gh-bradleyboehmke-brads-marketplace-git-workflow/skills/github-issue-analysis.md

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GitHub Issue Analysis	Scoring rubrics and clustering methodology for analyzing GitHub issues	github issues scoring clustering analysis

GitHub Issue Analysis

Metadata

Purpose: Analyze GitHub issues with multi-dimensional scoring and clustering Applies to: Repository investigation and issue backlog analysis Version: 1.0.0

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Instructions

Issue Scoring Dimensions

Score each issue on a scale of 1-5 (low to high) across four dimensions:

1. Risk Score (1-5)

Assess the potential negative impact if the issue is NOT addressed:

• 5 - Critical Risk: Security vulnerability, data loss, production outage, compliance violation
• 4 - High Risk: Major feature broken, significant performance degradation, user-blocking bug
• 3 - Medium Risk: Moderate bug affecting some users, technical debt that will compound
• 2 - Low Risk: Minor bug with workarounds, cosmetic issue, edge case problem
• 1 - Minimal Risk: Nice-to-have improvement, suggestion, question

Detection patterns:

• Keywords: security, vulnerability, crash, data loss, broken, urgent, blocker
• Labels: security, critical, bug, p0, blocker
• Issue state: Open for >90 days with high comment activity

2. Value Score (1-5)

Assess the positive impact if the issue IS addressed:

• 5 - Transformative: Major feature enabling new use cases, significant UX improvement
• 4 - High Value: Important enhancement improving core workflows, performance boost
• 3 - Medium Value: Useful improvement benefiting many users, code quality enhancement
• 2 - Low Value: Minor convenience, small optimization, documentation improvement
• 1 - Minimal Value: Cosmetic change, personal preference, duplicate effort

Detection patterns:

• Keywords: feature, enhancement, improve, optimize, speed up, user request
• Labels: enhancement, feature, performance, ux
• Community engagement: High thumbs-up reactions, many comments requesting feature

3. Ease Score (1-5)

Assess the effort required to implement (inverse scale - 5 is easiest):

• 5 - Trivial: Documentation update, config change, simple one-liner fix
• 4 - Easy: Small bug fix, minor refactor, straightforward enhancement (<1 day)
• 3 - Moderate: Feature addition requiring new code, moderate refactor (1-3 days)
• 2 - Challenging: Complex feature, architectural change, multiple dependencies (1-2 weeks)
• 1 - Very Hard: Major refactor, breaking changes, requires research/prototyping (>2 weeks)

Detection patterns:

• Keywords: easy, good first issue, help wanted, typo, documentation
• Labels: good-first-issue, easy, documentation, beginner-friendly
• Code references: Issues with specific file/line references are often easier

4. DocQuality Score (1-5)

Assess how well the issue is documented:

• 5 - Excellent: Clear description, reproduction steps, expected behavior, context, examples
• 4 - Good: Clear problem statement, some context, actionable
• 3 - Adequate: Basic description, understandable but missing details
• 2 - Poor: Vague description, missing context, requires clarification
• 1 - Very Poor: Unclear, no details, cannot action without extensive follow-up

Detection patterns:

• Length: Longer issues (>500 chars) often have better documentation
• Structure: Issues with headers, code blocks, numbered steps
• Completeness: Includes version info, environment details, error messages

Issue Clustering and Themes

Group related issues by detecting common themes:

Theme Detection Approach

1. Keyword extraction: Extract significant terms from title and body
2. Label analysis: Group by common labels
3. Component detection: Identify affected components/modules
4. Similarity scoring: Find issues with overlapping keywords

Common Theme Categories

• Bug Clusters: Similar error messages, same component, related failures
• Feature Requests: Related enhancements, common user needs
• Performance Issues: Speed, memory, scalability concerns
• Documentation: Missing docs, unclear guides, examples needed
• Dependencies: Third-party library issues, version conflicts
• DevOps/Infrastructure: CI/CD, deployment, build issues
• Security: Vulnerabilities, auth, permissions

Clustering Algorithm

For each issue:
  1. Extract keywords from title and body (remove stopwords)
  2. Extract labels
  3. Count keyword overlaps with other issues
  4. If overlap > threshold (e.g., 3+ keywords), mark as related
  5. Group related issues into clusters
  6. Name cluster by most common keywords

Staleness Detection

Definition: Issues with no activity for ≥N days (default: 60)

Activity indicators:

• New comments
• Label changes
• Status updates
• Commits referencing the issue

Staleness categories:

• Fresh: Activity within last 30 days
• Aging: 30-60 days since last activity
• Stale: 60-90 days since last activity
• Very Stale: 90+ days since last activity

Action recommendations:

• Very Stale: Consider closing with explanation or ping assignees
• Stale: Review priority, add to backlog grooming
• Aging: Monitor for staleness
• Fresh: Active work, no action needed

Issue Bucketing

Categorize issues into actionable buckets based on scores:

1. High Value: Value ≥ 4, Risk ≥ 3 → Priority work
2. High Risk: Risk ≥ 4 → Address urgently regardless of value
3. Newcomer Friendly: Ease ≥ 4, DocQuality ≥ 3 → Good first issues
4. Automation Candidate: Ease ≥ 4 AND at least 2 similar issues detected by clustering → Consider scripting/automation
5. Needs Context: DocQuality ≤ 2 → Request more information
6. Stale: No activity ≥ stale_days → Consider closing/archiving

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Resources

Scoring Examples

Example 1: Security Vulnerability

Title: "SQL injection in login endpoint"
Risk: 5 (critical security issue)
Value: 5 (must fix)
Ease: 3 (moderate fix, requires testing)
DocQuality: 5 (clear reproduction steps, example payload)
Bucket: High Risk

Example 2: Documentation Typo

Title: "Fix typo in README.md - 'installtion' -> 'installation'"
Risk: 1 (minimal impact)
Value: 1 (minor improvement)
Ease: 5 (trivial one-character change)
DocQuality: 5 (exact location specified)
Bucket: Newcomer Friendly

Example 3: Feature Request

Title: "Add dark mode support"
Risk: 1 (no negative impact if not done)
Value: 4 (highly requested UX improvement)
Ease: 2 (significant UI/CSS work)
DocQuality: 3 (clear request but missing design specs)
Bucket: High Value

Example 4: Vague Bug Report

Title: "App doesn't work"
Risk: ? (unclear)
Value: ? (unclear)
Ease: ? (cannot assess)
DocQuality: 1 (no details whatsoever)
Bucket: Needs Context

CLI Integration

The analysis uses GitHub CLI (gh) for read-only access:

# Fetch all open issues
gh issue list --state open --limit 1000 --json number,title,body,labels,createdAt,updatedAt,comments

# Fetch issues with specific labels
gh issue list --label bug --state open --json number,title,body,labels,createdAt,updatedAt

# Fetch issues since specific date
gh issue list --state all --search "updated:>2024-01-01"

Output Format

Console Output: Structured dashboard with tables and buckets Markdown Output: Detailed report with scores, themes, and recommendations

Example markdown structure:

# GitHub Issue Analysis

## Summary
- Total issues analyzed: 47
- Stale issues: 12
- High risk issues: 3
- High value issues: 8

## Issue Buckets

### High Risk (3 issues)
- #42: SQL injection vulnerability [Risk:5, Value:5, Ease:3]
- #38: Data corruption in export [Risk:5, Value:4, Ease:2]

### High Value (8 issues)
...

## Themes
1. **Performance** (5 issues): #12, #23, #34, #45, #56
2. **Documentation** (8 issues): ...

## Stale Issues
- #15: Last activity 87 days ago
- #22: Last activity 92 days ago