Code Patterns Skill

Statistical analysis to derive project conventions from code.

Core Principle

"What's the convention here?" - Derive from code, not impose external standards.

Pattern Types

1. Error Handling

• •try/except vs if/else vs result types
• •Error classes, logging patterns, error propagation

2. Testing Conventions

• •Test file/function naming
• •Fixture usage, mock patterns, assertion style

3. Code Organization

• •Import style (absolute vs relative), grouping
• •Module structure, file/directory naming

4. Naming Conventions

• •Functions, classes, constants, private members, booleans

5. Architecture Patterns

• •Dependency injection style
• •Layer separation, return types, config access

6. Code Style

• •Line length, quote style, trailing commas, whitespace

Analysis Method

Statistical Pattern Detection

1. •Scan: Find all instances of pattern type, categorize by approach, count
2. •Calculate: adoption_rate = count(pattern) / count(all_instances)
3. •Determine convention:
   • •

     =80% = strong convention

   • •60-79% = weak convention
   • •<60% = no clear convention

Detection Techniques

AST Parsing (preferred): Use language-specific parsers for accurate structure analysis.

How to scan per pattern type:

• •Error handling: Find try/except blocks, return statements with None/Error, raise statements
• •Testing: Scan test directories for file/function naming, fixture usage, mock imports
• •Imports: Parse import statements, categorize by source (stdlib, external, internal)
• •Naming: Extract function/class/constant names, categorize by case style
• •Architecture: Find class constructors, analyze parameter injection, trace layer calls

Python AST example:

import ast

tree = ast.parse(source_code)

# Find function definitions and analyze naming
functions = [node for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)]
snake_case = sum(1 for f in functions if '_' in f.name)
camel_case = sum(1 for f in functions if f.name[0].islower() and any(c.isupper() for c in f.name))

# Calculate adoption
total = len(functions)
print(f"snake_case: {snake_case}/{total} ({100*snake_case/total:.0f}%)")

Regex Matching (fallback): For simple patterns when AST unavailable.

Regex example (Python imports):

import re

# Count absolute vs relative imports
absolute = len(re.findall(r'^from \w+\.\w+', code, re.MULTILINE))
relative = len(re.findall(r'^from \.', code, re.MULTILINE))

# Count error handling patterns
try_except = len(re.findall(r'try:.*?except', code, re.DOTALL))
return_none = len(re.findall(r'return None\s*$', code, re.MULTILINE))

Diff Analysis

Compare file to detected conventions:

1. •Analyze file using same methods
2. •Compare to project conventions
3. •Identify deviations (using minority pattern, mixing patterns, inconsistent with nearby code)

Convention Thresholds

• •Strong (>=80%): Follow in new code, refactor non-conforming
• •Weak (60-79%): Standardize, document in CONVENTIONS.md
• •None (<60%): Team decision needed

Output Format

[Pattern Type] Patterns
=======================

Analyzed: <N> instances

Pattern Distribution:
  [Pattern A]: <count> (<percent>%) <- Convention
  [Pattern B]: <count> (<percent>%)

Convention: [Pattern A] (<percent>% adoption)
Status: Strong|Weak|None

Example:
  [code showing convention]

Module Adoption:
  [module]: [percent]% ([count]/[total])

Non-conforming files:
  [file]: [reason]

Limitations

• •Statistical, not semantic: Can't understand intent, only usage
• •Language-specific: AST parsing requires language-specific parsers
• •Project-scoped: No cross-repo learning, pure project-specific detection

Philosophy

• •Descriptive, not prescriptive - Learn from code, don't impose external rules
• •Statistical, not dogmatic - 80% = convention, but context matters
• •Consistency over perfection - Clear convention beats "best" approach
• •Team-specific - Each project defines conventions through usage