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Zhongwei Li
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commands/history-analysis/detect-patterns.md
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commands/history-analysis/detect-patterns.md
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# Operation: Detect Commit Patterns
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**Purpose:** Identify project-specific commit message patterns, conventions, and formatting standards from commit history.
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## Parameters
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From `$ARGUMENTS` (after operation name):
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- `count:N` - Number of commits to analyze (default: 50)
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- `branch:name` - Branch to analyze (default: current branch)
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- `format:json|text` - Output format (default: text)
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- `detailed:true|false` - Include detailed pattern breakdown (default: false)
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## Workflow
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### 1. Validate Repository
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```bash
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# Verify git repository
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if ! git rev-parse --git-dir >/dev/null 2>&1; then
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echo "Error: Not in a git repository"
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exit 1
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fi
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```
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### 2. Execute Pattern Detection
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Invoke the pattern-detector.py utility script:
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```bash
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./.scripts/pattern-detector.py --count <count> --branch <branch> [--detailed]
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```
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The script analyzes:
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- **Format Patterns:** Message structure and formatting
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- **Convention Patterns:** Conventional commits, semantic versioning references
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- **Content Patterns:** Imperative mood, capitalization, punctuation
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- **Metadata Patterns:** Issue references, co-authors, sign-offs
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- **Body Patterns:** Bullet points, wrapping, section structure
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### 3. Pattern Categories Analyzed
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**A. Format Patterns**
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```python
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patterns = {
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'conventional_commits': 0, # type(scope): subject
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'simple_subject': 0, # Just a subject line
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'prefixed': 0, # [PREFIX] subject
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'tagged': 0, # #tag subject
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'other': 0
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}
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```
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**B. Convention Patterns**
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```python
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conventions = {
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'imperative_mood': 0, # "add" vs "added"
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'capitalized_subject': 0, # First letter capitalized
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'no_period_end': 0, # No period at end
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'blank_line_before_body': 0, # Proper body separation
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'wrapped_body': 0, # 72-char wrap
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'has_footer': 0 # Breaking changes, issues
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}
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```
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**C. Content Patterns**
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```python
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content = {
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'references_issues': 0, # #123, Closes #456
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'mentions_breaking': 0, # BREAKING CHANGE:
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'has_co_authors': 0, # Co-authored-by:
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'signed_off': 0, # Signed-off-by:
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'includes_rationale': 0, # "because", "to", "for"
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'mentions_impact': 0 # "affects", "impacts", "changes"
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}
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```
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### 4. Pattern Analysis Algorithm
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The pattern-detector.py script implements:
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```python
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def analyze_commit_patterns(commits):
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"""
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Analyze commit messages for patterns.
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Returns pattern frequencies and confidence scores.
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"""
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patterns = initialize_pattern_counters()
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for commit in commits:
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# Parse commit structure
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subject, body, footer = parse_commit(commit)
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# Detect format pattern
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if is_conventional_commit(subject):
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patterns['format']['conventional_commits'] += 1
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elif has_prefix(subject):
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patterns['format']['prefixed'] += 1
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# ... more checks
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# Detect conventions
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if is_imperative_mood(subject):
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patterns['conventions']['imperative_mood'] += 1
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if is_capitalized(subject):
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patterns['conventions']['capitalized_subject'] += 1
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# ... more checks
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# Detect content patterns
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if references_issues(commit):
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patterns['content']['references_issues'] += 1
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# ... more checks
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# Calculate percentages
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return calculate_pattern_percentages(patterns, len(commits))
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```
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### 5. Output Structure
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**Text Format (default):**
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```
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Commit Pattern Analysis
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=======================
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Commits Analyzed: 50
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Branch: main
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FORMAT PATTERNS
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---------------
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Conventional Commits: 87% (44/50) ✓ DOMINANT
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Example: feat(auth): implement OAuth2
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Simple Subject: 10% (5/50)
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Example: Update documentation
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Prefixed: 3% (1/50)
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Example: [HOTFIX] Fix critical bug
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CONVENTION PATTERNS
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-------------------
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Imperative Mood: 92% (46/50) ✓ STRONG
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Capitalized Subject: 94% (47/50) ✓ STRONG
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No Period at End: 88% (44/50) ✓ STRONG
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Blank Line Before Body: 100% (17/17) ✓ PERFECT
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Body Wrapped at 72: 94% (16/17) ✓ STRONG
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Has Footer: 26% (13/50) ○ MODERATE
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CONTENT PATTERNS
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----------------
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References Issues: 67% (34/50) ✓ COMMON
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Mentions Breaking: 8% (4/50) ○ OCCASIONAL
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Has Co-Authors: 2% (1/50) ✗ RARE
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Signed-Off: 12% (6/50) ○ OCCASIONAL
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Includes Rationale: 45% (23/50) ○ MODERATE
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Mentions Impact: 31% (16/50) ○ MODERATE
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DETECTED CONVENTIONS
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--------------------
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✓ Project uses conventional commits format
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✓ Strong imperative mood usage
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✓ Consistent capitalization and punctuation
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✓ Frequent issue references
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○ Moderate footer usage
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○ Occasional breaking change mentions
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PATTERN CONSISTENCY
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-------------------
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Overall Score: 85/100 (GOOD)
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Format: High (87% conventional)
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Conventions: High (90%+ adherence)
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Content: Moderate (varied usage)
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RECOMMENDATIONS
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---------------
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• Continue using conventional commits format
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• Maintain imperative mood in subject lines
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• Consider more consistent footer usage
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• Document rationale in commit bodies when complex
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```
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**JSON Format:**
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```json
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{
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"analysis_type": "pattern_detection",
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"commits_analyzed": 50,
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"branch": "main",
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"patterns": {
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"format": {
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"conventional_commits": {"count": 44, "percentage": 87, "strength": "dominant"},
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"simple_subject": {"count": 5, "percentage": 10, "strength": "rare"},
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"prefixed": {"count": 1, "percentage": 3, "strength": "rare"}
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},
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"conventions": {
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"imperative_mood": {"count": 46, "percentage": 92, "strength": "strong"},
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"capitalized_subject": {"count": 47, "percentage": 94, "strength": "strong"},
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"no_period_end": {"count": 44, "percentage": 88, "strength": "strong"}
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},
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"content": {
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"references_issues": {"count": 34, "percentage": 67, "strength": "common"},
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"mentions_breaking": {"count": 4, "percentage": 8, "strength": "occasional"}
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}
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},
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"consistency_score": 85,
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"dominant_pattern": "conventional_commits",
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"recommendations": [
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"Continue using conventional commits format",
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"Maintain imperative mood in subject lines"
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]
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}
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```
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### 6. Pattern Strength Classification
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```
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PERFECT: 95-100% - Universal usage
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STRONG: 80-94% - Very consistent
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DOMINANT: 65-79% - Clear preference
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COMMON: 45-64% - Regular usage
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MODERATE: 25-44% - Occasional usage
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OCCASIONAL: 10-24% - Infrequent usage
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RARE: 1-9% - Seldom used
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ABSENT: 0% - Not used
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```
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### 7. Detailed Pattern Breakdown
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When `detailed:true` is specified, include:
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**Per-Pattern Examples:**
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```
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IMPERATIVE MOOD (92%)
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✓ "Add user authentication"
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✓ "Fix null pointer exception"
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✓ "Update API documentation"
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✗ "Added new feature"
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✗ "Updated dependencies"
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```
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**Timeline Analysis:**
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```
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Pattern Evolution (most recent 10 commits):
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Conventional Commits: 10/10 (100%) - Improving ↑
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Imperative Mood: 9/10 (90%) - Stable →
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Issue References: 8/10 (80%) - Improving ↑
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```
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## Error Handling
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**No git repository:**
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- Return: "Error: Not in a git repository"
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- Exit code: 1
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**No commits:**
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- Return: "Error: No commit history to analyze"
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- Exit code: 1
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**Insufficient commits:**
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- Warning: "Only X commits available (requested Y)"
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- Proceed with available commits
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**Pattern detection fails:**
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- Return partial results with warning
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- Indicate which patterns couldn't be detected
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## Integration Usage
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**By commit-assistant agent:**
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```
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Agent: Determine project conventions
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→ Invoke: /history-analysis detect-patterns
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→ Learn: Project uses conventional commits (87%)
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→ Apply: Use conventional format for new commits
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```
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**By message-generation skill:**
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```
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Before generating:
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→ Detect dominant patterns
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→ Extract format preferences
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→ Match project conventions
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```
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**By commit-best-practices skill:**
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```
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When reviewing commits:
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→ Compare against detected patterns
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→ Flag deviations from project norms
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→ Suggest consistency improvements
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```
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## Success Criteria
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Operation succeeds when:
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- [x] All pattern categories analyzed
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- [x] Frequencies calculated accurately
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- [x] Strength classifications assigned
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- [x] Consistency score computed
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- [x] Dominant pattern identified
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- [x] Recommendations generated
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- [x] Output formatted correctly
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## Performance
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- **Analysis Time:** ~2-3 seconds for 50 commits
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- **Memory Usage:** Low (streaming analysis)
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- **Accuracy:** High (>95% pattern detection accuracy)
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Reference in New Issue
Block a user