skill-lifecycle-manager

To check missing Python dependencies for a skills library, you can parse and verify installed packages to report missing or conflicting modules. This dependency analysis process also generates a dependency graph to help maintainers identify and install required packages quickly.

Bumping semantic versions for plugins automatically involves listing installed skills and applying major, minor, or patch version increments. This lifecycle management process updates version metadata and can generate a changelog to track the specific updates applied.

Yes, you can export skill health reports to Excel using openpyxl. The reporting process records execution metrics in SQLite, surfaces low-success skills, and optionally converts the generated health report into an Excel spreadsheet for stakeholder review.

A Git-backed snapshot for skill rollback is a recovery mechanism that creates a versioned checkpoint of your skills library. This snapshot process uses Git commits to track changes, allowing you to safely rollback to prior states if modifications break functionality.

SQLite usage tracking for skill execution metrics works by recording runtime statistics locally in a database. This tracking process logs execution data into SQLite, allowing the system to surface low-success skills and generate overall health reports for analysis.

The best way to audit MCP dependencies in a plugin directory is to run a centralized dependency analysis. This audit process parses configured MCP dependencies, checks for missing packages, and generates a dependency graph to report conflicts or gaps.