
exerciseId: string
): Promise<ExerciseHistory[]>
```
## References
- NDK SQLite Implementation
- Nostr NIP-01 Specification
- POWR Exercise NIP Draft
- POWR Library Tab PRD

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# NIP-XX: Workout Events
`draft` `optional`
This specification defines workout events for fitness tracking. These workout events support both planning (templates) and recording (completed activities).
## Event Kinds
### Exercise Template (kind: 33401)
Defines reusable exercise definitions. These should remain public to enable discovery and sharing. The `content` field contains detailed form instructions and notes.
#### Required Tags
* `d` - UUID for template identification
* `title` - Exercise name
* `format` - Defines data structure for exercise tracking (possible parameters: `weight`, `reps`, `rpe`, `set_type`)
* `format_units` - Defines units for each parameter (possible formats: "kg", "count", "0-10", "warmup|normal|drop|failure")
* `equipment` - Equipment type (possible values: `barbell`, `dumbbell`, `bodyweight`, `machine`, `cardio`)
#### Optional Tags
* `difficulty` - Skill level (possible values: `beginner`, `intermediate`, `advanced`)
* `imeta` - Media metadata for form demonstrations following NIP-92 format
* `t` - Hashtags for categorization such as muscle group or body movement (possible values: `chest`, `legs`, `push`, `pull`)
### Workout Template (kind: 33402)
Defines a complete workout plan. The `content` field contains workout notes and instructions. Workout templates can prescribe specific parameters while leaving others configurable by the user performing the workout.
#### Required Tags
* `d` - UUID for template identification
* `title` - Workout name
* `type` - Type of workout (possible values: `strength`, `circuit`, `emom`, `amrap`)
* `exercise` - Exercise reference and prescription. Format: ["exercise", "kind:pubkey:d-tag", "relay-url", ...parameters matching exercise template format]
#### Optional Tags
* `rounds` - Number of rounds for repeating formats
* `duration` - Total workout duration in seconds
* `interval` - Duration of each exercise portion in seconds (for timed workouts)
* `rest_between_rounds` - Rest time between rounds in seconds
* `t` - Hashtags for categorization
### Workout Record (kind: 33403)
Records a completed workout session. The `content` field contains notes about the workout.
#### Required Tags
* `d` - UUID for record identification
* `title` - Workout name
* `type` - Type of workout (possible values: `strength`, `circuit`, `emom`, `amrap`)
* `exercise` - Exercise reference and completion data. Format: ["exercise", "kind:pubkey:d-tag", "relay-url", ...parameters matching exercise template format]
* `start` - Unix timestamp in seconds for workout start
* `end` - Unix timestamp in seconds for workout end
* `completed` - Boolean indicating if workout was completed as planned
#### Optional Tags
* `rounds_completed` - Number of rounds completed
* `interval` - Duration of each exercise portion in seconds (for timed workouts)
* `pr` - Personal Record achieved during workout. Format: "kind:pubkey:d-tag,metric,value". Used to track when a user achieves their best performance for a given exercise and metric (e.g., heaviest weight lifted, most reps completed, fastest time)
* `t` - Hashtags for categorization
## Exercise Parameters
### Standard Parameters and Units
* `weight` - Load in kilograms (kg). Empty string for bodyweight exercises, negative values for assisted exercises
* `reps` - Number of repetitions (count)
* `rpe` - Rate of Perceived Exertion (0-10):
- RPE 10: Could not do any more reps, technical failure
- RPE 9: Could maybe do 1 more rep
- RPE 8: Could definitely do 1 more rep, maybe 2
- RPE 7: Could do 2-3 more reps
* `duration` - Time in seconds
* `set_type` - Set classification (possible values: `warmup`, `normal`, `drop`, `failure`)
Additional parameters can be defined in exercise templates in the `format_units` tag as needed for specific activities (e.g., distance, heartrate, intensity).
## Workout Types and Terminology
This specification provides examples of common workout structures but is not limited to these types. The format is extensible to support various training methodologies while maintaining consistent data structure.
### Common Workout Types
#### Strength
Traditional strength training focusing on sets and reps with defined weights. Typically includes warm-up sets, working sets, and may include techniques like drop sets or failure sets.
#### Circuit
Multiple exercises performed in sequence with minimal rest between exercises and defined rest periods between rounds. Focuses on maintaining work rate through prescribed exercises.
#### EMOM (Every Minute On the Minute)
Time-based workout where specific exercises are performed at the start of each minute. Rest time is whatever remains in the minute after completing prescribed work.
#### AMRAP (As Many Rounds/Reps As Possible)
Time-capped workout where the goal is to complete as many rounds or repetitions as possible of prescribed exercises while maintaining proper form.
## Set Types
### Normal Sets
Standard working sets that count toward volume and progress tracking.
### Warm-up Sets
Preparatory sets using submaximal weights. These sets are not counted in metrics or progress tracking.
### Drop Sets
Sets performed immediately after a working set with reduced weight. These are counted in volume calculations but tracked separately for progress analysis.
### Failure Sets
Sets where technical failure was reached before completing prescribed reps. These sets are counted in metrics but marked to indicate intensity/failure was reached.
## Examples
### Exercise Template
```json
{
"kind": 33401,
"content": "Stand with feet hip-width apart, barbell over midfoot. Hinge at hips, grip bar outside knees. Flatten back, brace core. Drive through floor, keeping bar close to legs.\n\nForm demonstration: https://powr.me/exercises/deadlift-demo.mp4",
"tags": [
["d", "bb-deadlift-template"],
["title", "Barbell Deadlift"],
["format", "weight", "reps", "rpe", "set_type"],
["format_units", "kg", "count", "0-10", "warmup|normal|drop|failure"],
["equipment", "barbell"],
["difficulty", "intermediate"],
["imeta",
"url https://powr.me/exercises/deadlift-demo.mp4",
"m video/mp4",
"dim 1920x1080",
"alt Demonstration of proper barbell deadlift form"
],
["t", "compound"],
["t", "legs"],
["t", "posterior"]
]
}
```
### EMOM Workout Template
```json
{
"kind": 33402,
"content": "20 minute EMOM alternating between squats and deadlifts every 30 seconds. Scale weight as needed to complete all reps within each interval.",
"tags": [
["d", "lower-body-emom-template"],
["title", "20min Squat/Deadlift EMOM"],
["type", "emom"],
["duration", "1200"],
["rounds", "20"],
["interval", "30"],
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-back-squat-template", "wss://powr.me", "", "5", "7", "normal"],
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-deadlift-template", "wss://powr.me", "", "4", "7", "normal"],
["t", "conditioning"],
["t", "legs"]
]
}
```
### Circuit Workout Record
```json
{
"kind": 33403,
"content": "Completed first round as prescribed. Second round showed form deterioration on deadlifts.",
"tags": [
["d", "workout-20250128"],
["title", "Leg Circuit"],
["type", "circuit"],
["rounds_completed", "1.5"],
["start", "1706454000"],
["end", "1706455800"],
// Round 1 - Completed as prescribed
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-back-squat-template", "wss://powr.me", "80", "12", "7", "normal"],
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-deadlift-template", "wss://powr.me", "100", "10", "7", "normal"],
// Round 2 - Failed on deadlifts
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-back-squat-template", "wss://powr.me", "80", "12", "8", "normal"],
["exercise", "33401:9947f9659dd80c3682402b612f5447e28249997fb3709500c32a585eb0977340:bb-deadlift-template", "wss://powr.me", "100", "4", "10", "failure"],
["completed", "false"],
["t", "legs"]
]
}
```
## Implementation Guidelines
1. All workout records SHOULD include accurate start and end times
2. Templates MAY prescribe specific parameters while leaving others as empty strings for user input
3. Records MUST include actual values for all parameters defined in exercise format
4. Failed sets SHOULD be marked with `failure` set_type
5. Records SHOULD be marked as `false` for completed if prescribed work wasn't completed
6. PRs SHOULD only be tracked in workout records, not templates
7. Exercise references SHOULD use the format "kind:pubkey:d-tag" to ensure proper attribution and versioning
## References
This NIP draws inspiration from:
- [NIP-01: Basic Protocol Flow Description](https://github.com/nostr-protocol/nips/blob/master/01.md)
- [NIP-92: Media Attachments](https://github.com/nostr-protocol/nips/blob/master/92.md#nip-92)

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# [Feature Name] Design Document
# POWR Database Implementation PRD
## Problem Statement
[Concise description of the problem being solved]
POWR requires a robust SQLite database implementation that enables local-first fitness tracking while supporting future Nostr protocol integration. The database must efficiently handle exercise definitions (NIP-33401), workout templates (NIP-33402), and workout records (NIP-33403) while maintaining a clean separation between local storage needs and Nostr protocol compatibility.
## Requirements
### Functional Requirements
- [List of must-have functionality]
- [User-facing features]
- [Core capabilities]
- Store and process exercise definitions with complete metadata
- Support workout template creation and management
- Track workout records and performance history
- Enable efficient content querying and filtering
- Handle both local and Nostr-sourced content seamlessly
- Support offline-first operations with sync capabilities
- Manage template dependencies and incomplete references
- Track exercise history and performance metrics
- Support batch operations and migrations
### Non-Functional Requirements
- Performance targets
- Security requirements
- Reliability goals
- Usability standards
- Query response time < 100ms for common operations
- Support concurrent read/write operations safely
- Minimize memory usage through efficient caching
- Maintain data integrity across sync operations
- Scale to handle 1000+ exercises and 100+ templates
- Support incremental sync with Nostr relays
- Ensure consistent performance on mobile devices
- Support automated testing and validation
## Design Decisions
### 1. [Major Decision Area]
[Description of approach chosen]
### 1. Storage Architecture
Use a dual storage approach with separate tables for raw events and processed data:
Rationale:
- [Key reason]
- [Supporting factors]
- [Trade-offs considered]
- Maintains perfect relay replication capability
- Enables efficient local querying
- Supports dependency tracking
- Facilitates future sync operations
- Allows for flexible schema evolution
### 2. [Major Decision Area]
[Description of approach chosen]
### 2. Cache Management
Implement an LRU cache system with configurable limits:
Rationale:
- [Key reason]
- [Supporting factors]
- [Trade-offs considered]
- Improves read performance for common queries
- Manages memory usage effectively
- Supports write buffering for batch operations
- Provides tunable performance controls
### 3. Schema Design
Use a normalized schema with proper constraints and indexing:
Rationale:
- Ensures data integrity
- Enables efficient querying
- Supports future extensions
- Maintains clear relationships
## Technical Design
### Core Schema
```sql
-- Schema Version
CREATE TABLE schema_version (
version INTEGER PRIMARY KEY,
updated_at INTEGER NOT NULL
);
-- Raw Events
CREATE TABLE nostr_events (
id TEXT PRIMARY KEY,
kind INTEGER NOT NULL,
pubkey TEXT NOT NULL,
created_at INTEGER NOT NULL,
content TEXT,
raw_event TEXT NOT NULL,
source TEXT DEFAULT 'local'
);
-- Processed Exercises
CREATE TABLE exercise_definitions (
event_id TEXT PRIMARY KEY,
title TEXT NOT NULL,
type TEXT NOT NULL,
category TEXT NOT NULL,
equipment TEXT,
description TEXT,
format_json TEXT,
format_units_json TEXT,
FOREIGN KEY(event_id) REFERENCES nostr_events(id)
);
-- Templates
CREATE TABLE workout_templates (
event_id TEXT PRIMARY KEY,
title TEXT NOT NULL,
type TEXT NOT NULL,
category TEXT NOT NULL,
description TEXT,
rounds INTEGER,
duration INTEGER,
interval_time INTEGER,
rest_between_rounds INTEGER,
created_at INTEGER NOT NULL,
FOREIGN KEY(event_id) REFERENCES nostr_events(id)
);
```
### Core Components
```typescript
// Key interfaces/types
interface ComponentName {
// Critical fields
interface DbService {
// Core database operations
executeSql(sql: string, params?: any[]): Promise<SQLiteResult>;
withTransaction<T>(operation: () => Promise<T>): Promise<T>;
// Migration handling
getCurrentVersion(): Promise<number>;
migrate(targetVersion?: number): Promise<void>;
}
// Core functionality
function mainOperation() {
// Key logic
interface CacheManager {
// Cache configuration
maxExercises: number;
maxTemplates: number;
writeBufferSize: number;
// Cache operations
get<T>(key: string): Promise<T | undefined>;
set<T>(key: string, value: T): Promise<void>;
invalidate(key: string): Promise<void>;
}
```
### Integration Points
- [System interfaces]
- [External dependencies]
- [API definitions]
## Implementation Plan
### Phase 1: [Initial Phase]
1. [Step 1]
2. [Step 2]
3. [Step 3]
### Phase 1: Core Infrastructure (Week 1-2)
1. Set up base schema and migrations
2. Implement DbService class
3. Add basic CRUD operations
4. Create test infrastructure
### Phase 2: [Next Phase]
1. [Step 1]
2. [Step 2]
3. [Step 3]
### Phase 2: Cache Layer (Week 2-3)
1. Implement CacheManager
2. Add LRU caching
3. Configure write buffering
4. Add cache invalidation
### Phase 3: Query Layer (Week 3-4)
1. Build query builders
2. Implement common queries
3. Add search functionality
4. Optimize performance
### Phase 4: Nostr Integration (Week 4-5)
1. Add event processing
2. Implement sync logic
3. Handle dependencies
4. Add relay management
## Testing Strategy
### Unit Tests
- [Key test areas]
- [Critical test cases]
- [Test tooling]
- Schema creation and migrations
- CRUD operations for all entities
- Cache operations and invalidation
- Query builder functions
### Integration Tests
- [End-to-end scenarios]
- [Cross-component tests]
- [Test environments]
- End-to-end workflow testing
- Template dependency handling
- Sync operations
- Performance benchmarks
### Performance Tests
- Query response times
- Cache hit rates
- Write operation latency
- Memory usage patterns
## Observability
### Logging
- [Key log points]
- [Log levels]
- [Critical events]
- Schema migrations
- Cache operations
- Query performance
- Error conditions
### Metrics
- [Performance metrics]
- [Business metrics]
- [System health metrics]
- Query response times
- Cache hit/miss rates
- Database size
- Operation counts
## Future Considerations
### Potential Enhancements
- [Future feature ideas]
- [Scalability improvements]
- [Technical debt items]
- Advanced caching strategies
- Full-text search
- Data compression
- Cloud backup options
### Known Limitations
- [Current constraints]
- [Technical restrictions]
- [Scope boundaries]
- SQLite concurrent access
- Initial sync performance
- Cache memory constraints
- Platform-specific issues
## Dependencies
### Runtime Dependencies
- [External services]
- [Libraries]
- [System requirements]
- expo-sqlite
- @react-native-async-storage/async-storage
- NDK (future)
### Development Dependencies
- [Build tools]
- [Test frameworks]
- [Development utilities]
- Jest
- TypeScript
- SQLite development tools
## Security Considerations
- [Security measures]
- [Privacy concerns]
- [Data protection]
## Rollout Strategy
### Development Phase
1. [Development steps]
2. [Testing approach]
3. [Documentation needs]
### Production Deployment
1. [Deployment steps]
2. [Migration plan]
3. [Monitoring setup]
- Input validation
- Query parameterization
- Event signature verification
- Access control
## References
- [Related documents]
- [External resources]
- [Research materials]
- NDK SQLite Implementation
- Nostr NIP-01 Specification
- POWR Exercise NIP Draft
- React Native SQLite Documentation

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{
"name": "rnr-test",
"name": "powr",
"version": "1.0.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "rnr-test",
"name": "powr",
"version": "1.0.0",
"hasInstallScript": true,
"dependencies": {

2
package.json
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@ -1,5 +1,5 @@
{
"name": "rnr-test",
"name": "powr",
"main": "index.js",
"version": "1.0.0",
"scripts": {