# Tactic Recognition Module - Technical Analysis

## Branch: `codex/add-tactic-recognition-module`

## Executive Summary

The tactic recognition module has been **partially implemented** with good foundational code, but has **critical gaps** that prevent it from being merge-ready:

1. ✅ **Core detection logic is implemented** - All required tactic types are detected
2. ✅ **Data structure matches requirements** - Output format is correct
3. ✅ **Integration in ChessGame component** - Tactics are detected and stored in move history
4. ❌ **NOT integrated with LLM pipeline** - Tactic data is NOT passed to the Tutor/LLM for real-time feedback
5. ❌ **NO test coverage** - Zero tests for the tactic detection module
6. ⚠️ **Only used in post-game analysis** - Not available during gameplay

## Detailed Analysis

### 1. Implementation Quality ✅

**File: `src/lib/tacticDetection.ts`** (366 lines)

The implementation is well-structured and covers all required tactic types:

- ✅ Material capture (win_piece, win_pawn)
- ✅ Pin detection
- ✅ Fork detection  
- ✅ Skewer detection
- ✅ Check detection
- ✅ Hanging piece detection
- ✅ Conservative approach (filters false positives)

**Strengths:**
- Clean, readable code with helper functions
- Proper use of chess.js library
- Conservative detection (e.g., checks if captured piece can be recaptured)
- Correct piece value assignments
- Proper handling of edge cases (no best move, same move, etc.)

**Minor Issues:**
- Line 200: Threshold of 200cp for "win_piece" vs "win_pawn" seems arbitrary (should be 100 for pawn)
- No configuration options exposed (thresholds are hardcoded)

### 2. Integration Status ⚠️

**ChessGame.tsx Integration:**
```typescript
// Lines 337-358: Tactic detection IS called
const missedTactics = detectMissedTactics({
    fen: fenP0,
    playerColor,
    playerMoveSan: moveResult.result.san,
    bestMoveUci: evalP0.bestMove,
    cpLoss,
});
// Stored in move history
const completeHistoryItem = {
    // ... other fields
    missedTactics,
};
```

✅ Tactics ARE detected after each player move
✅ Tactics ARE stored in `moveHistory` state
✅ Tactics ARE available in `GameOverModal` for post-game analysis

**GameOverModal.tsx Integration:**
```typescript
// Lines 145-160: Tactics are formatted for LLM in post-game analysis
const describeTactics = (tactics?: DetectedTactic[]) => {
    // Formats tactics as text for LLM
};
```

✅ Tactics ARE used in post-game analysis LLM prompt

### 3. CRITICAL GAP: Real-time LLM Integration ❌

**Tutor.tsx Analysis:**

The Tutor component (which provides real-time feedback during the game) does NOT receive or use tactic data:

```typescript
// Lines 17-32: TutorProps interface
interface TutorProps {
    game: Chess;
    currentFen: string;
    userMove: Move | null;
    computerMove: Move | null;
    stockfish: Stockfish | null;
    evalP0: StockfishEvaluation | null;
    evalP2: StockfishEvaluation | null;
    openingData: OpeningMetadata | null;
    // ❌ NO missedTactics prop!
    // ...
}
```

```typescript
// Lines 185-202: LLM prompt construction
const prompt = `
[SYSTEM TRIGGER: move_exchange]
User (${playerColorName}) Move: ${userMove.san}
My (${tutorColorName}) Reply: ${computerMove.san}

My Internal Thoughts (Data):
- Pre-Eval (Before User Move): ${preScore} cp
- Post-Eval (After My Reply): ${postScore} cp
- Delta: ${delta} cp
// ❌ NO tactic information included!
`;
```

**Impact:** The AI tutor cannot provide tactical feedback during the game (e.g., "You missed a fork with Nf3!").

### 4. Test Coverage ❌

**Status:** ZERO tests for tactic detection module

**Required tests:**
- Unit tests for each tactic type detection
- Edge case tests (empty board, no tactics, multiple tactics)
- Integration tests with chess.js
- UCI to SAN conversion tests
- False positive prevention tests

### 5. Requirements Compliance

| Requirement | Status | Notes |
|-------------|--------|-------|
| Detect material capture | ✅ | Implemented with safety check |
| Detect pins | ✅ | Sliding piece logic correct |
| Detect forks | ✅ | Multi-target detection works |
| Detect skewers | ✅ | Value comparison correct |
| Detect checks | ✅ | Uses chess.js inCheck() |
| Detect hanging pieces | ✅ | Attack/defense counting |
| Conservative approach | ✅ | Multiple safety filters |
| Integrate before LLM | ❌ | Only in post-game, not real-time |
| No engine calls | ✅ | Uses provided data only |
| Configurable thresholds | ⚠️ | Hardcoded, not exposed |
| Structured output | ✅ | Matches spec exactly |

## Recommendations

### MUST HAVE (Before Merge):

1. **Add comprehensive test suite** (CRITICAL)
   - Create `src/lib/__tests__/tacticDetection.test.ts`
   - Test each tactic type with known positions
   - Test edge cases and false positive prevention
   - Aim for >80% code coverage

2. **Integrate with real-time Tutor** (CRITICAL - per requirements)
   - Add `missedTactics` prop to `TutorProps`
   - Pass tactic data from ChessGame to Tutor
   - Include tactic information in LLM prompt
   - Format tactics in a way the LLM can explain naturally

### SHOULD HAVE (Quality improvements):

3. **Fix piece value threshold**
   - Line 200: Change threshold from 200 to 100 for pawn detection

4. **Add configuration options**
   - Expose `evalLossThreshold` as a prop
   - Allow customization based on player skill level

5. **Add documentation**
   - JSDoc comments for public functions
   - Usage examples in README

## Conclusion

**Recommendation: DO NOT MERGE YET**

The implementation is solid but incomplete. The module works well for post-game analysis but fails the primary requirement: providing tactical information to the LLM during gameplay for real-time feedback.

**Estimated work to make merge-ready:**
- Test suite: 4-6 hours
- Real-time integration: 2-3 hours  
- Minor fixes: 1 hour
- **Total: ~8 hours of work**

The code quality is good and the foundation is strong. With the additions above, this will be a valuable feature.

