remove-ai-comments
Removes redundant, obvious, or "AI-flavored" comments from code to improve signal-to-noise ratio. Use when the user asks to "clean up comments", "remove AI comments", or makes a general request to refactor verbose code documentation.
Best use case
remove-ai-comments is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Removes redundant, obvious, or "AI-flavored" comments from code to improve signal-to-noise ratio. Use when the user asks to "clean up comments", "remove AI comments", or makes a general request to refactor verbose code documentation.
Teams using remove-ai-comments should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/remove-ai-comments/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How remove-ai-comments Compares
| Feature / Agent | remove-ai-comments | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Removes redundant, obvious, or "AI-flavored" comments from code to improve signal-to-noise ratio. Use when the user asks to "clean up comments", "remove AI comments", or makes a general request to refactor verbose code documentation.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Remove "AI-Flavored" Comments
## Purpose
This skill guides the removal of low-value comments often generated by AI coding assistants. These comments typically narrate the code structure, state the obvious, or use verbose "tutorial style" explanations that clutter professional codebases.
## Identification Guide
Identify and remove comments that fall into these categories:
### 1. The "Narrator"
Comments that announce the start of a standard coding construct.
* **Remove**: `// Begin function`
* **Remove**: `// Loop through the array`
* **Remove**: `// Define variables`
* **Remove**: `// Initialize class`
* **Remove**: `// End of if statement`
### 2. The "Translator"
Comments that merely translate the code line into English without adding context.
* **Remove**: `i += 1 // Increment i`
* **Remove**: `return result // Return the result`
* **Remove**: `print(error) // Print the error`
### 3. The "Step-by-Step" (AI Tutorial Style)
Numbered steps that explain standard logic flows.
* **Remove**: `// Step 1: Get the data`
* **Remove**: `// Step 2: Validation`
* **Remove**: `// Step 3: Return response`
### 4. The "Placeholder"
Empty or content-free comments left over from templates.
* **Remove**: `// TODO: implementation` (only if the implementation is already there)
* **Remove**: `/* Your code here */`
## Retention Rules (When NOT to remove)
Do **NOT** remove:
* **Docstrings/JSDocs**: API documentation describing inputs, outputs, and public interfaces.
* **"Why" Comments**: Explanations of *why* a decision was made (e.g., specific workarounds, optimizations, business logic reasoning).
* **Warnings**: `WARNING`, `CAUTION`, or notes about side effects.
* **Todos**: Actionable `TODO` or `FIXME` items (unless the user specifically asks to clear them).
## Workflow
1. **Analyze**: Read the target file to understand its purpose.
2. **Evaluate**: Look at the comments. Run `python scripts/comment_density.py <file>` (if available) to gauge initial density.
3. **Process**:
* Iterate through comments.
* If a comment matches the "Identification Guide" for removal, delete it.
* If a comment explains complex logic, *keep it* or *refine it* if it's too verbose.
4. **Cleanup**: Remove any resulting double empty lines or trailing whitespace.
## Example
**Before (AI Style):**
```python
# Import the datetime library
import datetime
# Function to get the current date
def get_date():
# Step 1: Get today's date
today = datetime.date.today()
# Step 2: Return it
return today
```
**After (Cleaned):**
```python
import datetime
def get_date():
return datetime.date.today()
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