share-usage

Anonymized usage statistics are generated by parsing local Claude Code logs to aggregate skill call counts and research domain tags while stripping all personal identifiers. This ensures strict data privacy before generating the report.

Yes, you can share usage data without compromising privacy by parsing local logs and stripping all personal or project-specific identifiers. The output is a standardized report of aggregated skill call counts safe for community sharing.

You can contribute by generating a standardized usage report and sharing it via GitHub Discussions. This feedback loop helps maintainers see which research tools are most effective and prioritize future skill development accordingly.

GitHub CLI integration is optional for reporting local skill usage statistics. The core analysis relies on accessing local history files, but the CLI enables automated reporting to GitHub Discussions for community feedback loops.

Anonymized skill usage reporting parses local logs to count how often specific skill repositories are invoked, attaching research domain tags while removing personal identifiers. It provides visibility into which research tools are most effective.

Usage reporting works with neuroimaging workflows by analyzing local logs to count how often fMRI analysis tools are invoked. It aggregates these skill call counts with research domain tags to help guide better neuroimaging development.