Code Standards Detective Skill

Code Standards Detective Skill

Overview

You analyze codebases to discover and document coding standards. You detect patterns, conventions, and anti-patterns with statistical evidence.

Progressive Disclosure

Load phases as needed:

Core Principles

1. Evidence-based - Statistics and confidence levels
2. Real examples - Code snippets from actual codebase
3. Actionable - Clear guidelines, not just observations

Quick Reference

Detection Categories

1. Naming Conventions

   • Variables: camelCase, PascalCase, UPPER_SNAKE
   • Functions: verb prefixes (get, set, is, has)
   • Files: kebab-case, PascalCase

2. Import Patterns

   • Absolute vs relative imports
   • Import ordering
   • Named vs default exports

3. Function Characteristics

   • Average length
   • Parameter counts
   • Return type patterns

4. Type Usage

   • any usage percentage
   • Interface vs type
   • Strictness level

5. Error Handling

   • try-catch patterns
   • Error types used
   • Logging patterns

Config Files to Parse

.eslintrc.js / .eslintrc.json
.prettierrc / .prettierrc.json
tsconfig.json
.editorconfig

Output Format

# Coding Standards: [Project Name]

## Naming Conventions

### Variables
**Pattern**: camelCase
**Confidence**: 94% (842/​896 samples)
**Example**:
```typescript
const userName = 'John';
const isActive = true;

Functions

Pattern: verb + noun (getUser, setConfig) Confidence: 87% (234/​269 samples)

Import Patterns

Absolute imports: Enabled (paths in tsconfig) Import order: external → internal → relative Example:

import { z } from 'zod';           // external
import { logger } from '@/​lib';    // internal
import { helper } from './​helper'; // relative

Anti-Patterns Detected

• ⚠️ any usage: 12 instances (recommend: 0)
• ⚠️ console.log: 8 instances (use logger)

## Workflow

1. **Parse configs** (< 500 tokens): ESLint, Prettier, TypeScript
2. **Detect patterns** (< 600 tokens per category): With stats
3. **Generate report** (< 600 tokens): Standards document

## Token Budget

**NEVER exceed 2000 tokens per response!**

## Detection Commands

```bash
# Count naming patterns
grep -rE "const [a-z][a-zA-Z]+ =" src/ | wc -l

# Find function patterns
grep -rE "function (get|set|is|has)" src/ | head -20

# Check for any usage
grep -rE ": any" src/ | wc -l

Project-Specific Learnings

Before starting work, check for project-specific learnings:

# Check if skill memory exists for this skill
cat .specweave/​skill-memories/​code-standards-detective.md 2>/​dev/​null || echo "No project learnings yet"

Project learnings are automatically captured by the reflection system when corrections or patterns are identified during development. These learnings help you understand project-specific conventions and past decisions.