Tool Overview

This free online commit message generator creates conventional commit messages by analyzing your Git diff output. You paste the diff from your terminal or Git client, and the tool examines the changes to determine the commit type, scope, and subject. It then formats everything into a proper conventional commit message that you can copy and use in your git commit command or GUI client.

Writing good commit messages takes time and mental effort. You need to decide the type, choose a scope, write a clear subject, and sometimes add a body. When you are focused on coding, stopping to craft the perfect message can interrupt your flow. This generator works like an AI commit message generator for your diffs: it handles the analysis and formatting automatically, so you can generate git commit messages quickly while still maintaining clear history.

The tool is designed for developers, team leads, and anyone who makes Git commits regularly and wants a simple conventional commit message generator in the browser. Beginners can learn conventional commit format by seeing how diffs become messages. Experienced users can speed up their workflow without sacrificing message quality. Teams benefit from consistent commit messages that make history easier to read, search, and automate for changelogs and semantic versioning.

Background & Concept Explanation

Conventional Commits is a specification that standardizes commit message format. A conventional commit has a type like feat or fix, an optional scope in parentheses, a subject line, and optional body and footer sections. This format makes it easier to understand what changed, generate changelogs automatically, and determine version bumps based on commit history.

Many developers struggle with commit messages because they are writing them after the work is done, when details might be fuzzy. They might forget what type of change it was, struggle to summarize complex changes, or write messages that are too vague or too detailed. Without a standard format, commit history becomes inconsistent and hard to navigate. A related operation involves generating Git commands as part of a similar workflow.

The Commit Message Generator addresses this by reading your actual code changes from the diff. It looks at which files changed, what was added or removed, and uses pattern matching to infer the commit type and scope. It then builds a message following conventional commit format. This means the message reflects what actually changed, not what you remember changing.

Key Features

• Git diff input processing. You paste standard unified diff output from Git into a large text area. The tool accepts diffs up to 50KB in size, which covers most typical commits. It processes the diff immediately as you paste or type, giving you instant results.
• Automatic file change detection. The parser scans diff lines that start with +++ b/ to identify which files were modified. It collects these filenames and uses them to determine scope and context for the commit message.
• Addition and deletion tracking. The tool separates lines that start with + from lines that start with -. It collects added lines and deleted lines separately, which helps it understand whether you added features, fixed bugs, or removed code.
• Heuristic-based type detection. The analyzer examines file names and change content to determine the commit type. It looks for keywords like test, expect, or assert to suggest test type. It checks for .md files or docs folders to suggest docs type. It searches for fix, bug, error, or handle to suggest fix type. It looks for refactor or cleanup to suggest refactor type. It checks for style, css, padding, or color to suggest style type. It searches for optimized, faster, or performance to suggest perf type. If additions exist and no other type matches, it defaults to feat type. If only deletions exist, it uses chore type.
• Scope extraction from file paths. The tool uses the first changed file to determine scope. It takes the parent folder name if the file is in a subdirectory, or the base filename without extension if the file is at the root. This creates scopes like auth, utils, or components that help categorize commits.
• Subject line generation. The analyzer creates a subject by examining the first few added lines and extracting meaningful keywords. If additions exist, it builds a subject like add keyword and related changes. If only deletions exist, it uses remove unused code or dependencies. The subject is kept concise to fit conventional commit guidelines.
• Conventional commit format output. The tool combines type, optional scope in parentheses, and subject into a single line following the format type(scope): subject. This matches the Conventional Commits specification exactly, making messages compatible with automated tooling.
• Real-time analysis and updates. As soon as you paste or modify the diff, the tool runs analysis and updates the commit message. There is no button to click or delay to wait for. This gives you immediate feedback as you refine your diff.
• Sample diff loader. When the input is empty, a Try sample diff button appears. Clicking it loads a realistic example diff showing changes to an authentication file. This helps you understand what kind of input the tool expects and how it processes diffs.
• Local storage persistence. The tool automatically saves your diff input to browser storage. When you return to the page, it restores your previous diff so you can continue working or refine the message. This makes it easy to iterate on commit messages.
• Copy to clipboard functionality. A Copy message button appears below the generated commit message. Clicking it copies the message to your clipboard, and the button briefly shows Copied with a checkmark icon. This makes it easy to paste the message into your Git client or terminal.
• AI-powered enhancement option. A Get suggestions button sends your diff to a backend AI service for deeper analysis. The AI returns a more refined commit message that might be more descriptive or follow better conventions. You can use this enhanced message or stick with the heuristic result.
• Method indicator badges. The generated message shows a badge indicating whether it came from heuristic analysis or AI enhancement. Standard messages show a gray Standard badge, while AI-enhanced messages show a green Smart suggestions badge. This helps you understand the source of each message.
• Character count display. The tool shows how many characters you have pasted out of the maximum allowed. This helps you stay within limits and understand when your diff might be getting too large for processing.
• Clear input action. When you have text in the diff area, a clear button appears in the top right corner. Clicking it removes all input and resets the results, letting you start fresh quickly.

Common Use Cases

A developer finishes adding a new login feature. They run git diff to see their changes, copy the output, and paste it into the generator. The tool detects feat type from the additions, extracts auth as the scope from the file path, and generates feat(auth): add login functionality. They copy the message and commit with it.

Someone fixes a bug that was causing crashes. They paste the diff showing error handling additions. The tool recognizes fix keywords in the changes, determines the scope from the affected file, and creates fix(api): handle error cases. They use this message for their commit.

A team member updates documentation files. They paste a diff showing changes to README.md. The tool detects the .md extension, identifies docs type, and generates docs: update README with new instructions. They copy and commit with this clear message. For adjacent tasks, generating branch names addresses a complementary step.

A developer refactors payment processing code. They paste the diff showing structural changes. The tool detects refactor keywords, extracts the payment scope, and creates refactor(payment): restructure processing logic. They optionally click Get suggestions to see if the AI can improve the message further.

How to Use This Tool (Step-by-Step)

1. Open the Commit Message Generator. You will see a large text area on the left for pasting diffs and a results panel on the right.
2. In your terminal or Git client, run git diff to see your uncommitted changes, or git diff --cached to see staged changes. Select and copy the entire diff output.
3. Paste the diff into the text area on the left side of the tool. The tool will immediately start analyzing the diff and generating a commit message.
4. Watch the results panel on the right. Within moments, a commit message will appear in a white box. The message follows conventional commit format with type, optional scope, and subject.
5. Review the generated message. Check that the type matches your change, the scope makes sense, and the subject accurately describes what you did. If something looks wrong, you can modify your diff or try the AI enhancement.
6. If you want a more refined message, click the Get suggestions button below the generated message. Wait for the AI service to analyze your diff. When it finishes, the message will update with an AI-enhanced version, and the badge will change to Smart suggestions.
7. When you are satisfied with the message, click the Copy message button. The button will briefly show Copied with a checkmark to confirm the copy succeeded.
8. Open your terminal or Git client, paste the commit message, and complete your commit. You can use git commit -m followed by the pasted message, or paste it into your Git client's commit dialog.
9. If you want to start over, click the clear button in the top right of the diff area. This removes all input and resets the results so you can paste a new diff.
10. To see an example of how the tool works, click Try sample diff when the input is empty. This loads a sample diff showing authentication changes, and you can see how it generates a commit message from real code changes.

Calculations & Logic

The Commit Message Generator processes diffs through a series of parsing and analysis steps. First, it splits the diff into individual lines and validates that the input is long enough to be meaningful. If the diff is too short or empty, it returns a message asking for valid input.

Next, it scans through each line to identify file changes, additions, and deletions. Lines starting with +++ b/ indicate modified files, and the tool extracts the file path after the prefix. Lines starting with + but not +++ are added content, and lines starting with - but not --- are deleted content. The tool collects these separately for analysis.

To determine the commit type, the tool combines all file names and all change content into lowercase strings. It then checks for type-specific keywords in a priority order. Test-related keywords like test, expect, or assert suggest test type. Documentation indicators like .md or docs/ suggest docs type. Bug-related words like fix, bug, error, or handle suggest fix type. Refactoring keywords like refactor or cleanup suggest refactor type. Styling keywords like style, css, padding, or color suggest style type. Performance keywords like optimized, faster, or performance suggest perf type. If additions exist and no other type matches, it chooses feat type. If only deletions exist, it defaults to chore type. When working with related formats, generating changelogs can be a useful part of the process.

For scope extraction, the tool takes the first changed file and splits its path by slashes. If the file is in a subdirectory, it uses the parent folder name as the scope. If the file is at the root, it uses the base filename without extension. This creates meaningful scopes that help categorize commits.

To generate the subject line, the tool examines the first added line if additions exist. It extracts words that are at least three letters long and uses the first meaningful keyword to build a subject like add keyword and related changes. If only deletions exist, it uses a generic subject about removing code.

The tool then combines these parts into a conventional commit message. If a scope was determined, it formats as type(scope): subject. If no scope was found, it formats as type: subject. The result is a single-line message that follows the specification.

The analysis returns an AnalysisResult object containing the message string, a confidence score of 0.7 for heuristic analysis, and a method field set to heuristic. When AI enhancement is used, the method changes to ai and the confidence increases to 0.95. In some workflows, generating .gitignore files is a relevant follow-up operation.

For AI enhancement, the tool sends the entire diff string to a backend service. The service analyzes the changes more deeply and returns a refined commit message. The tool validates that the response is a non-empty string, strips any surrounding quotes, and updates the result with the AI message and method indicator.

Tips, Limitations & Best Practices

Paste complete, clean diffs for best results. Include all relevant changes, but avoid including unrelated files or very large diffs that might confuse the analysis. The tool works best with focused commits that change a few related files.

Review generated messages before committing. The heuristic analysis is good but not perfect. Check that the type, scope, and subject accurately reflect your changes. Adjust manually if needed, or use AI enhancement for a second opinion.

Use AI suggestions when you want more descriptive messages. The AI can provide better context and more detailed subjects, especially for complex changes. However, always verify that AI suggestions match your actual work before committing. For related processing needs, generating GitHub Actions handles a complementary task.

Keep diffs focused on single logical changes. If your diff includes multiple unrelated changes, the generator might pick the wrong type or create a vague subject. Consider splitting large changes into multiple commits, each with its own focused diff.

Understand that heuristic analysis uses pattern matching. It looks for keywords and file patterns, but it cannot understand code semantics deeply. For unusual changes or edge cases, you might need to refine the message manually.

Use the sample diff to learn the format. Loading the sample shows you what kind of input works well and how the tool processes real code changes. This helps you write better commit messages even when not using the generator.

Finally, remember that good commit messages help your team. Even with automatic generation, take a moment to ensure the message clearly communicates what changed and why. This makes code history more valuable for everyone who reads it later.