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GitHub uses code context to help spot exposed secrets

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GitHub uses code context to help spot exposed secrets

A context-aware classifier helps distinguish real credentials from examples and other lookalikes. It does not guarantee that every secret will be found.

GitHub has explained a secret-detection model that reads a possible credential alongside its file path, variable names and nearby code. The goal is to distinguish real passwords and keys from harmless examples or similar-looking strings, so developers receive fewer distracting alerts while still finding exposed credentials.

The model complements pattern-based scanning rather than replacing it. It does not mean every secret can be found. A practical check is to look for credentials accidentally written into your repository. If a real key has been exposed, revoke or replace it promptly; deleting the visible line alone is not a substitute.

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This directory lists AI development plugins with public source code and a clear purpose. Browse by task, then open an introduction to learn what a project does, who it is for, and what you need to get started. It covers selected projects, not every AI plugin.

How do the three plugin types differ?

Tool connections (MCP) let AI assistants use external tools such as browsers, code repositories and databases. MCP is the protocol used for these connections.

Reusable skills give a compatible AI assistant instructions and guidance for a particular task.

Editor plugins run inside the software you use to write code, adding AI chat, completions or suggested edits.

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Projects are sorted by their total GitHub stars. Stars indicate interest; they are not downloads, active users or quality scores. A skill pack counts as one project. Daily star growth is not shown without historical records.

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It uses the most recent code push, which may not be a new release. The 7-day filter checks that project update time; ranking still uses all-time stars. Each introduction shows when its star count was collected. Failed updates keep the last saved data.

Introductions are based on public project documentation. These projects have not each been installed and tested here. Check the project documentation for setup, compatibility and service costs. Refreshing this list only reads saved data; it does not start collection.

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