convention-miner

name	convention-miner
description	Extract actual coding conventions from existing codebases. Discover naming patterns, package structure, test conventions. Auto-generate skill drafts.
trigger	when running convention analysis or onboarding a new codebase
tags	["conventions","mining","analysis","knowledge-mining","extraction"]
version	3.0
category	foundation
scope	platform

Convention Miner

Purpose

Scan existing code to discover actual (not prescribed) conventions. This is the learning loop entry point: mined conventions become skill drafts that, after human review, join the skill repository.

Workflow

1. Run mining scripts → JSON output
2. Review mining results → identify conventions
3. Generate skill draft → SKILL.md template
4. Human review → approve/modify/reject
5. Add to skill repository → knowledge encoded

Mining Categories

1. Naming Patterns

• Class suffixes (Service, Controller, Repository, etc.)
• Method naming conventions (camelCase, snake_case, prefixes)
• Package/module structure

Script: scripts/mine_naming.py [PROJECT_ROOT]

2. Test Conventions

• Test naming patterns (should_X_when_Y, backtick descriptions, testMethodName)
• Framework usage (JUnit 5, MockK, Testcontainers)
• Test structure compliance (AAA pattern)

3. Architecture Patterns

• Layer structure (controller → service → repository)
• Dependency direction between packages
• Cross-layer violation detection

4. Configuration Patterns

• Properties vs YAML usage
• Environment variable patterns
• Profile-specific overrides

Scripts

Script	Input	Output
mine_naming.py	Project root path	JSON: class suffixes, method patterns, test naming
generate_practice_skill.py	Mining JSON	SKILL.md draft (to stdout or file)

Learning Loop Closure

The generate_practice_skill.py script is the closure point of the learning loop:

mine_naming.py → mining_result.json → generate_practice_skill.py → SKILL.md draft → human review → skill repository

This pipeline converts raw codebase data into structured, reviewable knowledge.