project time estimate calibration advisor

The problem

Developers and project leads consistently underestimate duration because they focus on the "inside view" and ignore historical performance. This planning fallacy leads to missed deadlines, broken commitments, and poor resource allocation.

What it does

• Preserves the original project estimate while applying a debiasing layer.
• Calculates actual-to-estimate ratios from historical reference classes to identify systematic bias.
• Generates probabilistic completion forecasts, including P50 and P80 percentiles.
• Distinguishes between pure effort and elapsed calendar time to account for dependencies and context switching.
• Quantifies commitment risk by testing current deadlines against historical overrun probabilities.

Why this beats prompting it yourself

General LLMs tend to agree with your optimistic estimates or provide vague buffers. This skill enforces a mathematical framework that requires historical evidence, preventing the AI from hallucinating false precision when data is sparse.

Use cases

• Calibrating a sprint task duration based on the last five similar features.
• Determining a safe commitment date for a client migration project.
• Converting person-hours of effort into a realistic calendar release window.
• Building a historical reference class to track and improve team estimation accuracy.

Known limitations

Requires at least five comparable historical data points for stable percentiles. Without history, it will refuse to provide precise confidence intervals to avoid false precision.