Project Estimation

This one walks you through building project estimates using three different techniques: three-point PERT estimation with confidence intervals, bottom-up task decomposition, and analogous estimation based on historical data. The code examples are practical, showing you how to calculate weighted estimates, standard deviations, and resource allocations in Python. It's most useful when you need to defend timeline and budget numbers to stakeholders or when you're working on something unfamiliar and can't just wing it. The best practices section is honest about common pitfalls like estimating under pressure or forgetting to account for non-coding work. Good reminder to track actual versus estimated metrics so your next estimate doesn't suck as much.

skills.sh installs: 670
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill project-estimation --agent claude-code

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Overview

Accurate project estimation determines realistic timelines, budgets, and resource allocation. Effective estimation combines historical data, expert judgment, and structured techniques to minimize surprises.

When to Use

Defining project scope and deliverables
Creating project budgets and timelines
Allocating team resources
Managing stakeholder expectations
Assessing project feasibility
Planning for contingencies
Updating estimates during project execution

Quick Start

Minimal working example:

# Three-point estimation technique for uncertainty

class ThreePointEstimation:
    @staticmethod
    def calculate_pert_estimate(optimistic, most_likely, pessimistic):
        """
        PERT formula: (O + 4M + P) / 6
        Weighted toward most likely estimate
        """
        pert = (optimistic + 4 * most_likely + pessimistic) / 6
        return round(pert, 2)

SKILL.md on GitHub