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Effective Commenting Strategies

Community
kitchen-engineer42
effective-commenting-strategies

Use this skill when writing, reviewing, or maintaining code comments. Apply these strategies to ensure comments explain intent rather than repeat code, use maintainable styles, and follow density guidelines. This skill covers comment types, style best practices, and performance considerations.

Overview

Publisherkitchen-engineer42
Repositorypdf2skills
Skill nameeffective-commenting-strategies
Stars
136
Forks
34
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kitchen-engineer42 on GitHub. Read the source before you install it.

Installation

Install the Effective Commenting Strategies AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kitchen-engineer42/pdf2skills/tree/main/skills_samples/Steve%20McConnell%20Code%20Complete%20A%20Practical%20Handbook%20of%20Software%20Construction_output/effective-commenting-strategies
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Effective Commenting Strategies in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Effective Commenting Strategies on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Effective Commenting Strategies is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Effective Commenting Strategies

Good comments explain the "why" behind code, not the "what." Use these strategies to write comments that enhance code maintainability without creating maintenance burden.

When to Use

  • Adding comments to new code
  • Reviewing existing comments for quality
  • Establishing team commenting standards
  • Optimizing comment density for readability
  • Handling performance-sensitive code with comments

Core Principle: Intent Over Repetition

Good comments should:

  • Not repeat code or explain what the code does
  • Clarify intent at a higher abstraction level than the code
  • Act as navigation like book headings or table of contents
  • Help maintenance by explaining the original programmer's intent
  • **Be efficient"—reading one English comment is faster than parsing 20 lines of code

Rule: If the code already explains everything, a comment that repeats it provides no value.

Comment Types

Marker Comments

Purpose: Remind developers of incomplete work (not intended for production)

Requirements:

  • Use standardized markers (e.g., ***, TBD, TODO)
  • Make markers mechanically searchable
  • Include in release checklist to prevent shipping known defects

Action: Search for all markers before release to ensure no incomplete code ships.

Explanatory Comments

Purpose: Explain complex, tricky, or sensitive code

Strategy:

  • If code is complex enough to need explanation, refactor the code first
  • Make the code itself clearer
  • Then use summary or intent comments if still needed

Summary Comments

Purpose: Condense several lines of code into one or two sentences

Value: More valuable than code-repeating comments because readers can scan them faster than reading code

When to use: Particularly helpful when non-original authors need to modify code

Intent Comments

Purpose: Explain the purpose of a code section at the intent/problem level

Level: Operates more at the problem level than the solution level

Example:

  • Intent: "Get current employee information"
  • Solution/Summary: "Update employeeRecord object"

Metadata Comments

Purpose: Record information that cannot be expressed in code

Includes:

  • Copyright notices
  • Confidentiality statements
  • Version numbers
  • Design notes
  • Requirement references
  • Online reference pointers
  • Optimization notes
  • Tool-required comments (e.g., Javadoc)

Maintainable Comment Style

Avoid High-Maintenance Styles

Do not use styles that require manual alignment:

  • ❌ Leader dots (...) connecting variables and descriptions
  • ❌ Asterisk boxes (*) surrounding paragraphs—requires adjusting both sides
  • ❌ Plus-sign underlines (+---+)—requires precise positioning when text length changes

Principle: Prefer accurate comments over pretty comments. If maintaining aesthetics requires tedious work, abandon that style.

Syntax Guidelines

For Java and C++:

  • Single-line comments: Use // syntax, keep comments short
  • Multi-line comments: Use /* ... */ syntax—easier to maintain than manually wrapped // lines

For emphasis: Use standard-length lines (via editor macros), not lines that vary with comment length

Comment Density Guidelines

Optimal Density

IBM Research Finding: Approximately 1 comment per 10 statements maximizes code clarity

  • Below this density: Code becomes difficult to understand
  • Above this density: Readability decreases

Avoid Quotas

Do not enforce rigid standards like "at least 1 comment per 5 lines"

Reason: This addresses the symptom (lack of comments) without solving the root cause (unclear code)

Performance Considerations

Principle: Do not avoid comments due to performance concerns (e.g., interpreted environments, network transmission overhead)

Solution: Create a separate release build

Implementation: Run a tool during the build process to automatically strip comments from the production version

Pseudocode Programming Process (PPP) Comment Efficiency

When using the Pseudocode Programming Process:

  1. Recognize the pattern: Effective PPP naturally results in "a comment every few lines"
  2. Understand the nature: Comment quantity is a side effect of the process, not a goal
  3. Focus on efficiency: Don't count comments—evaluate whether each comment is effective
  4. Assess sufficiency: Comments are sufficient if they:
    • Describe why the code was written
    • Meet the other standards in this skill

Conclusion: If comments explain "why" and meet quality standards, the quantity is adequate—don't worry about the number.

Frequently asked questions

What does the Effective Commenting Strategies AI skill do?

Use this skill when writing, reviewing, or maintaining code comments. Apply these strategies to ensure comments explain intent rather than repeat code, use maintainable styles, and follow density guidelines. This skill covers comment types, style best practices, and performance considerations.

Why use Effective Commenting Strategies on TypingMind?

Because you install it once and use it with any model. Effective Commenting Strategies is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Effective Commenting Strategies in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kitchen-engineer42/pdf2skills/tree/main/skills_samples/Steve%20McConnell%20Code%20Complete%20A%20Practical%20Handbook%20of%20Software%20Construction_output/effective-commenting-strategies. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Effective Commenting Strategies?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Effective Commenting Strategies?

As many as you like. As long as a model supports skills, you can use Effective Commenting Strategies with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Effective Commenting Strategies AI skill free?

It is published on GitHub by kitchen-engineer42. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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