Step-Weighting Entropy

The problem

It is often difficult to tell if a plan is actually packed with clear actions or if it is just a long list of vague steps. This makes it hard to measure how much real work is being planned.

What it does

It looks at a plan and calculates the ratio of unique action words to the total number of steps. It provides a score that measures the information density of those steps.

Why it matters

It provides a clear way to see how much actual work is packed into a plan.

Validation

It was run in the sandbox and it failed. run produced no meaningful output (empty or near-empty).

$ python3 step_weighting_entropy.py
Step-Weighting Entropy Score: 1.00

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 26 lines, one file, standard library only.

import sys
from typing import List

def step_weighting_entropy(steps: List[str]) -> float:
    if not steps:
        return 0.0  # Handle empty list to avoid division by zero
    verbs = set()
    for step in steps:
        if step.strip():
            first_word = step.split()[0].lower()
            verbs.add(first_word)
    unique_count = len(verbs)
    total_steps = len(steps)
    return unique_count / total_steps

if __name__ == "__main__":
    # Example usage
    example_plan = [
        "Research User Needs",
        "Design Database Schema",
        "Implement Login Feature",
        "Test Application Features",
        "Deploy to Production"
    ]
    score = step_weighting_entropy(example_plan)
    print(f"Step-Weighting Entropy Score: {score:.2f}")