knowledge-resolution

Knowledge gaps are handled through a multi-level fallback chain that prioritizes exact skill matches, then related skills, cross-agent borrowing, and finally model knowledge with confidence signals.

Manage fallback responses by leveraging a systematic resolution chain that checks related skills and cross-agent expertise before falling back to general knowledge, ensuring unaddressed gaps are logged to persistent memory.

Confidence signaling provides clear indicators of certainty levels when an agent responds from general knowledge after exhausting all specific skill matches and cross-agent borrowing options.

Cross-agent skill borrowing allows you to resolve niche programming concepts by checking if another agent has relevant expertise before resorting to general model knowledge or admitting uncertainty.

Logging unaddressed knowledge gaps to persistent memory tracks unresolved queries systematically, supporting future skill creation and building a growing skill marketplace by identifying missing capabilities.

When all fallback options fail, the mechanism admits uncertainty directly while still logging the knowledge gap to persistent memory, ensuring transparency and enabling future skill development for that specific deficiency.