sync: 190 file da Baida98/AI@47a230d7 (2026-08-26 12:19 UTC) [deploy-all] (#118)

- sync: 190 file da Baida98/AI@47a230d7 (2026-08-26 12:19 UTC) [deploy-all] (a5f882b015c6acae63ac57d462aa8398e48692bb)

benchmark-extended.mjs

@@ -1862,14 +1862,14 @@ async function makeReasoning(rng,seed){
 
 async function makeDataAnalysis(rng){
   // Time series con anomalia iniettata e ground truth calcolato
-  const n=rng.int(9,14);
-  const months=["Gen","Feb","Mar","Apr","Mag","Giu","Lug","Ago","Set","Ott","Nov","Dic"].slice(0,n);
+  const requestedN=rng.int(9,14);
+  const months=["Gen","Feb","Mar","Apr","Mag","Giu","Lug","Ago","Set","Ott","Nov","Dic"].slice(0,requestedN);
   const vals=months.map(()=>rng.int(80,320));
-  const aIdx=rng.int(2,n-2); vals[aIdx]=rng.int(5,15);
-  const avg=+(vals.reduce((a,b)=>a+b,0)/n).toFixed(1);
+  const aIdx=rng.int(2,vals.length-2); vals[aIdx]=rng.int(5,15);
+  const avg=+(vals.reduce((a,b)=>a+b,0)/vals.length).toFixed(1);
   const maxV=Math.max(...vals),maxM=months[vals.indexOf(maxV)],anomM=months[aIdx];
   const data=months.map((m,i)=>({mese:m,vendite:vals[i]}));
-  return{id:"DA",category:"data_analysis",label:`Time series ${n}m anomalia:${anomM}`,
+  return{id:"DA",category:"data_analysis",label:`Time series ${data.length}m anomalia:${anomM}`,
     hfSource:"local-ground-truth",targetMs:50000,ref:REF.data_analysis,
     prompt:`Analizza vendite mensili:\n\n${JSON.stringify(data,null,2)}\n\n` +
     `RISPONDI ESATTAMENTE CON QUESTO FORMATO (copia la struttura, sostituisci i valori):\n- **Media: N**   ← calcola la media reale (es. Media: ${avg})\n- **Picco: MESE (N)**   ← mese col valore massimo (es. Picco: ${maxM} (${maxV}))\n- **Anomalia: MESE (N)**   ← mese col valore anomalo fuori scala (es. Anomalia: ${anomM} (${vals[aIdx]}))\n- **Trend: testo breve**\n\nNon aggiungere testo prima dei 4 bullet. Usa i nomi mese: ${months.slice(0,3).join("/")}...`,