Issue Classification

Guide for configuring automatic classification of issues into problem classes.

When to Use

• Setting up ADW issue routing

• Improving classification accuracy

• Handling edge cases in classification

• Designing custom problem classes

Why Classification Matters

Classification routes issues to the correct template:

Issue: "Login button not working" ↓ Classification: /bug ↓ Template: Bug fix plan with root cause analysis

Without classification, agents don't know which workflow to use.

Standard Problem Classes

Chore

Maintenance tasks, updates, cleanup:

Examples:

• "Update dependencies to latest"
• "Clean up unused imports"
• "Rename function to follow convention"
• "Add missing documentation"

Signals:

• "update", "upgrade", "clean", "remove", "rename"
• "documentation", "comment", "format"
• No user-facing change
• Maintenance in nature

Bug

Defects, errors, unexpected behavior:

Examples:

• "Login form submits twice"
• "404 error on profile page"
• "Data not saving correctly"
• "Crash when clicking button"

Signals:

• "error", "bug", "fix", "broken", "crash"
• "not working", "fails", "incorrect"
• Something that worked before now doesn't
• Unexpected behavior

Feature

New functionality, enhancements:

Examples:

• "Add dark mode toggle"
• "Implement user authentication"
• "Create new dashboard page"
• "Add export to CSV"

Signals:

• "add", "create", "implement", "new"
• "enhance", "improve", "extend"
• User-facing new capability
• Didn't exist before

Classification Command

Basic Structure

Issue Classification

Analyze the issue and respond with exactly one of:

• /chore - maintenance, updates, cleanup
• /bug - defects, errors, unexpected behavior
• /feature - new functionality, enhancements

If the issue doesn't fit any category, respond with 0.

Issue

$ARGUMENTS

Enhanced with Examples

Issue Classification

Classify the GitHub issue into one of these categories.

Categories

/chore

• Maintenance and cleanup tasks
• Dependency updates
• Refactoring without behavior change
• Documentation improvements

Examples: "update deps", "clean up code", "rename variables"

/bug

• Something broken that should work
• Errors, crashes, incorrect behavior
• Regressions from previous functionality

Examples: "button doesn't work", "error on page", "crash when..."

/feature

• New functionality
• Enhancements to existing features
• User-facing improvements

Examples: "add dark mode", "create API endpoint", "implement..."

Rules

1. Respond with exactly one: /chore, /bug, /feature, or 0
2. If unclear, prefer /chore (safest default)
3. If multiple types, choose the primary purpose

Issue

$ARGUMENTS

Classification Accuracy

Testing Classification

Test chores

claude -p "/classify-issue 'Update all dependencies'"

Expected: /chore

claude -p "/classify-issue 'Clean up unused imports'"

Expected: /chore

Test bugs

claude -p "/classify-issue 'Login form submits twice on enter'"

Expected: /bug

claude -p "/classify-issue '404 error on profile page'"

Expected: /bug

Test features

claude -p "/classify-issue 'Add dark mode toggle'"

Expected: /feature

claude -p "/classify-issue 'Implement OAuth with Google'"

Expected: /feature

Handling Edge Cases

Multi-type issues:

"Fix the broken login AND add remember me feature"

Approach: Classify by primary purpose If equal: Request issue be split

Unclear issues:

"Improve the performance"

Approach: Default to /chore Rationale: Safest, lowest risk

Not classifiable:

"Question about the API"

Approach: Return 0 Action: Human triage required

Custom Problem Classes

For specialized workflows, extend the base classes:

Example: Refactor Class

/refactor

• Code restructuring
• Architecture changes
• Performance optimization
• No functional change

Examples: "refactor auth module", "optimize queries", "restructure..."

Example: Security Class

/security

• Vulnerability fixes
• Security improvements
• Access control changes
• Compliance requirements

Examples: "fix XSS vulnerability", "add rate limiting", "update auth..."

Integration with ADW

In your ADW orchestrator:

def classify_and_route(issue): # Classify result = execute_agent("classifier", issue) issue_type = parse_classification(result)

# Route to template
if issue_type == "/chore":
    return execute_agent("planner", "/chore", issue)
elif issue_type == "/bug":
    return execute_agent("planner", "/bug", issue)
elif issue_type == "/feature":
    return execute_agent("planner", "/feature", issue)
else:
    return mark_for_triage(issue)

Model Selection

For classification, use Haiku:

Model Speed Cost Accuracy

Haiku Fast Low Good for simple decisions

Sonnet Medium Medium Better for nuanced cases

Haiku is usually sufficient since classification is a simple decision.

Quality Metrics

Track classification quality:

• Accuracy: % correctly classified

• Confusion matrix: Which misclassifications happen

• Override rate: How often humans change classification

Related Memory Files

• @piter-framework.md - Classification is the "I" in PITER

• @adw-anatomy.md - How classification fits in ADW

• @template-engineering.md - Templates for each class

Version History

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

Last Updated

Date: 2025-12-26 Model: claude-opus-4-5-20251101