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dependabot.yml

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Tools

The configuration file used to control Dependabot update schedules, grouping, and limits.

Why it's here

Placed in Trial: 6 article(s) of evidence from 5 source(s), led by security coverage, with 3 in the last 30 days. Confidence 76%.

Evidence (6)

  • 8GitHub Blog·8/6/2026security
    GitHub expands malware advisories beyond npm

    GitHub says Dependabot malware advisories now cover eight package ecosystems, including PyPI, by ingesting reports from OpenSSF’s malicious-packages repository. The update replaces a GitHub-only npm path with a shared importer that validates and normalizes OSV-format malware reports before they reach the advisory database.

  • 5GitHub Blog·7/29/2026open_source
    GitHub recommends grouping Dependabot updates into slower monthly batches

    GitHub Blog describes a way to reduce Dependabot noise by grouping dependency updates and changing the check cadence from daily to monthly. The approach keeps security updates flowing while cutting down on pull request volume, CI runs, and review overhead, especially in active repositories and monorepos.

  • 4GitHub Blog·7/14/2026security
    GitHub adds a three-day cooldown for Dependabot updates

    GitHub has changed Dependabot so it now waits until a new release has been available in its registry for at least three days before opening a version update pull request. The cooldown is now the default behavior and does not require any configuration.

  • 7GitHub Blog·7/1/2026security
    GitHub urges maintainers to enable key security settings

    GitHub Security Lab highlights six free security settings that maintainers can enable in under 30 minutes to improve project security posture. The recommendations include adding a SECURITY.md file, enabling private vulnerability reporting, secret scanning with push protection, Dependabot and dependency review, and code scanning.

  • 4Martin Fowler·5/20/2026research
    Three More Static Code Analysis Sensors

    Martin Fowler highlights a practical follow-up on using static code analysis and related sensors to help coding agents keep codebases maintainable. The article discusses feedback tools such as linting, dependency rules, coupling analysis, modularity review, and mutation testing to catch issues early and support self-correction.

  • 5Martin Fowler·5/19/2026research
    Maintainability sensors for coding agents

    The article describes practical experiments with using automated “sensors” to help AI coding agents keep a codebase maintainable. It highlights tools such as linting, dependency checks, test coverage, mutation testing, and structural analysis to catch maintainability issues early and support self-correction before changes reach humans.