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---
title: Critical Anti-Patterns Discovered During Mindmodel Generation
date: 2026-04-12
category: docs/solutions/best-practices
module: stemwijzer
problem_type: best_practice
component: documentation
severity: critical
applies_when:
- When adding logging to any module
- When working with Streamlit test isolation
- When generating or updating .mindmodel/ for this project
tags: [anti-patterns, logging, streamlit, mindmodel, code-quality]
---
# Critical Anti-Patterns Discovered During Mindmodel Generation
## Context
During a comprehensive mindmodel generation session (Phase 1: 7 parallel analysis agents, Phase 2: constraint-writer assembly), several critical anti-patterns were discovered and documented in `.mindmodel/anti-patterns/anti-patterns.yaml`. This document captures the key findings for future reference.
## Guidance
### 1. Use Logging, Not Print Statements
**CRITICAL**: `api_client.py` uses `print()` instead of logging throughout (11 instances).
**Broken pattern:**
```python
# api_client.py - BAD
print(f"Fetched {len(voting_records)} voting records from API")
print(f"Error fetching motions from API: {e}") # No traceback
```
**Correct pattern:**
```python
# GOOD - use logging throughout
import logging
_logger = logging.getLogger(__name__)
def get_motions(self, ...):
try:
_logger.info("Fetched %d voting records from API", len(voting_records))
except Exception as e:
_logger.exception("Error fetching motions from API: %s", e)
return []
```
### 2. Streamlit Global State Replacement
**CRITICAL**: `explorer.py` has module-level `st = _DummySt()` which shadows Streamlit globally.
**Broken pattern:**
```python
# explorer.py - BAD
try:
import plotly.express as px
except Exception:
class _DummySt:
figure = _DummyFigure
# ...
st = _DummySt() # Global replacement - affects all imports!
```
**Correct pattern:**
```python
# GOOD - use conditional flags
try:
import plotly.express as px
import plotly.graph_objects as go
HAS_PLOTLY = True
except ImportError:
HAS_PLOTLY = False
px = None
go = None
def render_chart(data):
if not HAS_PLOTLY:
_logger.warning("Plotly not available")
return
# ... rest of chart logic
```
### 3. Logger Naming Inconsistency
**WARNING**: 33 files split between `logger = logging.getLogger(__name__)` and `_logger = logging.getLogger(__name__)`.
Files with `logger` (16): api_client.py, ai_provider.py, pipeline files, analysis files
Files with `_logger` (17): database.py, explorer.py, explorer_helpers.py
**Recommendation**: Standardize on `_logger` for module-level loggers. Update CODE_STYLE.md to explicitly state the convention.
### 4. Bare Except with Pass
**CRITICAL**: `database.py` line 47 has bare `except: pass` that catches KeyboardInterrupt, SystemExit, MemoryError.
**Broken pattern:**
```python
# database.py line 47 - BAD
try:
conn.execute("CREATE SEQUENCE IF NOT EXISTS motions_id_seq START 1")
except: # Catches EVERYTHING
pass
```
**Correct pattern:**
```python
# GOOD
try:
conn.execute("CREATE SEQUENCE IF NOT EXISTS motions_id_seq START 1")
except Exception as exc:
_logger.debug("Sequence creation skipped: %s", exc)
```
## Why This Matters
1. **Logging over Print**: Structured logging enables log aggregation, filtering by level, and includes stack traces. Print statements are invisible in production and provide no context during failures.
2. **Global State**: Module-level replacements of standard library modules cause subtle bugs where code imports work differently depending on import order.
3. **Consistency**: Mixed logger naming makes code harder to grep and grep-replace. Pick one convention and enforce it via linting.
4. **Bare Except**: Catching all exceptions including `KeyboardInterrupt` and `SystemExit` can prevent graceful shutdown and mask serious issues.
## When to Apply
- Before committing any logging changes: ensure using `_logger`, not `print()`
- When adding optional dependency handling: use flags, not global replacements
- When updating CODE_STYLE.md: add explicit logger naming convention
- When updating .mindmodel/: verify anti-patterns section is current
## Examples
### Fixing api_client.py Logging
```python
# Before (broken)
print(f"Processed {count} motions")
# After (correct)
_logger.info("Processed %d motions", count)
```
### Fixing Exception Handling
```python
# Before (broken)
try:
risky_operation()
except:
pass
# After (correct)
try:
risky_operation()
except Exception as exc:
_logger.warning("Operation failed: %s", exc)
return safe_fallback
```
## Related
- `.mindmodel/anti-patterns/anti-patterns.yaml` - Full anti-pattern documentation
- `.mindmodel/constraints/logging.yaml` - Logging conventions
- `.mindmodel/constraints/error-handling.yaml` - Error handling patterns
- `CODE_STYLE.md` - Code style guide (needs update for logger naming)
- `AGENTS.md` - Project conventions (RIGHT-wing parties on RIGHT, SVD labels = voting patterns)