{
  "recordVersion": 1,
  "app": "Anomaly",
  "environment": "Development, not production",
  "surface": "Ordinary project chat, visible Auto routing",
  "account": "New synthetic account with FREE badge and allowance raised from 30 to 600 development test credits. No conclusion about ordinary Free-plan completion.",
  "operator": "AI evidence worker for Anomaly using isolated Playwright Chromium",
  "reviewer": "Separate AI writer for Anomaly independently checked saved input and actual output files",
  "notBlinded": true,
  "blindingDisclosure": "Operator read the rubric and reference files at 14:50:32.858 UTC, before P1. Its first direct read of the original answer key was at 14:59:40.890 UTC, after P1 was sent but before it completed. The July-specific key was first read at 15:55:27.973 UTC, after the July response completed. Writer/coordinator knew references when checking. Broader session not blinded; no expected values or corrective numerical hints sent to the app.",
  "independenceDisclosure": "Vendor-authored demonstration of our own product, outside the three-app comparison. No fourth-app ranking or production-performance claim.",
  "accountState": "Fresh synthetic project, project learning enabled; not a guarantee of isolation from shared product learning.",
  "dateUTC": "2026-10-19",
  "retrievalMethod": "Product viewer Download controls. CSV/XLSX obtained as browser downloads; controls for some other types opened signed file URLs, fetched exactly as opened. Private URLs are not published.",
  "verificationTools": "Independent CSV calculations, DuckDB SQL, openpyxl, LibreOffice 24.2.7.2 and visual inspection of original PNGs. No manual Microsoft Excel session.",
  "limits": [
    "One development configuration and synthetic dataset, not production behavior or ordinary Free-plan entitlement.",
    "No unattended refresh, scheduled delivery, live connector, statistical inference or large-file benchmark.",
    "Supplied Python requires product-only workspace_utils and failed unchanged outside Anomaly; operator adapter does not count as unchanged reproduction.",
    "Changed workbook copies were recalculated in LibreOffice. openpyxl warned reading those converted chart objects; original delivered files opened cleanly and converted chart XML contained the expected edit."
  ],
  "runId": "anomaly-dev-july-update",
  "files": [
    {
      "name": "README_jan_jul_2026.md",
      "bytes": 339,
      "sha256": "7562e9579b89a7e08941e315198c901b33c9e595b7783f87bf43e3b034d313da",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/README_jan_jul_2026.md",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "category_return_rates_jan_jul_2026.csv",
      "bytes": 364,
      "sha256": "11f7406f61e4ba0afd9c65a62685e945b2e9996613f3a3b7b5c7880d033c7e70",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/category_return_rates_jan_jul_2026.csv",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "category_sales_jan_jul_2026.csv",
      "bytes": 125,
      "sha256": "01d7c592ccf544ad300cf1f5b83cc6fa02ad00010ca088fb23c66deb6bb3b276",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/category_sales_jan_jul_2026.csv",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "monthly_sales_jan_jul_2026.csv",
      "bytes": 129,
      "sha256": "1088e8b48df80187190c980a9e69004bd9264deae62075f51340bf23f90059b3",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/monthly_sales_jan_jul_2026.csv",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "monthly_units_sold_jan_jul_2026.png",
      "bytes": 102514,
      "sha256": "32e7a9803ca41faf94f8098957ed42cc78e362079ba33c0655f59527765d9ad0",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/monthly_units_sold_jan_jul_2026.png",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "region_sales_jan_jul_2026.csv",
      "bytes": 88,
      "sha256": "ebee9f5a1474e320c107be012fd241e03c2a9db48200f9444efd434b7981b771",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/region_sales_jan_jul_2026.csv",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "reproduce_jan_jul_sales_returns.py",
      "bytes": 5310,
      "sha256": "6ae5ae7d2a3158079d97e62d91b169f9331a15a3f85ff26d0c051e68740fe7cb",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/reproduce_jan_jul_sales_returns.py",
      "kind": "Unmodified app-delivered file",
      "limitation": "Requires Anomaly-only workspace_utils; does not run unchanged outside the product."
    },
    {
      "name": "reproduce_jan_jul_sales_returns.sql",
      "bytes": 758,
      "sha256": "9d34ebcfdcca8ced888323469839bf26240054d399129b54974e1d089186e3cd",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/reproduce_jan_jul_sales_returns.sql",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    },
    {
      "name": "sales_returns_summary_jan_jul_2026.xlsx",
      "bytes": 24025,
      "sha256": "19a99908eaf3bfd57cf5990d4d96ce0b0a0a34b30bf68391939f09a5ad1240e5",
      "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/sales_returns_summary_jan_jul_2026.xlsx",
      "kind": "Unmodified app-delivered file",
      "limitation": null
    }
  ],
  "status": "One explicit additional upload and requested update completed in the existing conversation",
  "previousPeriod": "../anomaly-janjun/results.json",
  "input": {
    "name": "july-update.xlsx",
    "sha256": "3f83da511514e20f561dc8c2b24b174732028d4e4adf5b3059938d993061a248",
    "url": "../july-update.xlsx",
    "handling": "Added 1,000 sales rows and 50 return events as separate new tables; original tables retained. New returns can relate to earlier sales."
  },
  "reference": "../january-july-reference-values.csv",
  "prompt": {
    "text": "I have added the July 2026 update to this project: the file july-update.xlsx, with new rows for the Sales and Returns sheets. Please produce the same checked summary again, now including July, using the same data dictionary and the same duplicate rule as before. Report units sold by month, by category and by region plus the grand total, and the return rate for each category (returned units, sold units and the rate as a percentage to two decimal places, undefined where it cannot be defined), keeping every category from the Categories sheet. Show the row counts before and after removing duplicates, make the chart for January to July 2026, and give me the updated downloadable files, including an XLSX summary workbook. Do not fill any months after July with zeros or estimates.",
    "sha256": "6da047e7d3a7c3562f5297d1f9f192e4bae4049e2dab7211f25f7eea8793d6e4",
    "frozen": false,
    "note": "Operator-authored explicit update request, not a pre-registered repeated comparison prompt; no reference answers supplied.",
    "sentUTC": "2026-10-19T15:48:40.677Z",
    "completedUTC": "2026-10-19T15:52:18.467Z"
  },
  "scoringNote": "Changed input and operator-authored prompt: no original-22-check aggregate score. All checked table/chart values match the July reference; full Python portability fails while monthly SQL succeeds.",
  "verification": {
    "grandSoldUnits": 35832,
    "julySoldUnits": 2726,
    "monthlySoldUnits": [
      3456,
      3761,
      4872,
      5643,
      6815,
      8559,
      2726
    ],
    "salesRows": [
      13120,
      13000
    ],
    "returnsEvents": 650,
    "returnedUnits": 1245,
    "allFourCsvTablesAndWorkbookValuesMatchReference": true,
    "noSalesCategoryRate": "Undefined, with explanation",
    "formulas": 21,
    "nativeCharts": 1,
    "exportedSQLScope": "Monthly sold units only; no executable category/region/rate calculation in this SQL file",
    "controlledEdit": "On a copy, Monthly Sales!B8 2,726→2,727; total 35,833 and chart updated. Aggregate edit only; category/region sheets and findings stayed static.",
    "originalJanJunFilesRetained": [
      {
        "name": "monthly_units_sold_jan_jun_2026.csv",
        "sha256": "46542fe238e08dfe74510e7a5a47bc34c5fd49a99cd165c0c4c15fab70a82b28",
        "byteIdentical": true
      },
      {
        "name": "monthly_units_sold_summary.xlsx",
        "sha256": "f1f17909a2993587d925c753091bf81b62cc05bf1f44690110e7b93d58171fe7",
        "byteIdentical": true
      }
    ]
  },
  "notDemonstrated": "Unattended refresh, scheduled delivery, replacing an existing report or running the same July input through the three comparison apps.",
  "screenshot": {
    "file": "anomaly-09-july-xlsx-export-viewer.png",
    "sha256": "59e837cd46abcf33743e9aa54fbe178f3b15698c83be010c1bbf4653c67211d3",
    "url": "/blog/best-ai-model-for-data-analysis/anomaly-july/anomaly-09-july-xlsx-export-viewer.png",
    "note": "Actual requested-update workbook viewer and chat summary. Visible total 35,832, 13,000 deduplicated rows, 120 duplicates. Chart is clipped on the right; inspect the separately supplied full PNG for all seven values."
  }
}