estimate-assistant

name	estimate-assistant
description	Provide data-driven task estimates using git history, code complexity analysis, and similar past work

Estimate Assistant

Provide data-driven task estimation based on historical git data, code complexity, and similar past work.

Instructions

Analyze the task described in $ARGUMENTS and produce a data-backed estimate.

1. Gather Git History

Collect relevant historical data from the repository:

# Get commit history with timestamps and authors (last 6 months)
git log --pretty=format:"%h|%an|%ad|%s" --date=iso --since="6 months ago"

# Analyze PR completion times (if gh CLI is available)
gh pr list --state closed --limit 50 --json number,title,createdAt,closedAt,additions,deletions,files

# Get file change frequency to identify hot spots
git log --pretty=format: --name-only --since="6 months ago" | sort | uniq -c | sort -rn | head -20

# Analyze commit patterns by author
git shortlog -sn --since="6 months ago"

2. Analyze Code Complexity

For the files and modules relevant to the task:

• Count lines of code in the affected area
• Estimate cyclomatic complexity (number of branches, loops, conditions)
• Count dependencies and imports
• Identify how many files will likely need changes
• Check test coverage in the affected area

3. Find Similar Past Work

Search the git history for similar completed tasks:

• Look for PRs or commits with related keywords (e.g., if the task is "add OAuth", search for past auth-related PRs)
• Note the time span from first commit to merge for similar work
• Note the number of files changed and lines modified
• Record the actual effort those similar tasks required

4. Assess Complexity Factors

Evaluate factors that increase or decrease effort:

Factor	Impact	Notes
New code vs refactoring	Refactoring typically 1.3x	Existing code has hidden dependencies
External API integration	+20-40%	API docs, auth, error handling
Database changes	+15-30%	Migrations, backwards compatibility
UI work	+20-30%	Cross-browser, responsive, accessibility
Security-sensitive	+20-40%	Review, testing, edge cases
Existing test coverage	-10-20%	Safety net for changes
Well-documented area	-10-15%	Faster ramp-up

5. Calculate the Estimate

Combine the data:

1. Start with the median duration of similar past tasks
2. Adjust for complexity factors identified in step 4
3. Apply a confidence level based on how much historical data was available
4. Produce optimistic / realistic / pessimistic range

6. Produce the Estimate Report

Present findings in this format:

## Task Estimation Report

**Task:** [description]
**Date:** [date]

### Estimate: [X] story points (or [X] hours/days)
**Confidence:** [Low/Medium/High] ([percentage]%)
**Range:** [optimistic] - [pessimistic]

### Similar Completed Tasks
1. "[PR/commit title]" - [duration], [files changed], [lines changed]
2. "[PR/commit title]" - [duration], [files changed], [lines changed]
3. "[PR/commit title]" - [duration], [files changed], [lines changed]

### Complexity Factors
- **[Factor]** ([+/- impact]): [explanation]
- **[Factor]** ([+/- impact]): [explanation]

### Affected Areas
- [file/module]: [what changes are needed]
- [file/module]: [what changes are needed]

### Risk Factors
- [risk]: [mitigation]
- [risk]: [mitigation]

### Recommendations
1. [actionable suggestion]
2. [actionable suggestion]

### Breakdown (if applicable)
1. [subtask] - [estimate]
2. [subtask] - [estimate]
3. [subtask] - [estimate]

Tips

• If fewer than 5 similar tasks are found, note that confidence is lower
• Always present a range, not a single number
• Track actual vs estimated for continuous improvement
• Consider external factors (holidays, team changes, on-call rotations)
• For large tasks, break down into subtasks and estimate each

See references/estimation-models.md for detailed estimation model examples and pattern recognition approaches.