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Code Commenting Strategy

Community
kitchen-engineer42
code-commenting-strategy

Apply strategic approaches to code commenting including when to comment, optimal comment density, performance considerations, and integration with development workflow. Use when establishing commenting standards, addressing performance concerns, implementing Pseudocode Programming Process (PPP), or determining appropriate comment frequency.

Overview

Publisherkitchen-engineer42
Repositorypdf2skills
Skill namecode-commenting-strategy
Stars
136
Forks
34
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Code Commenting Strategy 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/code-commenting-strategy
  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 Code Commenting Strategy 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 Code Commenting Strategy 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 Code Commenting Strategy 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.

Code Commenting Strategy

When to Use This Skill

  • Establishing or updating code commenting standards for a project
  • Addressing concerns about comment performance impact
  • Implementing Pseudocode Programming Process (PPP)
  • Determining optimal comment density for codebases
  • Training developers on commenting best practices

Core Principles

1. Integrate Comments with Development Workflow

Write comments synchronously with code, not as a separate task.

Why delay comments?

  • Perceived workload: Commenting at the end becomes a separate, overwhelming task
  • Time cost: Requires recalling or re-understanding code logic
  • Accuracy loss: Design assumptions and nuances are forgotten

Handling the "interruption" objection:

  • If commenting interrupts your coding flow, this is a warning sign of complexity
  • Use pseudocode for design first
  • Convert pseudocode to comments
  • Fill in the code implementation
  • High concentration needs indicate overly complex design—simplify first

2. Performance Considerations

For interpreted languages and resource-constrained environments:

  • Comments can add measurable performance overhead
  • Never skip comments to improve performance
  • Create separate build/release versions
  • Use build tools to strip comments during the build process

For compiled languages:

  • Comments are typically stripped automatically during compilation
  • No performance impact concern

3. Optimal Comment Density

Research-based guideline:

  • IBM research shows clarity peaks at approximately 1 comment per 10 statements
  • Below this density: Code becomes difficult to understand
  • Above this density: Also reduces code comprehensibility

Avoid arbitrary standards:

  • Don't use mandatory rules like "1 comment per 5 lines"
  • Arbitrary standards address symptoms, not root causes
  • Focus on code clarity, not comment quotas

4. Pseudocode Programming Process (PPP) Integration

When using PPP effectively:

  • You may naturally achieve high comment density (every few lines)
  • This is a side effect, not a goal
  • Focus on efficiency, not quantity

Evaluate each comment:

  • Does it describe why the code was written?
  • Does it meet established quality standards?
  • If yes, you have sufficient comments

Decision Flow

IF writing code
    THEN write comments synchronously
    IF concentration is too high for commenting
        THEN simplify design using pseudocode first

IF concerned about comment performance
    IF compiled language
        THEN no action needed (auto-stripped)
    IF interpreted language
        THEN implement build-time comment stripping

IF evaluating comment density
    THEN aim for ~1 comment per 10 statements
    Avoid arbitrary minimum quotas

IF using PPP
    THEN focus on comment efficiency (describing "why")
    Not on achieving specific quantity

Warning Signs

  • Coding requires such extreme concentration that commenting feels disruptive
  • Comment density significantly deviates from 1:10 ratio without justification
  • Comments are treated as a separate "cleanup" phase
  • Performance concerns lead to reduced commenting

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Code Commenting Strategy AI skill do?

Apply strategic approaches to code commenting including when to comment, optimal comment density, performance considerations, and integration with development workflow. Use when establishing commenting standards, addressing performance concerns, implementing Pseudocode Programming Process (PPP), or determining appropriate comment frequency.

Why use Code Commenting Strategy on TypingMind?

Because you install it once and use it with any model. Code Commenting Strategy 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 Code Commenting Strategy 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/code-commenting-strategy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Code Commenting Strategy?

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 Code Commenting Strategy?

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

Is the Code Commenting Strategy 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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