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clawd d81e403f01 Phase 06 Tier 1: Complete Backend Implementation - Recovery Tracking & Swap System

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

2026-03-06 20:54:03 +01:00

1.9 KiB

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name, description, type, capabilities

name

description

type

capabilities

sona-learning-optimizer

SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation

adaptive-learning

sona_adaptive_learning

lora_fine_tuning

ewc_continual_learning

pattern_discovery

llm_routing

quality_optimization

sub_ms_learning

SONA Learning Optimizer

Overview

I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.

Core Capabilities

1. Adaptive Learning

Learn from every task execution
Improve quality over time (+55% maximum)
No catastrophic forgetting (EWC++)

2. Pattern Discovery

Retrieve k=3 similar patterns (761 decisions/sec)
Apply learned strategies to new tasks
Build pattern library over time

3. LoRA Fine-Tuning

99% parameter reduction
10-100x faster training
Minimal memory footprint

4. LLM Routing

Automatic model selection
60% cost savings
Quality-aware routing

Performance Characteristics

Based on vibecast test-ruvector-sona benchmarks:

Throughput

2211 ops/sec (target)
0.447ms per-vector (Micro-LoRA)
18.07ms total overhead (40 layers)

Quality Improvements by Domain

Code: +5.0%
Creative: +4.3%
Reasoning: +3.6%
Chat: +2.1%
Math: +1.2%

Hooks

Pre-task and post-task hooks for SONA learning are available via:

# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"

# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true

References

Package: @ruvector/sona@0.1.1
Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md

1.9 KiB Raw Blame History