ChatGPT agent analyses six Indian budget years
Sankrityayan reports that ChatGPT agent collected six years of Indian budget allocations from government reports into a spreadsheet and produced agriculture and sector-comparison charts.
Original prompt
Shared by the source author. See source notes for context.
Create a comprehensive spreadsheet and analysis of the Indian Union Finance Budgets from 2020 to 2025, focusing on sector-wise allocations and trends.
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Sourced account- Source author
- Vasu Deo Sankrityayan
- Original account
- Read the source ↗
- Published
- Source link checked
- Reported result
- Sankrityayan reports that ChatGPT agent collected six years of Indian budget allocations from government reports into a spreadsheet and produced agriculture and sector-comparison charts.
- Human involvement
- The author reviews the output and links the workbook. The source does not establish independent validation of every value, unit or cross-year sector definition.
A linked account supports attribution. It does not mean we independently reproduced its result.
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How the agent did it
- Specify the 2020–2025 government budget sources and allocation fields.
- Collect year, sector and currency-labelled allocation data.
- Build the workbook and calculate cross-year changes.
Vasu Deo Sankrityayan tests ChatGPT agent on India's Union Finance Budgets from 2020 through 2025. The brief specifies sector-wise allocations, labelled currency units, an agriculture trend and a comparison of absolute and percentage changes between the first and last years.
The author reports watching the agent consult government budget reports for all six years. It then creates a spreadsheet, a summary table, an agriculture-allocation plot and a sector-comparison bar graph. The original article links the spreadsheet, making the completed artifact more concrete than a claim that the model merely answered a budget question.
The reported eighteen-minute run is the author's observation, not a controlled timing benchmark. The review praises government sources but does not provide an independent row-by-row validation of the extracted figures, unit consistency or cross-year sector definitions. The useful brief therefore names the years, output columns, units and desired calculations before the agent collects data. A reviewer still needs to inspect whether a numerical change reflects comparable allocations rather than a renamed or reorganised sector. The original prompt excerpt below begins the actual tested instruction; the full source also defines the chart and summary requirements.
Prompt
Original prompt
Source: Blog · Vasu Deo Sankrityayan
Frequently asked questions
Which sources were used?
The author reports that the agent retrieved the allocation information from government budget reports.
What artifacts were produced?
A spreadsheet, summary table, agriculture-allocation plot and sector-comparison bar graph.
Is the eighteen-minute run a benchmark?
No. It is the author’s timing for the test, and the review does not independently audit every extracted figure.
More details
Remaining steps
- Generate the agriculture trend and sector-comparison charts.
- Inspect values, units and sector comparability before using the analysis.
About this account
ChatGPT agent retrieves government reports across six years, organises extracted allocations and produces spreadsheet calculations and charts.
Firsthand account by Vasu Deo Sankrityayan; original publication date verified against Public HTML datePublished 2025-07-25T12:24:26+00:00; Jul28 update is not substituted. Outcomes are the author’s reports, not independently reproduced. Original opening prompt excerpt; the full task also specifies calculations, charts and summary requirements.
Credit: Vasu Deo Sankrityayan
Source published
Added to Agents In Use
Source checked
Tools and topics
ChatGPT agent · Government budget reports · Spreadsheet · Analysis charts
Budget allocations · Six-year comparison · Source-unit checks
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.