What problem does it solve?
When deciding whether to fork an existing harness, rank candidate templates, or detect duplicate work across teams, you need an objective similarity score between two harness fingerprints (genome + score JSON) rather than relying on intuition.
Core Features & Use Cases
- Weighted composite scoring: Combines cosine similarity over 9 numeric dimensions (0.60), categorical agreement over 4 enum fields (0.25), and jaccard over agent_topology (0.15) into an overall score in [0,1].
- Flexible inputs: Compare fingerprints from JSON files or from persisted memory records via keys, with optional per-dimension breakdowns and drift alerts below a configurable threshold.
- Use Case: An agent evaluating whether to scaffold a new harness or fork an existing one runs the similarity function against candidate templates and uses the per-dimension breakdown to feed the ADR-151 Recommender's confidence number.
Quick Start
Compare two harness fingerprint files by running npx ruflo metaharness similarity --a a.json --b b.json to get the overall score and component breakdown.