clawd
d81e403f01
COMPLETED TASKS: ✅ 06-01: Workout Swap System - Added swapped_from_id to workout_logs - Created workout_swaps table for history - POST /api/workouts/:id/swap endpoint - GET /api/workouts/available endpoint - Reversible swaps with audit trail ✅ 06-02: Muscle Group Recovery Tracking - Created muscle_group_recovery table - Implemented calculateRecoveryScore() function - GET /api/recovery/muscle-groups endpoint - GET /api/recovery/most-recovered endpoint - Auto-tracking on workout log completion ✅ 06-03: Smart Workout Recommendations - GET /api/recommendations/smart-workout endpoint - 7-day workout analysis algorithm - Recovery-based filtering (>30% threshold) - Top 3 recommendations with context - Context-aware reasoning messages DATABASE CHANGES: - Added 4 new tables: muscle_group_recovery, workout_swaps, custom_workouts, custom_workout_exercises - Extended workout_logs with: swapped_from_id, source_type, custom_workout_id, custom_workout_exercise_id - Created 7 new indexes for performance IMPLEMENTATION: - Recovery service with 4 core functions - 2 new route handlers (recovery, smartRecommendations) - Updated workouts router with swap endpoints - Integrated recovery tracking into POST /api/logs - Full error handling and logging TESTING: - Test file created: /backend/test/phase-06-tests.js - Ready for E2E and staging validation STATUS: Ready for frontend integration and production review Branch: feature/06-phase-06
1.2 KiB
1.2 KiB
SPARC Analyzer Mode
Purpose
Deep code and data analysis with batch processing capabilities.
Activation
Option 1: Using MCP Tools (Preferred in Claude Code)
mcp__claude-flow__sparc_mode {
mode: "analyzer",
task_description: "analyze codebase performance",
options: {
parallel: true,
detailed: true
}
}
Option 2: Using NPX CLI (Fallback when MCP not available)
# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run analyzer "analyze codebase performance"
# For alpha features
npx claude-flow@alpha sparc run analyzer "analyze codebase performance"
Option 3: Local Installation
# If claude-flow is installed locally
./claude-flow sparc run analyzer "analyze codebase performance"
Core Capabilities
- Code analysis with parallel file processing
- Data pattern recognition
- Performance profiling
- Memory usage analysis
- Dependency mapping
Batch Operations
- Parallel file analysis using concurrent Read operations
- Batch pattern matching with Grep tool
- Simultaneous metric collection
- Aggregated reporting
Output Format
- Detailed analysis reports
- Performance metrics
- Improvement recommendations
- Visualizations when applicable