Reduce Cyclomatic Complexity with Claude

What Is the Cyclomatic Complexity Skill?

cyclomatic-complexity is a Claude skill designed to reduce excessive branching in existing code. It is especially useful for AI-generated code that works correctly but has accumulated deeply nested conditions, numerous decision paths, or functions that are difficult for humans to maintain.

The skill guides Claude through a structured process: measure complexity per function, identify the worst hotspots, refactor those hotspots, verify behavior, and summarize the results with before-and-after complexity values.

Rather than optimizing the score with compressed expressions or clever one-liners, the skill moves decision logic into clear, well-named functions. Its goal is maintainability as well as a lower complexity measurement.

Key Features

• Per-function measurement: Complexity can be measured with Radon, ESLint, gocyclo, Lizard, or a manual count.
• Project-aware thresholds: The skill respects complexity limits already configured in the project's linter.
• Hotspot prioritization: It refactors the most complex functions first instead of changing code indiscriminately.
• Established refactoring patterns: The skill uses guard clauses, extracted functions, lookup tables, and named predicates.
• No metric gaming: Complexity is reorganized into understandable units rather than hidden in dense syntax.
• Clear reporting: Every refactor ends with a before-and-after complexity report.

Installation

Install in Claude Code

Add the project's plugin marketplace, then install the skill:

/plugin marketplace add saurabhkumar8112/cyclomatic-complexity-skill
/plugin install cyclomatic-complexity@cyclomatic-complexity-skill

After installation, Claude Code can activate the skill for relevant refactoring, cleanup, and code review requests.

Install in Claude.ai

1. Open the repository's Releases section and download the .skill file. As an alternative, create a ZIP archive of the skills/cyclomatic-complexity folder.
2. Open Claude.ai and go to Settings, then Capabilities, then Skills.
3. Upload the downloaded skill file or ZIP archive.

Install with the Claude API

For API-based workflows, upload the skill through the Claude Skills API. Follow the API guide for the current upload and configuration procedure.

Basic Usage

The skill triggers automatically when your request concerns refactoring, cleanup, or code review. For more control, explicitly name the skill and identify the file you want Claude to examine.

Use the cyclomatic complexity skill to refactor parser.py

This direct request establishes both the intended workflow and the target file. Claude can then measure complexity, locate the most problematic functions, and apply suitable refactoring patterns.

Request a Focused Code Review

You can also frame the task as a complexity-focused review:

Review parser.py with the cyclomatic complexity skill. Measure complexity per function and refactor the worst hotspots first.

If your project already defines a linter threshold, keep that configuration available in the repository so the skill can respect the project's existing standard.

Understanding the Refactoring Process

1. Measure each function: The skill uses an appropriate supported analyzer, such as Radon, ESLint, gocyclo, or Lizard. It can also use a manual count.
2. Check project thresholds: Existing linter limits take precedence when they are configured.
3. Rank hotspots: Functions with the highest complexity are addressed first.
4. Apply readable transformations: Claude introduces guard clauses, extracts functions, creates lookup tables, or names complicated predicates where appropriate.
5. Verify behavior: The refactor should preserve existing behavior, with the available test suite used for verification.
6. Report the result: The final response shows complexity before and after the changes.

Reading the Output

A completed refactor includes a report similar to the following:

## Complexity report
| Function | Before | After |
|----------|--------|-------|
| parseOrder | 14 | 4 |

Extracted: validateHeader, resolveDiscount
Behavior verified: existing test suite passes

In this example, parseOrder is reduced from a complexity of 14 to 4. The report also names the extracted functions, validateHeader and resolveDiscount, and records that the existing tests pass.

The extracted-function list matters because a lower number alone does not prove that the code is easier to maintain. Clear names show where responsibilities moved and help reviewers understand the new structure.

Refactoring Techniques Used by the Skill

Guard Clauses

Guard clauses handle invalid or exceptional conditions early. This can reduce nesting and make the primary execution path easier to follow.

Extracted Functions

When one function performs several decisions or responsibilities, the skill can move coherent pieces into well-named functions. This distributes complexity into units that communicate their intent.

Lookup Tables

Repeated branches that map known inputs to known outcomes may be replaced with a lookup table. The skill uses this approach where it improves clarity rather than merely shortening the code.

Named Predicates

Complicated conditions can be placed in functions whose names explain the business rule. This makes decision points more readable and avoids opaque conditional expressions.

Advanced Tips

Start with a Narrow Target

For a large codebase, begin with one file or a known problem area. A focused instruction such as refactor parser.py gives the skill a clear boundary and makes the resulting report easier to review.

Keep Linter Configuration Available

If your project has its own complexity threshold, ensure the relevant configuration is present when Claude performs the task. The skill is designed to follow project-defined limits instead of imposing an unrelated standard.

Ask for the Worst Hotspots First

Prioritizing the highest-complexity functions concentrates effort where it can have the greatest maintainability impact. It also avoids unnecessary changes to already-simple functions.

Review Names, Not Only Scores

Inspect the names of extracted functions and predicates in addition to the final numbers. Names such as validateHeader and resolveDiscount explain responsibility; vague helper names would make the refactor less useful even if the measured score decreased.

Use Tests as the Safety Check

Complexity reduction should preserve behavior. Keep the existing test suite available and confirm the final report records whether it passes. A better complexity score should not come at the expense of correctness.

Avoid Requests That Encourage Metric Gaming

Ask for readable, maintainable changes rather than the smallest possible score. The skill explicitly avoids hiding decisions in clever one-liners, so prompts should reinforce clarity and long-term maintainability.

Conclusion

The cyclomatic-complexity skill gives Claude a repeatable workflow for improving branch-heavy code. It measures complexity, follows project thresholds, tackles the worst functions first, applies readable refactoring patterns, verifies behavior, and finishes with a concrete before-and-after report. The project is distributed under the Apache 2.0 license.