How to use Devin to map database bottlenecks
Infomart used Devin's Python analysis to map API and SQL load, then hit private-dependency limits during generated test execution and moved reports to Slack.
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Suggested prompt
Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.
Investigate this repository’s database performance by mapping API entry points to SQL, writing repeatable analysis scripts, and ranking likely bottlenecks with file-level evidence. Produce scoped test designs and implementation proposals. Stop and report private-dependency failures honestly rather than claiming unexecuted tests passed; respect the agreed session budget.
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Sourced account- Source author
- Xu, Infomart development team (infomart_lab)
- Original account
- Read the source ↗
- Published
- Source link checked
- Reported result
- Devin delivered a database-performance investigation and test-design/code artifacts; unit tests could not execute because private dependencies were unreachable.
- Human involvement
- Xu supplied investigation priorities, read the returned reports and narrowed future task scope. The team set stopping rules and session budgets.
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How the agent did it
- Xu supplied database investigation priorities across the TRADE repository.
- Devin wrote analysis scripts and returned API/SQL mappings and ranked findings.
- Generated tests stopped at private dependency resolution; investigations were later exposed through Slack.
Xu from Infomart's development team asked Devin to investigate database performance across the BtoB Platform TRADE repository. The request specified the areas to inspect without prescribing individual analysis steps.
Devin wrote and executed Python scripts to map API calls to SQL, collect suspicious implementation patterns and rank table load. It returned a report prioritizing changes expected to offer useful improvements for relatively little implementation effort. The reported benefit was a repository-wide investigation artifact rather than an already-deployed performance fix.
A follow-up request asked for unit tests of the analyzed APIs, including execution. Devin produced a test-design table and code, but could not resolve dependencies from the company's internal package repository. Its cloud workspace remained outside the corporate network, and the team had not completed the available VPN setup.
Infomart subsequently connected investigations to Slack so requests and results remained in the same team thread. Xu still read and checked the output, narrowed assignments to reviewable slices and described explicit stop-and-report rules and session limits. The account demonstrates useful autonomous analysis alongside a concrete infrastructure barrier to verification.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: note · Xu, Infomart development team (infomart_lab)
Frequently asked questions
What did Devin actually deliver?
A report mapping API calls to SQL, implementation patterns, table load and prioritized improvement ideas.
Why were the tests not run?
The cloud workspace could not reach the company's internal dependency repository.
How did the team review investigations?
Xu described reading outputs, narrowing scope and using stop-and-report rules.
More details
About this account
Devin wrote and executed Python scripts to map API calls to SQL, collect suspicious implementation patterns and rank table load. It returned a report prioritizing changes expected to offer useful improvements for relatively little implementation effort. The reported benefit was a repository-wide investigation artifact rather than an already-deployed performance fix.
Japanese original opened; facts checked against original sections. Generated unit tests did not execute because private dependencies were unreachable. No measured performance improvement is claimed. Prompt is suggested by Agents In Use, not a quoted original.
Credit: Xu, Infomart development team (infomart_lab) on note
Source published
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Tools and topics
Devin · Python · Slack
Performance investigation · Network constraints
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.