context-compression

Context compression for project management works by scoring entries based on impact, risk, and downstream relevance, then generating semantic summaries for high-priority items. This mechanism automatically applies field masks to filter information specifically for downstream agents.

Priority scoring works by automatically evaluating context entries against impact, risk, novelty, blockers, and downstream relevance. This mechanism identifies critical project management details to ensure they are preserved during compression.

Yes, you can use this context compression approach with Python, as it requires Python for scripting and data processing. The scripts component handles the priority scoring and semantic summarization logic for your project management workflows.

The best way to maintain cross-references when compressing context is using the automatic cross-reference mechanism. It identifies and creates references for endpoints, database tables, migrations, components, and decisions to preserve critical project relationships.

Yes, downstream-aware compression applies field masks based on the specific relationship between agents. This ensures that the compressed context only includes the relevant information required by the next downstream project management phase.