Effort Estimation

To predict task effort and token cost, you can use historical episode_memory data and quality_scorer baselines. This provides per-task and aggregate estimates including thinking mode, model tier, and confidence intervals for budget feasibility.

The best way to estimate token usage for codebase tasks is by incorporating structural analysis of codebase characteristics like lines of code and complexity. This adjusts effort and cost predictions to match specific build and architecture requirements.

Yes, you can check budget feasibility by running an effort estimation against your current task plan. It returns aggregate cost projections and confidence intervals by leveraging historical episode_memory data to guide replanning and resource allocation.

You forecast resource allocation needs by applying effort estimation during task decomposition. It analyzes historical baselines and structural codebase metrics to return per-task duration, token usage, and cost estimates for driving planning decisions.

Effort estimation relies heavily on historical episode_memory data and quality_scorer baselines to generate reliable predictions. Without this historical context, the accuracy of per-task effort, token usage, and duration forecasts may be significantly reduced.

You need to estimate model tier and thinking mode during task planning and budget checks to ensure cost feasibility. This predicts per-task token usage and aggregate costs, enabling accurate capacity management and budget validation for pipelines.