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convention-learner

Analyze repository structure and public API to infer coding conventions.

Updated Apr 27, 2026
Search Tags:#coding standards#repository analysis#public api#convention detection#project graph#editorconfig suggestions#anti-pattern tracking
One-click install
npx skills add https://github.com/shahdanish/vibepos --skill convention-learner-shahdanish
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: convention-learner
Source: https://github.com/shahdanish/vibepos/tree/main/skills/convention-learner
Command: npx skills add https://github.com/shahdanish/vibepos --skill convention-learner-shahdanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents codebase drift by learning the existing patterns and conventions already used in a project, so new code can match the established style instead of enforcing mismatched defaults.

Core Features & Use Cases

  • Convention Detection Flow: Analyzes project structure, public type shapes, folder organization, and configuration files to infer naming and organization rules.
  • Evidence-Driven Pattern Learning: Uses objective signals from codebase analysis tools (project graph and public API inspection) before proposing any conventions.
  • Convention Enforcement Guidance: Recommends how to generate matching code and how to flag deviations during review, including suggested editorconfig-style rules.
  • Anti-pattern Tracking: Identifies recurring quality issues over time so teams can document and address them consistently.

Quick Start

Run the convention detection flow for your repository to produce a structured summary of detected naming, structure, and style rules.

Frequently Asked Questions about convention-learner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect existing coding conventions in a repository?▼

To detect coding conventions in a repository, analyze the project structure, public API shapes, folder layout, and configuration files. This evidence-driven pattern learning infers naming and organization rules directly from objective codebase signals.

What is the best way to prevent codebase drift when onboarding new developers?▼

Preventing codebase drift involves learning existing project patterns and generating a conventions summary. New code can then match established styles instead of enforcing mismatched defaults during onboarding and code generation.

Can I generate editorconfig suggestions based on my current codebase?▼

Yes, you can generate editorconfig suggestions by analyzing your project graph and public API. The resulting convention enforcement guidance aligns inferred style rules with existing build props and configuration files.

How do I track anti-patterns and recurring quality issues in a project?▼

Anti-pattern tracking identifies recurring quality issues over time by inspecting the project graph and public API. Teams can document and address these consistency deviations systematically during code review.

Does repository analysis for convention detection require specific dependencies?▼

Repository analysis for convention detection requires no external dependencies. It relies on an evidence-first workflow using objective signals from project graph inspection and public API sampling to produce enforcement guidance.

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