estimation

name	estimation
description	Estimation techniques including analogous, parametric, three-point, and expert judgment methods. Provides effort, cost, duration, and complexity estimates for projects, features, and tasks.
allowed-tools	Read, Glob, Grep, Task, Skill

Estimation

When to Use This Skill

Use this skill when:

• Estimation tasks - Working on estimation techniques including analogous, parametric, three-point, and expert judgment methods. provides effort, cost, duration, and complexity estimates for projects, features, and tasks
• Planning or design - Need guidance on Estimation approaches
• Best practices - Want to follow established patterns and standards

Overview

Systematically estimate effort, cost, duration, and complexity using proven estimation techniques. Supports analogous, parametric, three-point (PERT), and expert judgment methods for projects, features, and tasks.

What is Estimation?

Estimation is the process of forecasting the resources, time, and cost required to complete work. Good estimation balances:

• Accuracy: How close to actual results
• Precision: Consistency of estimates
• Speed: Time to produce estimates
• Communication: Shared understanding of uncertainty

Estimation vs Commitment

Concept	Definition	Use
Estimate	Best guess given current knowledge	Planning, forecasting
Commitment	Promise to deliver by date/cost	Contracts, deadlines
Target	Desired outcome to aim for	Goals, objectives

Key principle: Estimates are ranges with uncertainty, not single-point guarantees.

Estimation Techniques

Analogous Estimation (Top-Down)

Estimate based on similar past work:

Step	Action
1	Identify similar completed project/feature
2	Retrieve actual effort/cost/duration
3	Adjust for differences (complexity, team, technology)
4	Apply adjustment factor

Formula:

New Estimate = Historical Actual × Adjustment Factor

Example:
Similar project took 200 hours
New project is ~20% more complex
Estimate = 200 × 1.20 = 240 hours

When to Use: Early phases, limited detail, experienced teams with historical data

Accuracy: +/- 25-50% (improves with good historical data)

Parametric Estimation

Estimate using statistical relationships:

Element	Description
Unit of Work	Measurable element (screen, API, table)
Productivity Rate	Effort per unit from historical data
Quantity	Number of units to produce

Formula:

Estimate = Quantity × Productivity Rate

Example:
10 API endpoints × 16 hours/endpoint = 160 hours

Common Productivity Metrics:

Work Type	Metric	Typical Range
UI screens	Hours/screen	8-40 hours
API endpoints	Hours/endpoint	4-24 hours
Database tables	Hours/table	4-16 hours
Test cases	Hours/test	0.5-4 hours
Documentation pages	Hours/page	2-8 hours

When to Use: Repeatable work, good historical data, similar technology

Accuracy: +/- 15-25% (with calibrated rates)

Three-Point Estimation (PERT)

Estimate using optimistic, most likely, and pessimistic values:

Value	Symbol	Definition
Optimistic	O	Best case, everything goes right
Most Likely	M	Most probable outcome
Pessimistic	P	Worst case, problems occur

PERT Formula (Weighted Average):

Expected = (O + 4M + P) / 6
Standard Deviation = (P - O) / 6

Example:
O = 5 days, M = 8 days, P = 17 days
Expected = (5 + 4×8 + 17) / 6 = 9 days
Std Dev = (17 - 5) / 6 = 2 days

Confidence Intervals:

Confidence	Calculation	Example (E=9, SD=2)
68%	E ± 1 SD	7-11 days
95%	E ± 2 SD	5-13 days
99.7%	E ± 3 SD	3-15 days

When to Use: Uncertain work, new technology, need to communicate risk

Accuracy: Provides explicit uncertainty range

Expert Judgment

Estimate using collective expert knowledge:

Wideband Delphi Process:

Round	Activity
1	Experts estimate independently
2	Collect and share anonymous estimates
3	Discuss high/low outliers, share rationale
4	Re-estimate independently
5	Repeat until convergence (or average)

Planning Poker (Agile):

Step	Action
1	Present item to estimate
2	Discuss briefly (2-5 minutes)
3	Each team member selects card privately
4	Reveal simultaneously
5	Discuss outliers
6	Re-vote until consensus

Common Scales:

Scale Type	Values
Fibonacci	1, 2, 3, 5, 8, 13, 21, 34, 55, 89
Modified Fibonacci	0, 0.5, 1, 2, 3, 5, 8, 13, 20, 40, 100
T-shirt	XS, S, M, L, XL, XXL
Powers of 2	1, 2, 4, 8, 16, 32, 64

When to Use: Complex work, multiple perspectives needed, team alignment

Function Point Analysis

Estimate based on functional size (for larger systems):

Component	Description	Weight Range
External Inputs	Data entering system	3-6
External Outputs	Data leaving system	4-7
External Inquiries	Read-only queries	3-6
Internal Files	Logical data stores	7-15
External Interfaces	Shared data	5-10

Process:

1. Count each component type
2. Classify complexity (low/medium/high)
3. Apply weights
4. Calculate unadjusted function points
5. Apply technical complexity factor

When to Use: Large projects, formal contracts, industry benchmarking

Relative Estimation

Story Points

Relative complexity/effort measure:

Points	Relative Size	Example
1	Trivial	Fix typo, config change
2	Simple	Simple bug fix, minor feature
3	Moderate	Standard feature, moderate complexity
5	Complex	Multi-component feature
8	Very complex	Integration work, significant unknowns
13	Epic-sized	Consider breaking down
21+	Too large	Must decompose

Baseline: Pick a well-understood story as reference (e.g., "this is a 3")

Velocity: Story points completed per iteration (used for forecasting)

T-Shirt Sizing

Quick relative sizing for roadmap planning:

Size	Effort Range	Duration Range
XS	1-4 hours	< 1 day
S	0.5-2 days	1-2 days
M	2-5 days	3-5 days
L	1-2 weeks	1-2 weeks
XL	2-4 weeks	2-4 weeks
XXL	1-3 months	Too big, decompose

Workflow

Phase 1: Prepare

Step 1: Clarify Scope

## Estimation Request

**Item:** [What's being estimated]
**Requester:** [Who needs the estimate]
**Purpose:** [Planning/budgeting/commitment]
**Deadline:** [When estimate is needed]
**Precision:** [ROM/Budget/Definitive]

### Scope Definition

- **In Scope:** [What's included]
- **Out of Scope:** [What's excluded]
- **Assumptions:** [Key assumptions]
- **Constraints:** [Known constraints]

Step 2: Select Estimation Technique

Situation	Recommended Technique
Early project phase	Analogous + T-shirt sizing
Detailed requirements	Parametric + Three-point
Agile backlog	Story points + Planning poker
New technology/domain	Expert judgment + Three-point
Contract/budget	Function points + Parametric

Phase 2: Estimate

Step 1: Decompose Work

Break down into estimable units (half-day to 2-week chunks)

Step 2: Apply Technique

Use selected technique(s) from above

Step 3: Add Contingency

## Contingency Calculation

| Risk Level | Contingency | When to Use |
|------------|-------------|-------------|
| Low | 10-15% | Well-understood, experienced team |
| Medium | 20-30% | Some unknowns, new team members |
| High | 40-50% | Significant unknowns, new technology |
| Very High | 75-100% | Research, innovation, first-of-kind |

Step 4: Validate

• Sanity check against similar work
• Review with team
• Check for missing items
• Verify assumptions

Phase 3: Communicate

Step 1: Express as Range

## Estimate Summary

**Effort:** 160-200 hours (confidence: 80%)
**Duration:** 4-5 weeks (with 2 developers)
**Cost:** $24,000-$30,000

**Key Risks:**
- API integration complexity unknown
- Dependency on third-party availability

Step 2: Document Assumptions

## Estimation Assumptions

1. Requirements are stable and complete
2. Team has 80% availability (20% overhead)
3. No major technology changes
4. Dependencies delivered on time
5. [Additional assumptions...]

**If assumptions change, estimate should be revisited.**

Output Formats

Narrative Summary

## Estimation Summary

**Item:** [Feature/Project name]
**Date:** [ISO date]
**Estimator:** estimation-analyst
**Technique:** [Technique used]

### Estimate

| Dimension | Low | Expected | High | Confidence |
|-----------|-----|----------|------|------------|
| Effort | 120h | 160h | 220h | 80% |
| Duration | 4w | 5w | 7w | 80% |
| Cost | $18K | $24K | $33K | 80% |

### Basis of Estimate

- **Historical Reference:** [Similar past work]
- **Productivity Rate:** [If parametric]
- **Expert Input:** [Who contributed]

### Assumptions

1. [Assumption 1]
2. [Assumption 2]

### Risks Affecting Estimate

| Risk | Impact on Estimate |
|------|-------------------|
| [Risk 1] | +20% if occurs |
| [Risk 2] | +15% if occurs |

### Recommendations

1. [Next steps for refining estimate]
2. [When to re-estimate]

Structured Data (YAML)

estimation:
  version: "1.0"
  date: "2025-01-15"
  item: "User Dashboard Feature"
  estimator: "estimation-analyst"
  technique: "three_point"

  scope:
    description: "Interactive user dashboard with analytics"
    in_scope:
      - "Dashboard UI components"
      - "Data visualization"
      - "User preferences"
    out_of_scope:
      - "Backend analytics engine"
      - "Real-time updates"
    assumptions:
      - "API endpoints available"
      - "Design mockups complete"

  estimates:
    effort:
      optimistic: 120
      most_likely: 160
      pessimistic: 240
      expected: 166
      unit: "hours"
      std_deviation: 20
    duration:
      optimistic: 3
      most_likely: 4
      pessimistic: 6
      expected: 4.2
      unit: "weeks"
    cost:
      expected: 24000
      range_low: 18000
      range_high: 36000
      currency: "USD"

  confidence: 0.80
  contingency: 0.20

  breakdown:
    - component: "UI Components"
      effort: 60
      technique: "parametric"
      rate: "15h/component"
      quantity: 4
    - component: "Data Integration"
      effort: 40
      technique: "analogous"
      reference: "PROJ-123"
    - component: "Testing"
      effort: 40
      technique: "percentage"
      percentage: 0.25

  risks:
    - risk: "API complexity higher than expected"
      probability: 0.3
      impact_hours: 30
    - risk: "Design changes during development"
      probability: 0.2
      impact_hours: 20

  historical_comparison:
    similar_item: "Admin Dashboard"
    actual_effort: 180
    adjustment_factor: 0.9

Breakdown Table

## Effort Breakdown

| Component | Technique | Estimate | Contingency | Total |
|-----------|-----------|----------|-------------|-------|
| UI Components | Parametric | 60h | 12h | 72h |
| Data Integration | Analogous | 40h | 10h | 50h |
| Testing | % of Dev | 40h | 8h | 48h |
| Documentation | Parametric | 16h | 4h | 20h |
| **Subtotal** | | **156h** | **34h** | **190h** |
| Management Overhead | 10% | | | 19h |
| **Total** | | | | **209h** |

Estimation Accuracy

Cone of Uncertainty

Estimate accuracy improves as project progresses:

Phase	Accuracy Range
Initial concept	0.25x - 4x
Approved project	0.5x - 2x
Requirements complete	0.67x - 1.5x
Detailed design	0.8x - 1.25x
Code complete	0.9x - 1.1x

Implication: Early estimates need wider ranges; refine as knowledge grows.

Estimation Levels

Level	Accuracy	When Used
ROM (Rough Order of Magnitude)	-25% to +75%	Initial budgeting
Budget	-10% to +25%	Project approval
Definitive	-5% to +10%	Execution baseline

Common Pitfalls

Pitfall	Prevention
Single-point estimates	Always provide ranges
Optimism bias	Use historical data, add contingency
Anchoring	Estimate before seeing others' estimates
Planning fallacy	Include realistic overhead and risks
Scope creep	Document assumptions, re-estimate on changes
Precision theater	Match precision to actual knowledge

Integration

Upstream

• Requirements - What to estimate
• risk-analysis - Risks affecting estimates
• stakeholder-analysis - Who needs estimates, precision required

Downstream

• Project planning - Resource allocation, scheduling
• Budgeting - Cost forecasting
• prioritization - Cost input for value/effort analysis

Related Skills

• risk-analysis - Risks affecting estimates
• prioritization - Using estimates for prioritization
• decision-analysis - Trade-off decisions
• value-stream-mapping - Estimating process improvement effort

Version History

• v1.0.0 (2025-12-26): Initial release