SkillCost Meter

AI skills runtime forecast

AI Skills Runtime Forecast for Token, Time, and Retry Load

An AI skills runtime forecast predicts the behavior of a skill across realistic tasks. It estimates prompt tokens, context tokens, tool calls, latency, failure retries, and review burden. This is different from a static checklist because it models how the skill behaves when tasks vary.

Open the forecaster

When this matters

  • A skill works well on a demo but fails when the input is incomplete.
  • A developer needs to set usage limits for a paid Team plan.
  • A marketplace wants to compare skills across different agent runtimes.

How to run the workflow

  1. Create representative tasks for normal, edge, ambiguous, and failure-prone cases.
  2. Estimate context and tool load for each task class.
  3. Model retries when criteria are not met or external tools fail.
  4. Calculate average tokens, p90 tokens, elapsed time, and failure rate.
  5. Publish a clear forecast with plan limits and buyer-facing assumptions.

Common risks

  • Averages hide costly p90 runs.
  • Tool timeouts can create retries even when model prompts are strong.
  • Runtime assumptions should be updated when the skill or model changes.

Where SkillCost Meter fits

SkillCost Meter generates a standard task set and models 20 typical runs so teams can price and govern skills with evidence.