simplifying-control-flow
Flatten nested conditionals with early returns or table-driven methods - keep nesting depth under 3 levels
ソース情報
- リポジトリ
- obra/clank
- ソースの最終更新活動
- 2025年10月7日 17:04
- 検出された SKILL.md の言語
- 英語
- スター
- 42
- フォーク
- 26
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ソースの指示 · 読み取り専用プレビュー- name
- Simplifying Control Flow
- description
- Flatten nested conditionals with early returns or table-driven methods - keep nesting depth under 3 levels
- when_to_use
- When writing conditional logic. When nesting depth exceeds 2-3 levels. When multiple conditions determine outcome. When similar if/else patterns repeat. When business rules encoded in nested ifs. When control flow is hard to follow. When nested if statements exist. When adding new cases requires deep surgery.
- version
- 1.0.0
- languages
- all
# Simplifying Control Flow
## Overview
Nested conditionals are hard to understand and error-prone. Flatten them.
**Core principle:** Nesting depth < 3 levels. Use early returns, table-driven methods, or extracted conditions.
## Baseline Violation
**Agents create nested if/else for multi-condition logic:**
❌ **Nested (baseline):**
```python
def calculate_discount(order_amount, is_vip):
if is_vip:
if order_amount > 1000:
return 0.20
elif order_amount > 500:
return 0.15
else:
if order_amount > 1000:
return 0.10
elif order_amount > 500:
return 0.05
return 0.0
```
**Problems:** Duplicated logic, hard to see all tiers, adding tier requires finding nesting spot.
## Three Techniques
### 1. Flatten with Combined Conditions
✅ **All at same level:**
```python
def calculate_discount(order_amount, is_vip):
if is_vip and order_amount > 1000: return 0.20
if is_vip and order_amount > 500: return 0.15
if order_amount > 1000: return 0.10
if order_amount > 500: return 0.05
return 0.0
```
### 2. Table-Driven (Best for Data)
✅ **Business rules as data:**
```python
DISCOUNT_TIERS = [
(1000, 0.20, 0.10), # min_amount, vip_rate, regular_rate
(500, 0.15, 0.05),
]
def calculate_discount(order_amount, is_vip):
for min_amount, vip_rate, regular_rate in DISCOUNT_TIERS:
if order_amount > min_amount:
return vip_rate if is_vip else regular_rate
return 0.0
```
**When to use:** Pricing tiers, status transitions, configuration-driven logic.
### 3. Extract Complex Conditions
✅ **Named boolean for clarity:**
```python
def is_eligible(user, minimum):
return (user.age >= 18 and user.verified_email and
user.balance > minimum and not user.suspended)
if is_eligible(user, minimum_purchase):
allow_purchase()
```
**When to use:** Complex boolean expressions, reused conditions.
## Quick Reference
| Problem | Solution |
|---------|----------|
| Nested if/else | Flatten with combined conditions OR table-driven |
| Deep nesting (>3) | Extract inner logic to function |
| Complex boolean | Extract to named function |
| Business rules | Table-driven method |
| Long if/elif chain | Table lookup OR polymorphism |
## Guard Clauses
**Baseline showed agents already use these well:**
```python
def validate(data):
if not data:
return False, "data required" # Early return
if data.amount <= 0:
return False, "amount must be positive" # Early return
# Main logic here (no nesting)
```
**Keep using this pattern for validation and error cases.**
## Red Flags
- Nesting depth > 3
- Can't see matching braces without scrolling
- Duplicate conditions in nested blocks
- Adding new case touches multiple nesting levels
**Fix:** Flatten or use table-driven.
## Real-World Impact
From baseline:
- Agents created 2-level nesting for 4 discount tiers
- With table-driven: All tiers visible, easy to add/modify
## Integration with Other Skills
**For complex functions:** See skills/keeping-routines-focused - extract when nesting gets deep
**For reducing complexity:** See skills/architecture/reducing-complexity - simpler control flow = less complexity
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