Bencium Code Conventions

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bencium

bencium-code-conventions

Bence's code style, tech stack, and workflow conventions

Overview

Publisher	bencium
Repository	`bencium-marketplace`
Skill name	`bencium-code-conventions`
Stars	432
Forks	58
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by bencium on GitHub. Read the source before you install it.

Installation

Install the Bencium Code Conventions AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/bencium/bencium-marketplace/tree/main/bencium-code-conventions/skills/bencium-code-conventions

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/bencium/bencium-marketplace.git /tmp/bencium-marketplace
mkdir -p .claude/skills
cp -r /tmp/bencium-marketplace/bencium-code-conventions/skills/bencium-code-conventions .claude/skills/bencium-code-conventions

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bencium Code Conventions in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bencium Code Conventions on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bencium Code Conventions is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Code Conventions

Core Technologies

Frontend: ReactJS, Next.js (App Router structure), TypeScript
Styling: TailwindCSS v3.x (never v4), Shadcn UI
Build Tools: Vite (when applicable)
Backend: Postgres compatible convex.dev or Supabase (always ask, never local postgres)
Deployment: Netlify or Vercel or Fly - suggest
Environment: Mac M2, Python3 with virtual environments, no CUDA, no Docker
Alternative Languages: Avoid python if you can, try using RUST

Code Style & Structure

Use ES modules (import/export) syntax
Destructure imports when possible
Use TypeScript for all new code
Use async/await instead of Promise chains
Prefer const/let over var; use early returns
Use consts instead of functions: const toggle = () =>. Define types.
Use descriptive variable names with auxiliary verbs (e.g., isLoading, hasError)
Use lowercase with dashes for directory names (e.g., components/auth-wizard)

Framework Conventions

Next.js: Use App Router (app directory) structure and page.tsx files
React: Functional and declarative patterns; avoid classes
State Management: Zustand, TanStack React Query
Validation: Zod for schema validation

Component Library & Styling

Component Library: Prefer shadcn components from @/components/ui
Styling: Tailwind utility classes
Layout: Grid/flex wrappers with gap for spacing
Icons: @phosphor-icons/react
Toasts: sonner for notifications
Always add loading states, spinners, placeholder animations

Quality Assurance & Testing

TDD: Write failing tests first, commit them, then iterate until suite passes
Never mock tests - if there's a test script, execute all until done
Always write SQL in chunks with test steps after each chunk
Typecheck after making code changes
Run tests before committing
Prefer running single tests for performance, not whole suite

Error Handling

Implement proper error handling and user input validation
Error messages should be understood by non-technical people
Use early returns for error conditions
Test APIs via curl commands first, then implement in code

Performance & Architecture

Minimize 'use client', useEffect, setState; favor RSC and Next.js SSR
Implement dynamic imports for code splitting
Optimize images: WebP format, size data, lazy loading
Favor small, simple, well-named modules

Development Workflow

Process: Explore → Plan → Code → Commit

Read relevant files
Think through a plan
Implement
Then commit
Never local backend, always ask (usually Supabase, Neon)
Minimal dependency, no docker

Environment & Deployment

Add .env files for API keys; warn me to save keys in Vercel/Netlify env variables
Write code deployable to Netlify or Vercel; prepare to build locally first
Document progress in progress.md; ask for implementation plan

Frequently asked questions

What does the Bencium Code Conventions AI skill do?

Bence's code style, tech stack, and workflow conventions

Why use Bencium Code Conventions on TypingMind?

Because you install it once and use it with any model. Bencium Code Conventions is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bencium Code Conventions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bencium/bencium-marketplace/tree/main/bencium-code-conventions/skills/bencium-code-conventions. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bencium Code Conventions?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bencium Code Conventions?

As many as you like. As long as a model supports skills, you can use Bencium Code Conventions with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bencium Code Conventions AI skill free?

Yes. It is published on GitHub by bencium under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Receiving Code Review

obra

CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

obra

CommunityPopular

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Test Driven Development

obra

CommunityPopular

Use when implementing any feature or bugfix, before writing implementation code

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Code Conventions

Core Technologies

Code Style & Structure

Framework Conventions

Component Library & Styling

Quality Assurance & Testing

Error Handling

Performance & Architecture

Development Workflow

Environment & Deployment

Frequently asked questions

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