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All rights reserved.",{"text":651,"caption":652},"Measure what you keep, and then grow what you measure.","The DataGlass team",{"label":654,"prompt":655,"assistants":656},"Ask AI about DataGlass","I want to learn how to use DataGlass, a decision engine for Shopee, Lazada and TikTok Shop sellers that optimizes ads, pricing and promotions for true profit. Use web search to read the DataGlass docs at https:\u002F\u002Flearn.dataglasslabs.com\u002Fdocs (with an agent-readable summary at https:\u002F\u002Flearn.dataglasslabs.com\u002Fllms.txt), then explain what DataGlass does, how to get started and which guides to read first.",[657,661,665],{"label":658,"icon":659,"url":660},"ChatGPT","chatgpt","https:\u002F\u002Fchatgpt.com\u002F?prompt={prompt}",{"label":662,"icon":663,"url":664},"Claude","claude","https:\u002F\u002Fclaude.ai\u002Fnew?q={prompt}",{"label":666,"icon":667,"url":668},"Perplexity","perplexity","https:\u002F\u002Fwww.perplexity.ai\u002Fsearch?q={prompt}",{"sections":670,"navLabel":683,"openApp":643,"search":684},[671,674,675,677,679,681],{"key":672,"label":673},"home","Home",{"key":7,"label":5},{"key":676,"label":617},"use-cases",{"key":678,"label":620},"training",{"key":680,"label":77},"releases",{"key":682,"label":623},"resources","Sections","Start searching",{"label":686},"Region and language",{},"en\u002Fchrome","mobUs3s2Zj3jKhQlnWk_tray3mh7L0ptp0ZGYXlK4NA",{"id":691,"title":499,"body":692,"description":2144,"draft":11,"draftReason":2145,"extension":2146,"layout":2145,"meta":2147,"navigation":2148,"path":500,"platforms":2145,"rawbody":2149,"seo":2150,"stem":501,"__hash__":2151},"docs_en\u002Fdocs\u002F04.ai-and-api\u002F01.assistants\u002F04.prompt-library.md",{"type":693,"value":694,"toc":2124},"minimark",[695,712,725,732,738,743,753,772,832,838,842,849,866,928,933,937,944,952,1014,1019,1023,1030,1039,1091,1096,1100,1107,1117,1130,1192,1205,1209,1216,1221,1273,1286,1290,1297,1313,1365,1370,1374,1381,1397,1449,1454,1458,1465,1481,1533,1538,1542,1547,1562,1614,1622,1626,1630,1650,1702,1707,1711,1715,1730,1781,1786,1790,1802,1811,1863,1879,1883,1887,1896,1948,1959,1963,1970,1978,2020,2036,2040,2043,2053],[696,697,698,699,703,704,707,708,711],"p",{},"These prompts are written in business language, not tool syntax. Replace placeholders such as ",[700,701,702],"code",{},"{shopName}",", ",[700,705,706],{},"{period}",", and ",[700,709,710],{},"{productName}"," when you know them. If you do not specify a shop, the assistant should discover the shops available to your DataGlass account before continuing.",[696,713,714,715,720,721,724],{},"If you are new to the connector, use the ",[716,717,719],"a",{"href":718},"\u002Fdocs\u002Fai-and-api\u002Fassistants#try-an-interactive-example","interactive prompt lab"," to build a question and preview its tool route. The ",[716,722,723],{"href":515},"worked examples"," then show six complete answers with evidence and quality checks.",[726,727,729],"callout",{"icon":728},"i-lucide-copy",[696,730,731],{},"Start with one question. Ask for a deeper breakdown only when the first answer identifies a useful lead; this keeps answers focused and avoids unnecessary data calls.",[696,733,734,735,737],{},"Most of these prompts are answered by reading alone. A few end in a proposed change — a target ROAS, a price move, a reorder. Those only execute if your connector holds that domain's write scope and you approve the specific change; see ",[716,736,509],{"href":510},".",[739,740,742],"h2",{"id":741},"_1-get-started-and-discover-shops","1. Get started and discover shops",[696,744,745,749,750],{},[746,747,748],"strong",{},"Native playbook:"," ",[700,751,752],{},"getting-started",[696,754,755,758,759,762,763,703,766,707,769,737],{},[746,756,757],{},"Workflow:"," retrieve ",[700,760,761],{},"get_playbook"," when guidance is needed, then use one or more of ",[700,764,765],{},"list_shopee_shops",[700,767,768],{},"list_lazada_shops",[700,770,771],{},"list_tiktok_shops",[773,774,775,788],"table",{},[776,777,778],"thead",{},[779,780,781,785],"tr",{},[782,783,784],"th",{},"Scenario",[782,786,787],{},"Prompt",[789,790,791,802,812,822],"tbody",{},[779,792,793,797],{},[794,795,796],"td",{},"First connection",[794,798,799],{},[700,800,801],{},"Show me all of my connected shops and group them by marketplace.",[779,803,804,807],{},[794,805,806],{},"Choose a shop",[794,808,809],{},[700,810,811],{},"Which of my shops can I analyze, and what region and currency does each use?",[779,813,814,817],{},[794,815,816],{},"Understand coverage",[794,818,819],{},[700,820,821],{},"What can DataGlass analyze for my Shopee, Lazada, and TikTok shops?",[779,823,824,827],{},[794,825,826],{},"Selected workspace",[794,828,829],{},[700,830,831],{},"Give me a quick guide to the data available for {shopName}.",[696,833,834,837],{},[746,835,836],{},"Good answer:"," show names and platforms first. Keep raw IDs as supporting metadata unless the user requests them.",[739,839,841],{"id":840},"_2-check-shop-health","2. Check shop health",[696,843,844,749,846],{},[746,845,748],{},[700,847,848],{},"shop-health-check",[696,850,851,853,854,857,858,861,862,865],{},[746,852,757],{}," discover the shop, then call ",[700,855,856],{},"diagnose_shop",". Follow a signal's ",[700,859,860],{},"suggestedTool"," only when more evidence is needed. Use ",[700,863,864],{},"get_financial_overview"," for an all-shops rollup.",[773,867,868,876],{},[776,869,870],{},[779,871,872,874],{},[782,873,784],{},[782,875,787],{},[789,877,878,888,898,908,918],{},[779,879,880,883],{},[794,881,882],{},"Selected shop snapshot",[794,884,885],{},[700,886,887],{},"How is {shopName} doing over the last 30 days? Flag anything that needs attention.",[779,889,890,893],{},[794,891,892],{},"Week-over-week check",[794,894,895],{},[700,896,897],{},"Give me a health check for {shopName} this week versus the previous week.",[779,899,900,903],{},[794,901,902],{},"Risk scan",[794,904,905],{},[700,906,907],{},"Is anything wrong with {shopName} right now? Focus on profit, GMV, margin, COGS coverage, and loss-makers.",[779,909,910,913],{},[794,911,912],{},"Portfolio view",[794,914,915],{},[700,916,917],{},"Summarize realized profit and margin across all of my shops for {period}.",[779,919,920,923],{},[794,921,922],{},"Executive summary",[794,924,925],{},[700,926,927],{},"Give me the three most important things I should know about {shopName} today.",[696,929,930,932],{},[746,931,836],{}," lead with a one-line conclusion, label warning or critical signals, and avoid dumping full product tables unless asked.",[739,934,936],{"id":935},"_3-explain-a-profit-gmv-or-sales-drop","3. Explain a profit, GMV, or sales drop",[696,938,939,749,941],{},[746,940,748],{},[700,942,943],{},"why-profit-dropped",[696,945,946,948,949,951],{},[746,947,757],{}," start with ",[700,950,856],{},", then use the relevant product ranking, breakdown, cost, COGS, or ads tools. Compare with the immediately preceding equal-length period unless the prompt names another baseline.",[773,953,954,962],{},[776,955,956],{},[779,957,958,960],{},[782,959,784],{},[782,961,787],{},[789,963,964,974,984,994,1004],{},[779,965,966,969],{},[794,967,968],{},"Profit decline",[794,970,971],{},[700,972,973],{},"Why did realized profit drop for {shopName} in {period}? Rank the biggest drivers.",[779,975,976,979],{},[794,977,978],{},"Sales decline",[794,980,981],{},[700,982,983],{},"Sales are down for {shopName}. Tell me whether the cause is fewer orders, lower revenue per order, ads, or product mix.",[779,985,986,989],{},[794,987,988],{},"Margin compression",[794,990,991],{},[700,992,993],{},"Why is margin lower for {shopName} even though GMV is stable? Break down the cost lines that changed.",[779,995,996,999],{},[794,997,998],{},"Product movers",[794,1000,1001],{},[700,1002,1003],{},"Which products contributed most to the profit change for {shopName} in {period}?",[779,1005,1006,1009],{},[794,1007,1008],{},"Data-quality check",[794,1010,1011],{},[700,1012,1013],{},"Could the apparent profit drop for {shopName} be caused by missing COGS or settlement lag?",[696,1015,1016,1018],{},[746,1017,836],{}," separate real business movement from missing-cost or settlement artifacts, and state the strongest driver first.",[739,1020,1022],{"id":1021},"_4-improve-low-margin-or-loss-making-products","4. Improve low-margin or loss-making products",[696,1024,1025,749,1027],{},[746,1026,748],{},[700,1028,1029],{},"improve-low-margin",[696,1031,1032,1034,1035,1038],{},[746,1033,757],{}," use the platform financial overview and product breakdown or ranking, confirm ",[700,1036,1037],{},"get_product_costs",", run the relevant profit simulation, then retrieve margin-defense or price-buffer recommendations when useful.",[773,1040,1041,1049],{},[776,1042,1043],{},[779,1044,1045,1047],{},[782,1046,784],{},[782,1048,787],{},[789,1050,1051,1061,1071,1081],{},[779,1052,1053,1056],{},[794,1054,1055],{},"Find loss-makers",[794,1057,1058],{},[700,1059,1060],{},"Which products are losing the most money in {shopName} over {period}, and why?",[779,1062,1063,1066],{},[794,1064,1065],{},"Thin-margin review",[794,1067,1068],{},[700,1069,1070],{},"Find products in {shopName} with dangerously low margin and rank them by profit impact.",[779,1072,1073,1076],{},[794,1074,1075],{},"Remediation options",[794,1077,1078],{},[700,1079,1080],{},"For the three worst-margin products in {shopName}, compare a price increase, a smaller discount, and a COGS reduction.",[779,1082,1083,1086],{},[794,1084,1085],{},"Cost validation",[794,1087,1088],{},[700,1089,1090],{},"Before recommending changes, verify whether the worst products have explicit or estimated COGS.",[696,1092,1093,1095],{},[746,1094,836],{}," do not recommend a price move based on inferred COGS without clearly stating the uncertainty.",[739,1097,1099],{"id":1098},"_5-diagnose-and-optimize-ads","5. Diagnose and optimize ads",[696,1101,1102,749,1104],{},[746,1103,748],{},[700,1105,1106],{},"debug-ads",[696,1108,1109,1112,1113,1116],{},[746,1110,1111],{},"Shopee workflow:"," use ",[700,1114,1115],{},"get_shopee_ads_performance",", True or profit-adjusted ROAS, then the relevant ads, campaign-floor, target-ROAS, GMS, or boost recommendation tools.",[696,1118,1119,1112,1122,1125,1126,1129],{},[746,1120,1121],{},"TikTok workflow:",[700,1123,1124],{},"get_tiktok_gmv_max_campaigns",", optional session drilldown, ",[700,1127,1128],{},"get_tiktok_campaign_roas",", then budget or target-ROAS recommendations.",[773,1131,1132,1140],{},[776,1133,1134],{},[779,1135,1136,1138],{},[782,1137,784],{},[782,1139,787],{},[789,1141,1142,1152,1162,1172,1182],{},[779,1143,1144,1147],{},[794,1145,1146],{},"Weak campaigns",[794,1148,1149],{},[700,1150,1151],{},"Which ad campaigns in {shopName} have the weakest true ROAS in {period}?",[779,1153,1154,1157],{},[794,1155,1156],{},"Hidden losses",[794,1158,1159],{},[700,1160,1161],{},"Find campaigns that look good on marketplace ROAS but lose money after COGS, fees, cancellations, and returns.",[779,1163,1164,1167],{},[794,1165,1166],{},"Budget allocation",[794,1168,1169],{},[700,1170,1171],{},"Where should I increase, hold, or reduce ad budget for {shopName}? Explain the profit evidence.",[779,1173,1174,1177],{},[794,1175,1176],{},"Bid or target guidance",[794,1178,1179],{},[700,1180,1181],{},"Show the recommended bid or target ROAS for the weakest campaigns in {shopName}.",[779,1183,1184,1187],{},[794,1185,1186],{},"TikTok drilldown",[794,1188,1189],{},[700,1190,1191],{},"Review the GMV Max campaigns and product sessions for {shopName} and identify wasted spend.",[696,1193,1194,1196,1197,1200,1201,1204],{},[746,1195,836],{}," optimize on True or profit-adjusted ROAS, not headline ROAS. A recommendation is a proposal until someone applies it — in DataGlass, or through a tool such as ",[700,1198,1199],{},"apply_shopee_target_roas"," when ",[700,1202,1203],{},"PAT_ADS_WRITE"," is granted and the user has agreed to the specific change.",[739,1206,1208],{"id":1207},"_6-defend-margin-from-commission-changes","6. Defend margin from commission changes",[696,1210,1211,749,1213],{},[746,1212,748],{},[700,1214,1215],{},"defend-margin-commission",[696,1217,1218,1220],{},[746,1219,757],{}," retrieve the platform commission overview and impact, then the matching margin-defense actions and a simulation for selected products.",[773,1222,1223,1231],{},[776,1224,1225],{},[779,1226,1227,1229],{},[782,1228,784],{},[782,1230,787],{},[789,1232,1233,1243,1253,1263],{},[779,1234,1235,1238],{},[794,1236,1237],{},"Upcoming increase",[794,1239,1240],{},[700,1241,1242],{},"How much will the upcoming commission change cost {shopName}, by product?",[779,1244,1245,1248],{},[794,1246,1247],{},"Preserve target margin",[794,1249,1250],{},[700,1251,1252],{},"What price or discount changes would preserve margin after the commission increase for {shopName}?",[779,1254,1255,1258],{},[794,1256,1257],{},"Prioritize action",[794,1259,1260],{},[700,1261,1262],{},"Rank the products in {shopName} most exposed to the new commission rate and suggest the smallest safe adjustment.",[779,1264,1265,1268],{},[794,1266,1267],{},"Compare tactics",[794,1269,1270],{},[700,1271,1272],{},"For affected products in {shopName}, compare raising price with trimming discount before I decide.",[696,1274,1275,1277,1278,1281,1282,1285],{},[746,1276,836],{}," include the effective date, current and new rate when available, profit impact, and the proposed margin-defense move. If the connector holds ",[700,1279,1280],{},"PAT_PRICING_WRITE",", it can apply that move with ",[700,1283,1284],{},"apply_margin_defense_action"," — it should show the per-variant prices and get an explicit go-ahead first.",[739,1287,1289],{"id":1288},"_7-plan-inventory-and-restocking","7. Plan inventory and restocking",[696,1291,1292,749,1294],{},[746,1293,748],{},[700,1295,1296],{},"plan-restock",[696,1298,1299,1301,1302,703,1305,1308,1309,1312],{},[746,1300,757],{}," call ",[700,1303,1304],{},"get_inventory_alerts",[700,1306,1307],{},"get_reorder_plan",", a forecast or demand chart, then ",[700,1310,1311],{},"get_open_purchase_orders"," to avoid double ordering.",[773,1314,1315,1323],{},[776,1316,1317],{},[779,1318,1319,1321],{},[782,1320,784],{},[782,1322,787],{},[789,1324,1325,1335,1345,1355],{},[779,1326,1327,1330],{},[794,1328,1329],{},"Immediate risk",[794,1331,1332],{},[700,1333,1334],{},"Which SKUs need restocking now, and how urgent is each one?",[779,1336,1337,1340],{},[794,1338,1339],{},"Reorder quantities",[794,1341,1342],{},[700,1343,1344],{},"Build a reorder plan using current stock, reorder points, forecast demand, and inbound purchase orders.",[779,1346,1347,1350],{},[794,1348,1349],{},"Demand explanation",[794,1351,1352],{},[700,1353,1354],{},"Explain why {productName} needs its recommended reorder quantity using historical and forecast demand.",[779,1356,1357,1360],{},[794,1358,1359],{},"Overstock review",[794,1361,1362],{},[700,1363,1364],{},"Which products appear overstocked or slow-moving, and what evidence supports that?",[696,1366,1367,1369],{},[746,1368,836],{}," subtract or explicitly show inbound stock and distinguish open alerts from resolved alert history.",[739,1371,1373],{"id":1372},"_8-audit-cogs-and-margin-confidence","8. Audit COGS and margin confidence",[696,1375,1376,749,1378],{},[746,1377,748],{},[700,1379,1380],{},"check-cogs",[696,1382,1383,1385,1386,1389,1390,1392,1393,1396],{},[746,1384,757],{}," read shop financial COGS coverage, resolve canonical products with ",[700,1387,1388],{},"list_products",", use ",[700,1391,1037],{},", and retrieve ",[700,1394,1395],{},"get_estimated_margin"," for Shopee shop-level inference when needed.",[773,1398,1399,1407],{},[776,1400,1401],{},[779,1402,1403,1405],{},[782,1404,784],{},[782,1406,787],{},[789,1408,1409,1419,1429,1439],{},[779,1410,1411,1414],{},[794,1412,1413],{},"Missing costs",[794,1415,1416],{},[700,1417,1418],{},"Which products in {shopName} are missing explicit COGS?",[779,1420,1421,1424],{},[794,1422,1423],{},"Profit confidence",[794,1425,1426],{},[700,1427,1428],{},"How much of {shopName}'s profit in {period} depends on estimated rather than entered COGS?",[779,1430,1431,1434],{},[794,1432,1433],{},"Cost audit",[794,1435,1436],{},[700,1437,1438],{},"Verify the stored cost basis for {productName} and explain how it affects margin.",[779,1440,1441,1444],{},[794,1442,1443],{},"Prioritize cleanup",[794,1445,1446],{},[700,1447,1448],{},"Rank the missing-COGS products by how much they could distort reported profit.",[696,1450,1451,1453],{},[746,1452,836],{}," label explicit, inferred, and missing costs separately. Users enter costs in the DataGlass app, not through the MCP connector.",[739,1455,1457],{"id":1456},"_9-simulate-price-discount-or-cost-changes","9. Simulate price, discount, or cost changes",[696,1459,1460,749,1462],{},[746,1461,748],{},[700,1463,1464],{},"simulate-price-change",[696,1466,1467,1469,1470,703,1473,1476,1477,1480],{},[746,1468,757],{}," locate the product, read its current economics, then call exactly one of ",[700,1471,1472],{},"simulate_shopee_profit",[700,1474,1475],{},"simulate_lazada_profit",", or ",[700,1478,1479],{},"simulate_tiktok_profit"," for each proposed scenario.",[773,1482,1483,1491],{},[776,1484,1485],{},[779,1486,1487,1489],{},[782,1488,784],{},[782,1490,787],{},[789,1492,1493,1503,1513,1523],{},[779,1494,1495,1498],{},[794,1496,1497],{},"Price increase",[794,1499,1500],{},[700,1501,1502],{},"If I raise {productName}'s price by 5%, what happens to per-unit profit and margin?",[779,1504,1505,1508],{},[794,1506,1507],{},"Discount reduction",[794,1509,1510],{},[700,1511,1512],{},"Compare the current result for {productName} with reducing its discount from 15% to 10%.",[779,1514,1515,1518],{},[794,1516,1517],{},"COGS negotiation",[794,1519,1520],{},[700,1521,1522],{},"How would {productName}'s profit change if unit COGS fell by {currency} 10?",[779,1524,1525,1528],{},[794,1526,1527],{},"Break-even target",[794,1529,1530],{},[700,1531,1532],{},"What selling price would {productName} need to reach a 20% margin, based on its current fees and COGS?",[696,1534,1535,1537],{},[746,1536,836],{}," show current versus simulated values and list the assumptions. Never claim that the live listing was changed.",[739,1539,1541],{"id":1540},"_10-analyze-products-and-conversion","10. Analyze products and conversion",[696,1543,1544,1546],{},[746,1545,748],{}," none yet.",[696,1548,1549,1551,1552,1555,1556,1559,1560,737],{},[746,1550,757],{}," discover the platform shop, use a platform product list or ",[700,1553,1554],{},"search",", then the matching conversion-metrics tool. Use ",[700,1557,1558],{},"fetch"," for a complete canonical product after ",[700,1561,1554],{},[773,1563,1564,1572],{},[776,1565,1566],{},[779,1567,1568,1570],{},[782,1569,784],{},[782,1571,787],{},[789,1573,1574,1584,1594,1604],{},[779,1575,1576,1579],{},[794,1577,1578],{},"Product lookup",[794,1580,1581],{},[700,1582,1583],{},"Find {productName} across all of my connected shops and show its platform listings.",[779,1585,1586,1589],{},[794,1587,1588],{},"Funnel diagnosis",[794,1590,1591],{},[700,1592,1593],{},"Which products in {shopName} have high traffic but weak conversion in {period}?",[779,1595,1596,1599],{},[794,1597,1598],{},"Platform comparison",[794,1600,1601],{},[700,1602,1603],{},"Compare the conversion performance of {productName} across Shopee, Lazada, and TikTok where it is listed.",[779,1605,1606,1609],{},[794,1607,1608],{},"Discover opportunity",[794,1610,1611],{},[700,1612,1613],{},"Find products with improving conversion but low sales volume that may deserve more visibility.",[696,1615,1616,1112,1618,1621],{},[746,1617,836],{},[700,1619,1620],{},"YYYYMMDD"," windows for conversion-tool inputs, but display ordinary calendar dates to the user.",[739,1623,1625],{"id":1624},"_11-review-orders-and-fulfilment","11. Review orders and fulfilment",[696,1627,1628,1546],{},[746,1629,748],{},[696,1631,1632,1112,1634,1637,1638,1641,1642,1645,1646,1649],{},[746,1633,757],{},[700,1635,1636],{},"count_orders"," for a quick count, ",[700,1639,1640],{},"list_orders"," for rows, ",[700,1643,1644],{},"get_order"," for one normalized order, and ",[700,1647,1648],{},"count_unshipped_orders"," for backlog.",[773,1651,1652,1660],{},[776,1653,1654],{},[779,1655,1656,1658],{},[782,1657,784],{},[782,1659,787],{},[789,1661,1662,1672,1682,1692],{},[779,1663,1664,1667],{},[794,1665,1666],{},"Volume check",[794,1668,1669],{},[700,1670,1671],{},"How many orders did I receive across all shops in {period}, broken down by platform?",[779,1673,1674,1677],{},[794,1675,1676],{},"Fulfilment backlog",[794,1678,1679],{},[700,1680,1681],{},"How many orders are still waiting to ship, and how old is the backlog?",[779,1683,1684,1687],{},[794,1685,1686],{},"Order investigation",[794,1688,1689],{},[700,1690,1691],{},"Find the details and line items for order {order reference}.",[779,1693,1694,1697],{},[794,1695,1696],{},"Recent order list",[794,1698,1699],{},[700,1700,1701],{},"List my newest orders for {shopName} and highlight unusual totals or statuses.",[696,1703,1704,1706],{},[746,1705,836],{}," call the count tool before fetching a long list when the user asks only “how many.” Follow cursors only when more rows are necessary.",[739,1708,1710],{"id":1709},"_12-reconcile-marketplace-payouts","12. Reconcile marketplace payouts",[696,1712,1713,1546],{},[746,1714,748],{},[696,1716,1717,1719,1720,703,1723,1476,1726,1729],{},[746,1718,757],{}," discover the shop and use exactly one of ",[700,1721,1722],{},"get_shopee_payout",[700,1724,1725],{},"get_lazada_payout",[700,1727,1728],{},"get_tiktok_payout"," for the requested payout or settlement dates. Do not substitute profit tools.",[773,1731,1732,1740],{},[776,1733,1734],{},[779,1735,1736,1738],{},[782,1737,784],{},[782,1739,787],{},[789,1741,1742,1752,1761,1771],{},[779,1743,1744,1747],{},[794,1745,1746],{},"Amount paid",[794,1748,1749],{},[700,1750,1751],{},"How much did {platform} pay {shopName} in {period}?",[779,1753,1754,1756],{},[794,1755,260],{},[794,1757,1758],{},[700,1759,1760],{},"Compare DataGlass's expected payout with the marketplace-reported payout for {shopName}.",[779,1762,1763,1766],{},[794,1764,1765],{},"Investigate a delta",[794,1767,1768],{},[700,1769,1770],{},"Which orders or statements explain the payout mismatch for {shopName} in {period}?",[779,1772,1773,1776],{},[794,1774,1775],{},"Pending versus paid",[794,1777,1778],{},[700,1779,1780],{},"Separate paid and pending settlement amounts for {shopName} in {period}.",[696,1782,1783,1785],{},[746,1784,836],{}," state whether the date is a payout or settlement date, show local currency, and treat a zero reconciliation delta as an exact match. Lazada exposes a net finance-statement settlement rather than the Shopee and TikTok reconciliation report.",[739,1787,1789],{"id":1788},"_13-explore-recommendations-and-growth-opportunities","13. Explore recommendations and growth opportunities",[696,1791,1792,749,1795,703,1797,707,1799,1801],{},[746,1793,1794],{},"Native playbooks:",[700,1796,1106],{},[700,1798,1029],{},[700,1800,1215],{}," cover parts of this domain.",[696,1803,1804,1806,1807,1810],{},[746,1805,757],{}," begin with ",[700,1808,1809],{},"list_recommendations",", then retrieve the typed recommendation tool that matches the decision.",[773,1812,1813,1821],{},[776,1814,1815],{},[779,1816,1817,1819],{},[782,1818,784],{},[782,1820,787],{},[789,1822,1823,1833,1843,1853],{},[779,1824,1825,1828],{},[794,1826,1827],{},"Recommendation inbox",[794,1829,1830],{},[700,1831,1832],{},"What are the newest actionable recommendations across all of my shops?",[779,1834,1835,1838],{},[794,1836,1837],{},"Growth ideas",[794,1839,1840],{},[700,1841,1842],{},"Which products should I boost or bundle to improve discovery and basket size?",[779,1844,1845,1848],{},[794,1846,1847],{},"Margin protection",[794,1849,1850],{},[700,1851,1852],{},"Show price-buffer and margin-defense recommendations for {shopName}.",[779,1854,1855,1858],{},[794,1856,1857],{},"Ads actions",[794,1859,1860],{},[700,1861,1862],{},"Summarize the highest-impact ads recommendations for {shopName} and the evidence behind each.",[696,1864,1865,1867,1868,1871,1872,1200,1875,1878],{},[746,1866,836],{}," state recommendation status, shop and platform, expected impact, and the evidence. Applying an Action happens in DataGlass, or through a tool such as ",[700,1869,1870],{},"rollback_recommendation"," or ",[700,1873,1874],{},"stop_recommendation",[700,1876,1877],{},"PAT_RECOMMENDATION_WRITE"," is granted — never without the user's explicit agreement to that specific change.",[739,1880,1882],{"id":1881},"_14-troubleshoot-connection-or-missing-data","14. Troubleshoot connection or missing data",[696,1884,1885,1546],{},[746,1886,748],{},[696,1888,1889,1891,1892,1895],{},[746,1890,757],{}," list the platform's shops. For Shopee, use ",[700,1893,1894],{},"list_shopee_shop_accounts"," to inspect authorization by app type. Explain authentication, access, plan, and data-timing failures without retry loops.",[773,1897,1898,1906],{},[776,1899,1900],{},[779,1901,1902,1904],{},[782,1903,784],{},[782,1905,787],{},[789,1907,1908,1918,1928,1938],{},[779,1909,1910,1913],{},[794,1911,1912],{},"Missing shop",[794,1914,1915],{},[700,1916,1917],{},"Why is one of my connected shops missing from DataGlass?",[779,1919,1920,1923],{},[794,1921,1922],{},"Missing Shopee data",[794,1924,1925],{},[700,1926,1927],{},"Check whether {shopName}'s Shopee ERP or Ads authorization has expired.",[779,1929,1930,1933],{},[794,1931,1932],{},"Empty result",[794,1934,1935],{},[700,1936,1937],{},"I expected data for {shopName} in {period}. Check the shop, region, date format, authorization, and settlement timing.",[779,1939,1940,1943],{},[794,1941,1942],{},"Reconnect",[794,1944,1945],{},[700,1946,1947],{},"My DataGlass connector stopped working. Tell me whether I need to reconnect, re-authenticate, or upgrade my plan.",[696,1949,1950,1952,1953,737],{},[746,1951,836],{}," a 401 requires OAuth re-authentication; a 403 is an ownership or allowlist denial and should not trigger a re-authentication loop. A plan denial should link to ",[716,1954,1958],{"href":1955,"rel":1956},"https:\u002F\u002Fapp.dataglasslabs.com\u002Fopen\u002Fbilling",[1957],"nofollow","DataGlass billing",[739,1960,1962],{"id":1961},"_15-explain-query-conventions","15. Explain query conventions",[696,1964,1965,749,1967],{},[746,1966,748],{},[700,1968,1969],{},"conventions",[696,1971,1972,758,1974,1977],{},[746,1973,757],{},[700,1975,1976],{},"get_playbook(playbook=\"conventions\")"," when the model needs wire conventions, then translate the result into ordinary language.",[773,1979,1980,1988],{},[776,1981,1982],{},[779,1983,1984,1986],{},[782,1985,784],{},[782,1987,787],{},[789,1989,1990,2000,2010],{},[779,1991,1992,1995],{},[794,1993,1994],{},"Date help",[794,1996,1997],{},[700,1998,1999],{},"What date range will you use for \"last month,\" and what comparison period will you use?",[779,2001,2002,2005],{},[794,2003,2004],{},"Metric help",[794,2006,2007],{},[700,2008,2009],{},"Explain the difference between realized profit, payout, headline ROAS, and true ROAS.",[779,2011,2012,2015],{},[794,2013,2014],{},"Identifier help",[794,2016,2017],{},[700,2018,2019],{},"Find the correct shop and product identifiers for my request instead of asking me to look them up.",[696,2021,2022,2024,2025,2028,2029,2032,2033,2035],{},[746,2023,836],{}," human dates are ",[700,2026,2027],{},"YYYY-MM-DD",", conversion ",[700,2030,2031],{},"datadate"," values are ",[700,2034,1620],{},", timestamps are ISO 8601, and money uses the shop's local currency.",[739,2037,2039],{"id":2038},"prompt-template","Prompt template",[696,2041,2042],{},"Use this structure when creating another prompt:",[2044,2045,2051],"pre",{"className":2046,"code":2048,"language":2049,"meta":2050},[2047],"language-text","\u003Cbusiness question> for \u003Cshop or all shops> over \u003Cperiod>.\nShow \u003Cdecision metric> and explain \u003Cdesired evidence or next step>.\n","text","",[700,2052,2048],{"__ignoreMap":2050},[773,2054,2055,2065],{},[776,2056,2057],{},[779,2058,2059,2062],{},[782,2060,2061],{},"Placeholder",[782,2063,2064],{},"Use",[789,2066,2067,2076,2086,2095,2104,2114],{},[779,2068,2069,2073],{},[794,2070,2071],{},[700,2072,702],{},[794,2074,2075],{},"Human-readable shop name from the current workspace",[779,2077,2078,2083],{},[794,2079,2080],{},[700,2081,2082],{},"{platform}",[794,2084,2085],{},"Shopee, Lazada, or TikTok Shop",[779,2087,2088,2092],{},[794,2089,2090],{},[700,2091,706],{},[794,2093,2094],{},"Natural language such as “the last 30 days”",[779,2096,2097,2101],{},[794,2098,2099],{},[700,2100,710],{},[794,2102,2103],{},"Product name or SKU; let the connector resolve IDs",[779,2105,2106,2111],{},[794,2107,2108],{},[700,2109,2110],{},"{currency}",[794,2112,2113],{},"The shop's local currency, when already known",[779,2115,2116,2121],{},[794,2117,2118],{},[700,2119,2120],{},"{campaignName}",[794,2122,2123],{},"Campaign name, or ask for the weakest campaigns",{"title":2050,"searchDepth":2125,"depth":2125,"links":2126},3,[2127,2129,2130,2131,2132,2133,2134,2135,2136,2137,2138,2139,2140,2141,2142,2143],{"id":741,"depth":2128,"text":742},2,{"id":840,"depth":2128,"text":841},{"id":935,"depth":2128,"text":936},{"id":1021,"depth":2128,"text":1022},{"id":1098,"depth":2128,"text":1099},{"id":1207,"depth":2128,"text":1208},{"id":1288,"depth":2128,"text":1289},{"id":1372,"depth":2128,"text":1373},{"id":1456,"depth":2128,"text":1457},{"id":1540,"depth":2128,"text":1541},{"id":1624,"depth":2128,"text":1625},{"id":1709,"depth":2128,"text":1710},{"id":1788,"depth":2128,"text":1789},{"id":1881,"depth":2128,"text":1882},{"id":1961,"depth":2128,"text":1962},{"id":2038,"depth":2128,"text":2039},"Copy practical prompts for 15 common commerce decisions. Every pack includes at least three scenarios and the workflow an assistant should follow.",null,"md",{},{"icon":502},"---\ntitle: Prompt library\ndescription: Copy practical prompts for 15 common commerce decisions. Every pack includes at least three scenarios and the workflow an assistant should follow.\nnavigation:\n  icon: i-material-symbols-forum-rounded\n---\n\nThese prompts are written in business language, not tool syntax. Replace placeholders such as `{shopName}`, `{period}`, and `{productName}` when you know them. If you do not specify a shop, the assistant should discover the shops available to your DataGlass account before continuing.\n\nIf you are new to the connector, use the [interactive prompt lab](\u002Fdocs\u002Fai-and-api\u002Fassistants#try-an-interactive-example) to build a question and preview its tool route. The [worked examples](\u002Fdocs\u002Fai-and-api\u002Fassistants\u002Fworked-examples) then show six complete answers with evidence and quality checks.\n\n::callout{icon=\"i-lucide-copy\"}\nStart with one question. Ask for a deeper breakdown only when the first answer identifies a useful lead; this keeps answers focused and avoids unnecessary data calls.\n::\n\nMost of these prompts are answered by reading alone. A few end in a proposed change — a target ROAS, a price move, a reorder. Those only execute if your connector holds that domain's write scope and you approve the specific change; see [Permissions and safety](\u002Fdocs\u002Fai-and-api\u002Fassistants\u002Fusage-and-safety).\n\n## 1. Get started and discover shops\n\n**Native playbook:** `getting-started`\n\n**Workflow:** retrieve `get_playbook` when guidance is needed, then use one or more of `list_shopee_shops`, `list_lazada_shops`, and `list_tiktok_shops`.\n\n| Scenario | Prompt |\n| --- | --- |\n| First connection | `Show me all of my connected shops and group them by marketplace.` |\n| Choose a shop | `Which of my shops can I analyze, and what region and currency does each use?` |\n| Understand coverage | `What can DataGlass analyze for my Shopee, Lazada, and TikTok shops?` |\n| Selected workspace | `Give me a quick guide to the data available for {shopName}.` |\n\n**Good answer:** show names and platforms first. Keep raw IDs as supporting metadata unless the user requests them.\n\n## 2. Check shop health\n\n**Native playbook:** `shop-health-check`\n\n**Workflow:** discover the shop, then call `diagnose_shop`. Follow a signal's `suggestedTool` only when more evidence is needed. Use `get_financial_overview` for an all-shops rollup.\n\n| Scenario | Prompt |\n| --- | --- |\n| Selected shop snapshot | `How is {shopName} doing over the last 30 days? Flag anything that needs attention.` |\n| Week-over-week check | `Give me a health check for {shopName} this week versus the previous week.` |\n| Risk scan | `Is anything wrong with {shopName} right now? Focus on profit, GMV, margin, COGS coverage, and loss-makers.` |\n| Portfolio view | `Summarize realized profit and margin across all of my shops for {period}.` |\n| Executive summary | `Give me the three most important things I should know about {shopName} today.` |\n\n**Good answer:** lead with a one-line conclusion, label warning or critical signals, and avoid dumping full product tables unless asked.\n\n## 3. Explain a profit, GMV, or sales drop\n\n**Native playbook:** `why-profit-dropped`\n\n**Workflow:** start with `diagnose_shop`, then use the relevant product ranking, breakdown, cost, COGS, or ads tools. Compare with the immediately preceding equal-length period unless the prompt names another baseline.\n\n| Scenario | Prompt |\n| --- | --- |\n| Profit decline | `Why did realized profit drop for {shopName} in {period}? Rank the biggest drivers.` |\n| Sales decline | `Sales are down for {shopName}. Tell me whether the cause is fewer orders, lower revenue per order, ads, or product mix.` |\n| Margin compression | `Why is margin lower for {shopName} even though GMV is stable? Break down the cost lines that changed.` |\n| Product movers | `Which products contributed most to the profit change for {shopName} in {period}?` |\n| Data-quality check | `Could the apparent profit drop for {shopName} be caused by missing COGS or settlement lag?` |\n\n**Good answer:** separate real business movement from missing-cost or settlement artifacts, and state the strongest driver first.\n\n## 4. Improve low-margin or loss-making products\n\n**Native playbook:** `improve-low-margin`\n\n**Workflow:** use the platform financial overview and product breakdown or ranking, confirm `get_product_costs`, run the relevant profit simulation, then retrieve margin-defense or price-buffer recommendations when useful.\n\n| Scenario | Prompt |\n| --- | --- |\n| Find loss-makers | `Which products are losing the most money in {shopName} over {period}, and why?` |\n| Thin-margin review | `Find products in {shopName} with dangerously low margin and rank them by profit impact.` |\n| Remediation options | `For the three worst-margin products in {shopName}, compare a price increase, a smaller discount, and a COGS reduction.` |\n| Cost validation | `Before recommending changes, verify whether the worst products have explicit or estimated COGS.` |\n\n**Good answer:** do not recommend a price move based on inferred COGS without clearly stating the uncertainty.\n\n## 5. Diagnose and optimize ads\n\n**Native playbook:** `debug-ads`\n\n**Shopee workflow:** use `get_shopee_ads_performance`, True or profit-adjusted ROAS, then the relevant ads, campaign-floor, target-ROAS, GMS, or boost recommendation tools.\n\n**TikTok workflow:** use `get_tiktok_gmv_max_campaigns`, optional session drilldown, `get_tiktok_campaign_roas`, then budget or target-ROAS recommendations.\n\n| Scenario | Prompt |\n| --- | --- |\n| Weak campaigns | `Which ad campaigns in {shopName} have the weakest true ROAS in {period}?` |\n| Hidden losses | `Find campaigns that look good on marketplace ROAS but lose money after COGS, fees, cancellations, and returns.` |\n| Budget allocation | `Where should I increase, hold, or reduce ad budget for {shopName}? Explain the profit evidence.` |\n| Bid or target guidance | `Show the recommended bid or target ROAS for the weakest campaigns in {shopName}.` |\n| TikTok drilldown | `Review the GMV Max campaigns and product sessions for {shopName} and identify wasted spend.` |\n\n**Good answer:** optimize on True or profit-adjusted ROAS, not headline ROAS. A recommendation is a proposal until someone applies it — in DataGlass, or through a tool such as `apply_shopee_target_roas` when `PAT_ADS_WRITE` is granted and the user has agreed to the specific change.\n\n## 6. Defend margin from commission changes\n\n**Native playbook:** `defend-margin-commission`\n\n**Workflow:** retrieve the platform commission overview and impact, then the matching margin-defense actions and a simulation for selected products.\n\n| Scenario | Prompt |\n| --- | --- |\n| Upcoming increase | `How much will the upcoming commission change cost {shopName}, by product?` |\n| Preserve target margin | `What price or discount changes would preserve margin after the commission increase for {shopName}?` |\n| Prioritize action | `Rank the products in {shopName} most exposed to the new commission rate and suggest the smallest safe adjustment.` |\n| Compare tactics | `For affected products in {shopName}, compare raising price with trimming discount before I decide.` |\n\n**Good answer:** include the effective date, current and new rate when available, profit impact, and the proposed margin-defense move. If the connector holds `PAT_PRICING_WRITE`, it can apply that move with `apply_margin_defense_action` — it should show the per-variant prices and get an explicit go-ahead first.\n\n## 7. Plan inventory and restocking\n\n**Native playbook:** `plan-restock`\n\n**Workflow:** call `get_inventory_alerts`, `get_reorder_plan`, a forecast or demand chart, then `get_open_purchase_orders` to avoid double ordering.\n\n| Scenario | Prompt |\n| --- | --- |\n| Immediate risk | `Which SKUs need restocking now, and how urgent is each one?` |\n| Reorder quantities | `Build a reorder plan using current stock, reorder points, forecast demand, and inbound purchase orders.` |\n| Demand explanation | `Explain why {productName} needs its recommended reorder quantity using historical and forecast demand.` |\n| Overstock review | `Which products appear overstocked or slow-moving, and what evidence supports that?` |\n\n**Good answer:** subtract or explicitly show inbound stock and distinguish open alerts from resolved alert history.\n\n## 8. Audit COGS and margin confidence\n\n**Native playbook:** `check-cogs`\n\n**Workflow:** read shop financial COGS coverage, resolve canonical products with `list_products`, use `get_product_costs`, and retrieve `get_estimated_margin` for Shopee shop-level inference when needed.\n\n| Scenario | Prompt |\n| --- | --- |\n| Missing costs | `Which products in {shopName} are missing explicit COGS?` |\n| Profit confidence | `How much of {shopName}'s profit in {period} depends on estimated rather than entered COGS?` |\n| Cost audit | `Verify the stored cost basis for {productName} and explain how it affects margin.` |\n| Prioritize cleanup | `Rank the missing-COGS products by how much they could distort reported profit.` |\n\n**Good answer:** label explicit, inferred, and missing costs separately. Users enter costs in the DataGlass app, not through the MCP connector.\n\n## 9. Simulate price, discount, or cost changes\n\n**Native playbook:** `simulate-price-change`\n\n**Workflow:** locate the product, read its current economics, then call exactly one of `simulate_shopee_profit`, `simulate_lazada_profit`, or `simulate_tiktok_profit` for each proposed scenario.\n\n| Scenario | Prompt |\n| --- | --- |\n| Price increase | `If I raise {productName}'s price by 5%, what happens to per-unit profit and margin?` |\n| Discount reduction | `Compare the current result for {productName} with reducing its discount from 15% to 10%.` |\n| COGS negotiation | `How would {productName}'s profit change if unit COGS fell by {currency} 10?` |\n| Break-even target | `What selling price would {productName} need to reach a 20% margin, based on its current fees and COGS?` |\n\n**Good answer:** show current versus simulated values and list the assumptions. Never claim that the live listing was changed.\n\n## 10. Analyze products and conversion\n\n**Native playbook:** none yet.\n\n**Workflow:** discover the platform shop, use a platform product list or `search`, then the matching conversion-metrics tool. Use `fetch` for a complete canonical product after `search`.\n\n| Scenario | Prompt |\n| --- | --- |\n| Product lookup | `Find {productName} across all of my connected shops and show its platform listings.` |\n| Funnel diagnosis | `Which products in {shopName} have high traffic but weak conversion in {period}?` |\n| Platform comparison | `Compare the conversion performance of {productName} across Shopee, Lazada, and TikTok where it is listed.` |\n| Discover opportunity | `Find products with improving conversion but low sales volume that may deserve more visibility.` |\n\n**Good answer:** use `YYYYMMDD` windows for conversion-tool inputs, but display ordinary calendar dates to the user.\n\n## 11. Review orders and fulfilment\n\n**Native playbook:** none yet.\n\n**Workflow:** use `count_orders` for a quick count, `list_orders` for rows, `get_order` for one normalized order, and `count_unshipped_orders` for backlog.\n\n| Scenario | Prompt |\n| --- | --- |\n| Volume check | `How many orders did I receive across all shops in {period}, broken down by platform?` |\n| Fulfilment backlog | `How many orders are still waiting to ship, and how old is the backlog?` |\n| Order investigation | `Find the details and line items for order {order reference}.` |\n| Recent order list | `List my newest orders for {shopName} and highlight unusual totals or statuses.` |\n\n**Good answer:** call the count tool before fetching a long list when the user asks only “how many.” Follow cursors only when more rows are necessary.\n\n## 12. Reconcile marketplace payouts\n\n**Native playbook:** none yet.\n\n**Workflow:** discover the shop and use exactly one of `get_shopee_payout`, `get_lazada_payout`, or `get_tiktok_payout` for the requested payout or settlement dates. Do not substitute profit tools.\n\n| Scenario | Prompt |\n| --- | --- |\n| Amount paid | `How much did {platform} pay {shopName} in {period}?` |\n| Reconciliation | `Compare DataGlass's expected payout with the marketplace-reported payout for {shopName}.` |\n| Investigate a delta | `Which orders or statements explain the payout mismatch for {shopName} in {period}?` |\n| Pending versus paid | `Separate paid and pending settlement amounts for {shopName} in {period}.` |\n\n**Good answer:** state whether the date is a payout or settlement date, show local currency, and treat a zero reconciliation delta as an exact match. Lazada exposes a net finance-statement settlement rather than the Shopee and TikTok reconciliation report.\n\n## 13. Explore recommendations and growth opportunities\n\n**Native playbooks:** `debug-ads`, `improve-low-margin`, and `defend-margin-commission` cover parts of this domain.\n\n**Workflow:** begin with `list_recommendations`, then retrieve the typed recommendation tool that matches the decision.\n\n| Scenario | Prompt |\n| --- | --- |\n| Recommendation inbox | `What are the newest actionable recommendations across all of my shops?` |\n| Growth ideas | `Which products should I boost or bundle to improve discovery and basket size?` |\n| Margin protection | `Show price-buffer and margin-defense recommendations for {shopName}.` |\n| Ads actions | `Summarize the highest-impact ads recommendations for {shopName} and the evidence behind each.` |\n\n**Good answer:** state recommendation status, shop and platform, expected impact, and the evidence. Applying an Action happens in DataGlass, or through a tool such as `rollback_recommendation` or `stop_recommendation` when `PAT_RECOMMENDATION_WRITE` is granted — never without the user's explicit agreement to that specific change.\n\n## 14. Troubleshoot connection or missing data\n\n**Native playbook:** none yet.\n\n**Workflow:** list the platform's shops. For Shopee, use `list_shopee_shop_accounts` to inspect authorization by app type. Explain authentication, access, plan, and data-timing failures without retry loops.\n\n| Scenario | Prompt |\n| --- | --- |\n| Missing shop | `Why is one of my connected shops missing from DataGlass?` |\n| Missing Shopee data | `Check whether {shopName}'s Shopee ERP or Ads authorization has expired.` |\n| Empty result | `I expected data for {shopName} in {period}. Check the shop, region, date format, authorization, and settlement timing.` |\n| Reconnect | `My DataGlass connector stopped working. Tell me whether I need to reconnect, re-authenticate, or upgrade my plan.` |\n\n**Good answer:** a 401 requires OAuth re-authentication; a 403 is an ownership or allowlist denial and should not trigger a re-authentication loop. A plan denial should link to [DataGlass billing](https:\u002F\u002Fapp.dataglasslabs.com\u002Fopen\u002Fbilling).\n\n## 15. Explain query conventions\n\n**Native playbook:** `conventions`\n\n**Workflow:** retrieve `get_playbook(playbook=\"conventions\")` when the model needs wire conventions, then translate the result into ordinary language.\n\n| Scenario | Prompt |\n| --- | --- |\n| Date help | `What date range will you use for \"last month,\" and what comparison period will you use?` |\n| Metric help | `Explain the difference between realized profit, payout, headline ROAS, and true ROAS.` |\n| Identifier help | `Find the correct shop and product identifiers for my request instead of asking me to look them up.` |\n\n**Good answer:** human dates are `YYYY-MM-DD`, conversion `datadate` values are `YYYYMMDD`, timestamps are ISO 8601, and money uses the shop's local currency.\n\n## Prompt template\n\nUse this structure when creating another prompt:\n\n```text\n\u003Cbusiness question> for \u003Cshop or all shops> over \u003Cperiod>.\nShow \u003Cdecision metric> and explain \u003Cdesired evidence or next step>.\n```\n\n| Placeholder | Use |\n| --- | --- |\n| `{shopName}` | Human-readable shop name from the current workspace |\n| `{platform}` | Shopee, Lazada, or TikTok Shop |\n| `{period}` | Natural language such as “the last 30 days” |\n| `{productName}` | Product name or SKU; let the connector resolve IDs |\n| `{currency}` | The shop's local currency, when already known |\n| `{campaignName}` | Campaign name, or ask for the weakest campaigns |\n",{"title":499,"description":2144},"rVLKU2fmpdoseC59-OLeXrehkuurMwj3phKh4ZSUWe8",{"data":2153,"body":2154},{},{"type":2155,"children":2156},"root",[2157],{"type":2158,"tag":696,"props":2159,"children":2160},"element",{},[2161],{"type":2049,"value":2144},1792785190413]