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vibe-coding

Skill

Methodology for effective AI-assisted software development. Use when helping users build software with AI coding assistants, debugging AI-generated code, planning features for AI implementation, managing version control in AI workflows, or when users mention "vibe coding," Claude Code, Cursor, GitHu

0Version Control

Install

git clone https://github.com/jamditis/claude-skills-journalism.git ~/.claude/skills/vibe-coding

What is vibe-coding?

Methodology for effective AI-assisted software development. Use when helping users build software with AI coding assistants, debugging AI-generated code, planning features for AI implementation, managing version control in AI workflows, or when users mention "vibe coding," Claude Code, Cursor, GitHub Copilot, Aider, Continue, Cline, Codex, Windsurf, or similar AI coding tools. Provides strategies for planning, testing, debugging, and iterating on code written with LLM assistance.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~121 tokens of context used while enabled, before you invoke anything

Documentation

README · ~6 min read

Vibe coding methodology

Practical strategies for building software effectively with AI coding assistants.

Tool landscape moves fast. This skill was last swept 2026-05-08. The methodology (planning, version control, testing, bug-fixing) is stable; the specific tool names, instruction-file conventions, and pricing details drift quarterly. Treat the named tools as representative, not exhaustive.

Planning process

Start by working with the AI to write a detailed implementation plan in a markdown file.

Scope management: Review and refine the plan—delete unnecessary items, mark complex features as "won't do," and keep a separate section for ideas to implement later. This prevents scope creep and maintains focus.

Incremental implementation: Work section by section rather than building everything at once. Have the AI mark sections complete after successful implementation, and commit each working section to git before moving to the next.

Track progress visibly: Use todo lists, markdown checklists, or inline status markers so both you and the AI can see what's done and what remains. This prevents re-implementing completed work and keeps sessions focused.

Version control strategies

Git is your safety net — don't rely solely on the AI tool's revert functionality.

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