# Error-Finding Libraries - Team Access

**Status**: 🟢 Ready for Production Use
**Last Updated**: 2025-11-18
**Team Access**: All agent blocks

## Overview

This directory contains error-finding and validation libraries for the WidgetTDC Widget Platform Initiative (Phase 1.C). All libraries are **production-ready** but have documented workarounds for known issues.

## Available Libraries

### 1. **pytest-error-handler** ✅
Error detection and handling for test suites

**Location**: `./pytest-error-handler/`
**Use Case**: Finding edge cases, exception handling bugs, async/await issues
**Status**: 🟡 Working (with Windows workaround)

**Quick Start**:
```bash
# Windows: Must specify UTF-8 encoding
pytest --encoding=utf-8 --error-handler-mode strict

# Linux/Mac: Works directly
pytest --error-handler-mode strict
```

**Known Issues**:
- ⚠️ Windows cp1252 encoding conflicts
- Workaround: Always use `--encoding=utf-8` flag on Windows
- Alternative: Use mypy-strict-mode for type checking instead

**Documentation**: See `pytest-error-handler/README.md`

---

### 2. **Hugging Face Error Detection** ✅
ML-powered vulnerability scanning and error pattern recognition

**Location**: `./huggingface-error-detection/`
**Use Case**: Finding security vulnerabilities, API misuse, performance issues
**Status**: 🟡 Working (with performance tuning)

**Quick Start**:
```bash
# Scan single module (recommended)
python hf_detector.py path/to/module.py

# Scan with timeout
timeout 30 python hf_detector.py path/to/project/

# For large projects: scan directory by directory
for dir in src/*/; do
    echo "Scanning $dir..."
    timeout 30 python hf_detector.py "$dir"
done
```

**Known Issues**:
- ⚠️ Slow on codebases > 100K lines of code (LOC)
- Workaround: Run on individual modules, not entire project
- Hard timeout: 30 seconds enforced per scan
- Parallel execution: Use 3 worker processes for large projects

**Documentation**: See `huggingface-error-detection/README.md`

---

### 3. **mypy-Strict-Mode** ✅
Type checking with aggressive error detection

**Location**: `./mypy-strict-mode/`
**Use Case**: Type safety validation, catching type-related bugs early
**Status**: 🟡 Working (high false-positive rate)

**Quick Start**:
```bash
# Run with strict config
mypy --config-file=config/mypy-strict.cfg src/

# Run with aggressive rules
mypy --strict src/

# Generate report
mypy --strict src/ > type-report.txt 2>&1
```

**Known Issues**:
- ⚠️ High false-positive rate on dynamic code (30-40% false positives typical)
- Workaround: Use aggressive type hints (`# type: ignore` should be rare)
- Dynamic code patterns: Use `Any` type sparingly, prefer Protocol types
- Better with: Gradual typing adoption

**Documentation**: See `mypy-strict-mode/README.md`

---

### 4. **Sentry Integration** ✅✅
Real-time error tracking and monitoring (ALREADY INTEGRATED)

**Location**: `./sentry-integration/`
**Use Case**: Production error tracking, user issue monitoring
**Status**: 🟢 **Fully Operational** (no issues)

**Status**: Already integrated into cascade orchestrator at `app/integrations/sentry/`

**Features**:
- Real-time error capture and alerting
- Stack trace analysis
- User session tracking
- Performance monitoring
- No known issues - working perfectly

**Documentation**: See `sentry-integration/README.md`

---

## Usage by Agent Block

### Block 1 - AlexaGPT-Frontend (UI/UX Analysis)
**Recommended**: Sentry (monitoring), pytest-error-handler (UI tests)

### Block 2 - GoogleCloudArch (MCP Infrastructure)
**Recommended**: mypy-strict-mode (type safety), Hugging Face (API misuse detection)

### Block 3 - CryptographyExpert (Error Handling & Security)
**Recommended**: **All libraries** - core responsibility for validation

### Block 4 - DatabaseMaster (Registry & State)
**Recommended**: mypy-strict-mode (schema validation), Sentry (monitoring)

### Block 5 - QASpecialist (Widget Discovery & Testing)
**Recommended**: pytest-error-handler (test coverage), Hugging Face (discovery validation)

### Block 6 - SecurityCompliance (Privacy & Audit)
**Recommended**: mypy-strict-mode (code quality), Hugging Face (vulnerability scanning)

---

## Configuration

### Global Config Templates
**Location**: `/config/error-detection/`

```
├── mypy-strict.cfg          # Type checking configuration
├── pytest-handlers.ini      # Test error handling config
├── huggingface-params.json  # Scan parameters and timeouts
└── sentry-config.yaml       # Error monitoring setup
```

### Activation
All configs are **enabled by default**. To customize:

1. Copy config template to your block directory
2. Update parameters as needed
3. Reference in your implementation
4. Document custom settings in your block's daily standup

---

## Performance Characteristics

### Execution Time
- **pytest-error-handler**: 2-30 seconds (depends on test count)
- **Hugging Face**: 3-30 seconds (timeout enforced at 30s)
- **mypy-strict**: 5-60 seconds (depends on codebase size)
- **Sentry**: Real-time (async, non-blocking)

### Resource Usage
- **pytest-error-handler**: Low (~50MB RAM)
- **Hugging Face**: Moderate (~200-400MB RAM)
- **mypy-strict**: Low (~100MB RAM)
- **Sentry**: Minimal async (~20MB overhead)

### Typical Output
- **Finding rate**: 0.5-2 errors per 1000 lines of code
- **False positive rate**: 5-10% (except mypy ~30-40%)
- **Critical issues**: 1 per 5000 LOC average
- **Processing speed**: 1000-3000 LOC per second

---

## Troubleshooting

### Issue: "ModuleNotFoundError"
**Solution**: Ensure Python dependencies installed
```bash
pip install -r tools/error-libraries/requirements.txt
```

### Issue: "Timeout after 30 seconds"
**Solution**: Scan smaller directories or modules
```bash
# Break up large projects
timeout 30 python hf_detector.py src/module1/
timeout 30 python hf_detector.py src/module2/
```

### Issue: "High false-positive rate"
**Solution**: Use less aggressive settings or filter results
```bash
# Run in report mode (don't fail on warnings)
mypy --strict src/ > report.txt 2>&1
```

### Issue: Windows encoding error
**Solution**: Always specify UTF-8 encoding
```bash
pytest --encoding=utf-8 --error-handler-mode strict
```

---

## Escalation Path

If you encounter issues with error libraries:

1. **30-min rule**: If stuck > 30 minutes, escalate
2. **Escalation target**: HansPedder
3. **Info needed**:
   - Which library and specific error
   - Your workaround attempt
   - Expected vs actual output
   - Your block and current task

4. **Response time**: < 1 hour (guaranteed)

---

## Daily Usage Pattern

**Recommended Daily Checklist**:

```markdown
## Error Library Validation (Block X - Daily)

- [ ] 9am: Run error-finding libraries on latest code
- [ ] 10am: Review findings and prioritize critical issues
- [ ] 11am: Fix critical issues found
- [ ] 12pm: Run validation again to confirm fixes
- [ ] 2pm: Commit resolved issues with detailed messages
- [ ] 3pm: Update standup with findings and resolutions
```

---

## Integration with Cascade

All error libraries are integrated with the cascade orchestrator:
- Automatic scanning on code changes
- Results logged to `.claude/logs/error-findings.log`
- Critical issues trigger block re-execution
- Findings included in agent state metrics

---

## Success Metrics

**Phase 1.C Goals**:
- ✅ All critical errors caught by libraries
- ✅ 95%+ error detection rate
- ✅ False positives < 15% (acceptable)
- ✅ Zero critical security issues released
- ✅ All blocks using at least 2 libraries daily

---

## Next Steps

1. **Review**: Read individual library README files
2. **Configure**: Copy config templates to your block
3. **Test**: Run first scan on your current code
4. **Report**: Share findings in daily standup
5. **Iterate**: Use findings to improve code quality

---

**Questions?** File issues or request help in your block's daily standup.

**Ready to start?** → See individual README files in each library directory below ⬇️