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Comment Quality

Community
notque
comment-quality

Review and fix temporal references in code comments.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namecomment-quality
Stars
443
Forks
50
Bundled files
8
LicenseMIT
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.

  • 8 bundled files

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

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Comment Quality 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/notque/vexjoy-agent/tree/main/skills/code-quality/comment-quality
  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.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/code-quality/comment-quality .claude/skills/comment-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Comment Quality 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 Comment Quality 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 Comment Quality 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.

Comment Quality Skill

Review code comments for temporal references, development-activity language, and relative comparisons. Produces structured reports with actionable rewrites that explain WHAT the code does and WHY, only WHAT the code does and WHY. Supports .go, .py, .js, .ts, .md, and .txt files.

Instructions

Phase 1: SCAN

Goal: Identify all comments containing temporal, activity, or relative language.

Step 1: Determine scope

Read the repository CLAUDE.md first to pick up any project-specific comment conventions.

Scan only what was requested. If user specifies files, scan those files. If user specifies a directory, scan that directory. Honor the explicit scope -- even if you suspect other files have issues, honor the explicit scope and suggest expansion separately at the end.

If user explicitly requests auto-fix, enable it. Otherwise present findings for review. For large codebases, group findings by directory when reporting.

Step 2: Search for temporal patterns

Flag every instance of the following categories. No temporal word is "harmless" -- all temporal language ages poorly and must be rewritten regardless of how innocuous it seems:

  • Temporal words: "new", "old", "previous", "current", "now", "recently", "latest", "modern"
  • Development activity: "added", "removed", "deleted", "updated", "changed", "modified", "fixed", "improved", "enhanced", "refactored", "optimized"
  • State transitions: "replaced", "migrated", "upgraded", "deprecated", "became", "turned into", "evolved"
  • Date references: "as of", "since", "from", "after", "before"
  • Relative comparisons: "better than", "faster than", "instead of", "unlike the previous"

Step 3: Filter false positives

Exclude from findings -- these are not developer comments and must remain untouched:

  • Copyright and license headers (legal requirements, not code comments)
  • @generated markers (tooling markers)
  • @deprecated annotations (keep the tag, flag only temporal explanation text after it)
  • Variable names or string literals that happen to contain temporal words
  • TODO/FIXME items that describe future work without temporal references

When a finding appears, inspect nearby comments in the same function or block -- temporal language tends to cluster.

Gate: All files in scope scanned. Findings list populated with file path, line number, and matched text. Every finding listed, not just the first few. Proceed only when gate passes.

Phase 2: ANALYZE

Goal: Understand context for each finding to produce meaningful rewrites.

Step 1: Read surrounding code

For each finding, read the function, block, or section the comment describes. Understand what the code actually does. A rewrite without code context produces vague replacements that strip temporal words without adding substance.

Step 2: Classify the comment

markdown
| Finding | Type | Severity |
|---------|------|----------|
| "now uses JWT" | Temporal + Activity | High |
| "improved perf" | Activity | Medium |
| "Copyright 2024" | Legal (skip) | N/A |

Step 3: Determine replacement content

For each comment, identify:

  1. What does the code do right now?
  2. Why does it do it this way?
  3. What value does the comment add for a future reader?

Gate: Every finding classified with context understood. Proceed only when gate passes.

Phase 3: REWRITE

Goal: Generate specific, valuable replacement comments.

Step 1: Draft rewrites

For each finding, produce a structured entry with file path, line number, current text, suggested replacement, and reasoning:

markdown
**File: `path/to/file.ext`**

Line X - [Comment type]:
  Current:   // Authentication now uses JWT tokens
  Suggested: // Authenticates requests using signed JWT tokens
  Reason:    "now uses" is temporal - describe current behavior only

Step 2: Validate rewrite quality

Each rewrite MUST pass these checks:

  • Would this comment make sense in 10 years?
  • Does it explain WHAT or WHY, not WHEN?
  • Is it more specific than what it replaces (not just temporal word removed)?
  • Does it add value for a future maintainer?

If a rewrite just removes the temporal word without adding substance, it fails validation. Simply deleting a word produces a useless comment -- // Updated error handling becoming // Error handling adds nothing. Rewrite with specific, descriptive content: // Handles database connection errors with exponential backoff retry.

Gate: All rewrites pass quality checks. No vague or empty replacements. Proceed only when gate passes.

Phase 4: REPORT

Goal: Deliver structured, actionable report.

Step 1: Generate report

Report facts concisely with file paths and line numbers. Every finding must include the current text, suggested replacement, and reasoning -- a diagnostic-only count without rewrites creates work without providing solutions.

markdown
## Comment Quality Review

### Summary
- Files scanned: N
- Issues found: M
- Most common pattern: [temporal word]

### Findings
[All findings with file, line, current text, suggested text, reason]

### Recommendations
1. Apply suggested changes
2. Consider adding linter rules for temporal language prevention

Step 2: Apply fixes (if auto-fix enabled)

If user requested auto-fix, apply all rewrites using Edit tool. Verify each edit succeeded. Wait for explicit user permission before auto-fixing without explicit user authorization.

Step 3: Cleanup

Remove any scan results, intermediate reports, or helper files created during execution.

Gate: Report delivered. All findings accounted for. Task complete.

Error Handling

Error: "No Temporal Language Found"

Cause: Files are clean or scope was too narrow Solution:

  1. Verify common files were scanned (README, main source files)
  2. Report clean results -- this is a valid positive outcome
  3. Suggest expanding scope if user suspects issues exist elsewhere

Error: "Too Many Results to Display"

Cause: Large codebase with widespread temporal language Solution:

  1. Prioritize by file importance (README first, then core modules)
  2. Group findings by pattern type
  3. Process files by directory with grouped reports

Error: "Comment Meaning Unclear Without History"

Cause: Comment only makes sense with development context that no longer exists Solution:

  1. Read surrounding code to infer current purpose
  2. If purpose is clear from code, suggest removing the comment entirely
  3. If purpose is unclear, ask user for clarification before rewriting

Deep References

Load on demand when the current task matches the signal.

SignalReferenceContent
Go files in scopereferences/go-comment-patterns.mdGo-specific temporal patterns with detection commands
Error handling codereferences/error-handling-patterns.mdTemporal patterns in try/catch, if err !=, raise
Performance codereferences/performance-patterns.mdTemporal patterns in cache, pool, concurrency comments
Rewrite examples neededreferences/examples.mdBefore/after examples of comment rewrites
Full failure mode referencereferences/preferred-patterns.mdCore temporal failure mode catalog (language-agnostic)
Language-specific or doc commentsreferences/preferred-patterns-language-specific.mdGo, Python, JS/TS, README, API failure modes

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 Comment Quality AI skill do?

Review and fix temporal references in code comments.

Why use Comment Quality on TypingMind?

Because you install it once and use it with any model. Comment Quality 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 Comment Quality in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/code-quality/comment-quality. 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 Comment Quality?

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 Comment Quality?

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

Is the Comment Quality AI skill free?

Yes. It is published on GitHub by notque under the MIT license. 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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