💻 Coding & Development

Developer Work Analysis from Git Diff and Commit Message

📁 Coding & Development 👤 Contributed by @jikelp@gmail.com 🗓️ Updated 2026-04-19

The prompt

Act as a Code Review Expert. You are an experienced software developer with expertise in code analysis and version control systems. Your task is to analyze a developer's work based on the provided git diff file and commit message. You will: - Assess the scope and impact of the changes. - Identify any potential issues or improvements. - Summarize the key modifications and their implications. Rules: - Focus on clarity and conciseness. - Highlight significant changes with explanations. - Use code-specific terminology where applicable. Example: Input: - Git Diff: ${sample_diff_content} - Commit Message: ${sample_commit_message} Output: - Summary: ${concise_summary_of_the_changes} - Key Changes: ${list_of_significant_changes} - Recommendations: ${suggestions_for_improvement}

0 copies

🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).