Conflict Detection

Conflict Detection

Detect duplicate and overlapping stories in the story-tree database.

Database: .claude/​data/​story-tree.db

Critical: Use Python sqlite3 module, NOT sqlite3 CLI.

When to Run

1. Before generating new stories - prevent creating duplicates
2. On user request - audit existing stories for conflicts
3. Periodically in CI - catch conflicts early

Conflict Types

Quick Scan

Run the detection script:

python -c "
import subprocess, sys
result = subprocess.run([sys.executable, '.claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py'],
                       capture_output=True, text=True)
print(result.stdout)
if result.returncode > 0:
    print(f'Exit code {result.returncode} = {result.returncode} conflicts found')
"

Usage Options

# Default scan (50% confidence threshold)
python .claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py

# JSON output for programmatic use
python .claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py --format json

# Lower threshold for more matches
python .claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py --min-confidence 0.4

# Custom database path
python .claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py --db path/​to/​story-tree.db

Output Format

Text (default):

Found N potential conflict(s):

## Conflict 1: DUPLICATE (confidence: 94%)
**Story A**: `1.3.5` - Matter/​Client Database Management
**Story B**: `1.8.1` - Matter/​Client Database
**Reason**: Near-identical titles: title=85%

JSON:

[{
  "story_a_id": "1.3.5",
  "story_b_id": "1.8.1",
  "conflict_type": "duplicate",
  "confidence": 0.94,
  "reason": "Near-identical titles: title=85%",
  "want_similarity": 0.45,
  "benefit_similarity": 0.32,
  "title_similarity": 0.85
}]

Handling Conflicts

After detection, resolve conflicts by:

1. Duplicates: Merge stories or reject one
2. Scope overlap: Clarify boundaries or make one a child of the other
3. Competing: Choose an approach and document decision

Update story status to rejected for discarded stories with a note explaining the decision.

Integration with Story Writing

Before generating stories for a node, check for conflicts:

python -c "
import subprocess, sys, json
result = subprocess.run([sys.executable, '.claude/​skills/​conflict-detection/​scripts/​detect_conflicts.py',
                        '--format', 'json'], capture_output=True, text=True)
conflicts = json.loads(result.stdout) if result.stdout.strip().startswith('[') else []
if conflicts:
    print(f'WARNING: {len(conflicts)} existing conflicts - resolve before adding stories')
    for c in conflicts[:3]:
        print(f'  - {c[\"conflict_type\"]}: {c[\"story_a_id\"]} vs {c[\"story_b_id\"]}')
else:
    print('No conflicts detected - safe to proceed')
"

Algorithm

1. Load active stories (skip rejected/​archived)
2. Extract components: title, want, benefit, acceptance criteria
3. Normalize tokens using domain-specific synonym groups
4. Compare all pairs using:
   • Jaccard + containment similarity on normalized tokens
   • Acceptance criteria comparison (high signal)
   • Keyword containment ratio
5. Classify conflicts based on multi-signal scoring
6. Return sorted by confidence

Constraints:

• Python stdlib only (no numpy, sklearn, sentence-transformers)
• Runs in CI without external API calls
• O(n^2) comparison - fast for <500 stories

Performance Characteristics

Tested against ground truth test cases:

Known Limitation: Stories using very different vocabulary to describe the same functionality may not be detected. This is inherent to lexical similarity without semantic embeddings.

Tuning: Lower --min-confidence to 0.40 for higher recall (more false positives).