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/project-estimation

Estimate project scope, timeline, and resource requirements using multiple estimation techniques including bottom-up, top-down, and analogous estimation methods for accurate project planning.

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

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/project-estimation

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Estimate project scope, timeline, and resource requirements using multiple estimation techniques including bottom-up, top-down, and analogous estimation methods for accurate project planning.

SKILL.md

project-estimation.SKILL.md
name: project-estimation
description: >
  Estimate project scope, timeline, and resource requirements using multiple
  estimation techniques including bottom-up, top-down, and analogous estimation
  methods for accurate project planning.

Project Estimation

Table of Contents

  • [Overview](#overview)
  • [When to Use](#when-to-use)
  • [Quick Start](#quick-start)
  • [Reference Guides](#reference-guides)
  • [Best Practices](#best-practices)

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)

    @staticmethod
    def calculate_standard_deviation(optimistic, pessimistic):
        """Standard deviation for risk analysis"""
        sigma = (pessimistic - optimistic) / 6
        return round(sigma, 2)

    @staticmethod
    def calculate_confidence_interval(pert_estimate, std_dev, confidence=0.95):
        """
        Calculate confidence interval for estimate
        95% confidence ≈ ±2 sigma
        """
        z_score = 1.96 if confidence == 0.95 else 2.576
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the `references/` directory:

| Guide | Contents | |---|---| | [Three-Point Estimation (PERT)](references/three-point-estimation-pert.md) | Three-Point Estimation (PERT) | | [Bottom-Up Estimation](references/bottom-up-estimation.md) | Bottom-Up Estimation | | [Analogous Estimation](references/analogous-estimation.md) | Analogous Estimation | | [Resource Estimation](references/resource-estimation.md) | Resource Estimation | | [Estimation Templates](references/estimation-templates.md) | Estimation Templates |

Best Practices

✅ DO

  • Use multiple estimation techniques and compare results
  • Include contingency buffers (15-25% for new projects)
  • Base estimates on historical data from similar projects
  • Break down large efforts into smaller components
  • Get input from team members doing the actual work
  • Document assumptions and exclusions clearly
  • Review and adjust estimates regularly
  • Track actual vs. estimated metrics for improvement
  • Include non-development tasks (planning, testing, deployment)
  • Account for learning curve on unfamiliar technologies

❌ DON'T

  • Estimate without clear scope definition
  • Use unrealistic best-case scenarios
  • Ignore historical project data
  • Estimate under pressure to hit arbitrary targets
  • Forget to include non-coding activities
  • Use estimates as performance metrics for individuals
  • Change estimates mid-project without clear reason
  • Estimate without team input
  • Ignore risks and contingencies
  • Use one technique exclusively
Read more
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