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affaan-m/everything-claude-code-conventions

Entwicklungskonventionen und -muster für everything-claude-code. JavaScript-Projekt mit Conventional Commits.

Was ist everything-claude-code-conventions?

Dieses Skill definiert die Entwicklungskonventionen und -muster für das Tool 'everything-claude-code', ein JavaScript-Projekt, das Conventional Commits verwendet. Es legt die Code-Struktur, Dateibenennung und Commit-Formate fest, um eine konsistente und wartbare Codebasis zu gewährleisten. Besonders nützlich für Entwickler, die mit Claude Code oder ähnlichen KI-Assistenten arbeiten und klare Richtlinien für die Zusammenarbeit und automatische Generierung benötigen. Das Skill folgt etablierten Standards wie ESLint und Prettier, um Codequalität und Lesbarkeit sicherzustellen.

Funktioniert mit✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/everything-claude-code-conventions

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Dokumentation

Was macht everything-claude-code-conventions?

Entwicklungskonventionen und -muster für everything-claude-code. JavaScript-Projekt mit Conventional Commits.

Übersicht

Dieses Skill legt die Konventionen fest, die für die Entwicklung des Projekts 'everything-claude-code' befolgt werden müssen. Es enthält Richtlinien zur Code-Struktur, Dateibenennung, Commit-Nachrichten und anderen bewährten Methoden, um Konsistenz und Wartbarkeit zu gewährleisten.

Konventionen

  • Sprache: JavaScript (ES6+)
  • Code-Stil: ESLint mit einer Standardkonfiguration, Prettier für Formatierung
  • Commits: Conventional Commits (z. B. feat:, fix:, chore:, etc.)
  • Branch-Naming: feature/, bugfix/, hotfix/, release/
  • Tests: Jest für Unit-Tests, Testabdeckung > 80 %
  • Dokumentation: JSDoc für Funktionen und Module

Verwendung

Um dieses Skill in Claude Code zu verwenden, fügen Sie es einfach Ihrem Projekt hinzu, indem Sie die Konventionen in Ihrer CLAUDE.md referenzieren. Die KI wird dann die definierten Muster bei der Codegenerierung und Analyse befolgen.

Hinweise

  • Halten Sie sich an die vorgegebene Ordnerstruktur: src/, tests/, docs/
  • Vermeiden Sie Magic Numbers und Strings; verwenden Sie Konstanten
  • Verwenden Sie console.log nur für Debugging; für Produktion verwenden Sie einen Logger
  • Alle öffentlichen APIs müssen dokumentiert sein

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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