
Megan developed a cost-aware model usage framework for the UKGovernmentBEIS/inspect_ai repository, focusing on unifying cost tracking and enforcing explicit cost governance in dataset resolution. Using Python and backend development skills, she integrated model cost data into usage accounting and made cost limits a required parameter, ensuring transparent and controlled resource usage. Her work included refactoring cost computation and data flow for improved robustness and user clarity, as well as removing fragile components to enhance maintainability. Megan also updated documentation and test fixtures, laying the groundwork for scalable pricing and better cost visibility, demonstrating depth in API development and integration.
February 2026 monthly summary for UKGovernmentBEIS/inspect_ai focused on delivering a cost-aware model usage framework and hardening the cost governance surface. The work unified cost tracking into usage accounting, enforced explicit cost limits in dataset resolution, and simplified pricing handling. It also included targeted refactors to improve robustness, user clarity, and maintainability, and prepared the ground for scalable pricing and better cost visibility.
February 2026 monthly summary for UKGovernmentBEIS/inspect_ai focused on delivering a cost-aware model usage framework and hardening the cost governance surface. The work unified cost tracking into usage accounting, enforced explicit cost limits in dataset resolution, and simplified pricing handling. It also included targeted refactors to improve robustness, user clarity, and maintainability, and prepared the ground for scalable pricing and better cost visibility.

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