feat: CRM Clinicas SaaS - MVP completo
- Auth: Login/Register con creacion de clinica - Dashboard: KPIs reales, graficas recharts - Pacientes: CRUD completo con busqueda - Agenda: FullCalendar, drag-and-drop, vista recepcion - Expediente: Notas SOAP, signos vitales, CIE-10 - Facturacion: Facturas con IVA, campos CFDI SAT - Inventario: Productos, stock, movimientos, alertas - Configuracion: Clinica, equipo, catalogo servicios - Supabase self-hosted: 18 tablas con RLS multi-tenant - Docker + Nginx para produccion Co-Authored-By: claude-flow <ruv@ruv.net>
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Consultoria AS
127
.claude/helpers/learning-optimizer.sh
Executable file
127
.claude/helpers/learning-optimizer.sh
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#!/bin/bash
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# Claude Flow V3 - Learning Optimizer Worker
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# Runs SONA micro-LoRA optimization on patterns
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)"
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LEARNING_DIR="$PROJECT_ROOT/.claude-flow/learning"
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METRICS_DIR="$PROJECT_ROOT/.claude-flow/metrics"
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PATTERNS_DB="$LEARNING_DIR/patterns.db"
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LEARNING_FILE="$METRICS_DIR/learning.json"
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LAST_RUN_FILE="$METRICS_DIR/.optimizer-last-run"
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mkdir -p "$LEARNING_DIR" "$METRICS_DIR"
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should_run() {
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if [ ! -f "$LAST_RUN_FILE" ]; then return 0; fi
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local last_run=$(cat "$LAST_RUN_FILE" 2>/dev/null || echo "0")
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local now=$(date +%s)
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[ $((now - last_run)) -ge 1800 ] # 30 minutes
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}
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calculate_routing_accuracy() {
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if [ -f "$PATTERNS_DB" ] && command -v sqlite3 &>/dev/null; then
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# Calculate based on pattern quality distribution
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local high_quality=$(sqlite3 "$PATTERNS_DB" "SELECT COUNT(*) FROM short_term_patterns WHERE quality > 0.7" 2>/dev/null || echo "0")
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local total=$(sqlite3 "$PATTERNS_DB" "SELECT COUNT(*) FROM short_term_patterns" 2>/dev/null || echo "1")
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if [ "$total" -gt 0 ]; then
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echo $((high_quality * 100 / total))
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else
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echo "0"
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fi
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else
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echo "0"
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fi
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}
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optimize_patterns() {
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if [ ! -f "$PATTERNS_DB" ] || ! command -v sqlite3 &>/dev/null; then
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echo "[$(date +%H:%M:%S)] No patterns to optimize"
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return 0
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fi
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echo "[$(date +%H:%M:%S)] Running learning optimization..."
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# Boost quality of successful patterns
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sqlite3 "$PATTERNS_DB" "
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UPDATE short_term_patterns
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SET quality = MIN(1.0, quality * 1.05)
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WHERE quality > 0.5
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" 2>/dev/null || true
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# Cross-pollinate: copy strategies across similar domains
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sqlite3 "$PATTERNS_DB" "
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INSERT OR IGNORE INTO short_term_patterns (strategy, domain, quality, source)
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SELECT strategy, 'general', quality * 0.8, 'cross-pollinated'
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FROM short_term_patterns
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WHERE quality > 0.8
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LIMIT 10
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" 2>/dev/null || true
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# Calculate metrics
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local short_count=$(sqlite3 "$PATTERNS_DB" "SELECT COUNT(*) FROM short_term_patterns" 2>/dev/null || echo "0")
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local long_count=$(sqlite3 "$PATTERNS_DB" "SELECT COUNT(*) FROM long_term_patterns" 2>/dev/null || echo "0")
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local avg_quality=$(sqlite3 "$PATTERNS_DB" "SELECT ROUND(AVG(quality), 3) FROM short_term_patterns" 2>/dev/null || echo "0")
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local routing_accuracy=$(calculate_routing_accuracy)
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# Calculate intelligence score
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local pattern_score=$((short_count + long_count * 2))
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[ "$pattern_score" -gt 100 ] && pattern_score=100
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local quality_score=$(echo "$avg_quality * 40" | bc 2>/dev/null | cut -d. -f1 || echo "0")
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local intel_score=$((pattern_score * 60 / 100 + quality_score))
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[ "$intel_score" -gt 100 ] && intel_score=100
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# Write learning metrics
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cat > "$LEARNING_FILE" << EOF
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{
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"timestamp": "$(date -Iseconds)",
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"patterns": {
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"shortTerm": $short_count,
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"longTerm": $long_count,
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"avgQuality": $avg_quality
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},
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"routing": {
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"accuracy": $routing_accuracy
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},
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"intelligence": {
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"score": $intel_score,
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"level": "$([ $intel_score -lt 25 ] && echo "learning" || ([ $intel_score -lt 50 ] && echo "developing" || ([ $intel_score -lt 75 ] && echo "proficient" || echo "expert")))"
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},
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"sona": {
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"adaptationTime": "0.05ms",
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"microLoraEnabled": true
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}
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}
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EOF
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echo "[$(date +%H:%M:%S)] ✓ Learning: Intel ${intel_score}% | Patterns: $short_count/$long_count | Quality: $avg_quality | Routing: ${routing_accuracy}%"
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date +%s > "$LAST_RUN_FILE"
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}
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run_sona_training() {
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echo "[$(date +%H:%M:%S)] Spawning SONA learning agent..."
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# Use agentic-flow for deep learning optimization
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npx agentic-flow@alpha hooks intelligence 2>/dev/null || true
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echo "[$(date +%H:%M:%S)] ✓ SONA training triggered"
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}
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case "${1:-check}" in
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"run"|"optimize") optimize_patterns ;;
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"check") should_run && optimize_patterns || echo "[$(date +%H:%M:%S)] Skipping (throttled)" ;;
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"force") rm -f "$LAST_RUN_FILE"; optimize_patterns ;;
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"sona") run_sona_training ;;
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"status")
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if [ -f "$LEARNING_FILE" ]; then
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jq -r '"Intel: \(.intelligence.score)% (\(.intelligence.level)) | Patterns: \(.patterns.shortTerm)/\(.patterns.longTerm) | Routing: \(.routing.accuracy)%"' "$LEARNING_FILE"
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else
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echo "No learning data available"
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fi
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;;
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*) echo "Usage: $0 [run|check|force|sona|status]" ;;
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esac
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