skill-refinement

To identify underperforming skills, analyze ledger traces and open-question metrics across the evaluation period and org memory ledger. This process surfaces specific bottlenecks and structured per-skill performance metrics for review.

Running a skill performance retrospective involves evaluating cost data alongside period and memory ledger inputs. This generates structured refinement proposals and top metrics to guide skill improvement planning.

Yes, you can analyze open-questions and memory ledger data to surface skill improvement opportunities. The evaluation applies these metrics to output structured per-skill bottlenecks and actionable refinement proposals.

Generating refinement proposals for frequently failing skills requires evaluating ledger traces and open-question metrics. The output provides structured per-skill metrics and concrete improvement proposals for the evaluation period.

Evaluating skill performance and proposing improvements requires period, memory ledger, open-questions, and cost data. These metrics identify bottlenecks and generate structured refinement proposals for underperforming skills.

No external dependencies or components are required to perform skill performance evaluation. The process operates directly on ledger traces, open-question metrics, and cost data to output refinement proposals.