Agent skill

Comment Quality

by notque in notque/vexjoy-agent

Review and fix temporal references in code comments. An agent skill from notque/vexjoy-agent.

MITAuto-check: notesDevelopment

Install Comment Quality

skills CLI
$ npx skills add notque/vexjoy-agent --skill comment-quality -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install notque/vexjoy-agent comment-quality --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/notque/vexjoy-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/code-quality/comment-quality .claude/skills/comment-quality && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
comment-quality
GitHub stars
435
Token cost
~2k tokens
SKILL.md length
887 words
Files
9 (incl. scripts, references)
Skills in repo
61
Repo updated
First seen
Licence
MIT

At a glance

Review and fix temporal references in code comments. An agent skill from notque/vexjoy-agent.

  • Works in 4 steps: SCAN → ANALYZE → REWRITE → …
  • Tasks that involve Technical documentation
  • SKILL.md covers Instructions, Error Handling and Deep References
  • Runs Python scripts from its folder

What it does

Comment Quality is an agent skill from notque/vexjoy-agent. Review and fix temporal references in code comments.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/error-handling-patterns.md`, `references/examples.md` and `references/go-comment-patterns.md`).

It sits in Development, covering Technical documentation. The repository describes itself as: VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop. The licence is MIT.

When your agent uses it

  • Tasks that involve Technical documentation

Example prompts

  • “/comment-quality”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Bash, Grep, Glob, Edit, Task

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. SCAN
  2. ANALYZE
  3. REWRITE
  4. REPORT

What it can do on your machine

Read from SKILL.md and the folder at commit 5218674. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Bash
    • Grep
    • Glob
    • Edit
    • Task

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Comment Quality loads about 2k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 17 tokens; SKILL.md has 887 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~17
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Bash, Grep, Glob, Edit, Task

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from notque/vexjoy-agent at commit 5218674, republished under its MIT licence (© notque). 887 words, ~1,970 tokens.

Download SKILL.mdSave it as .claude/skills/comment-quality/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
comment-quality
description
Review and fix temporal references in code comments.
allowed-tools
Read, Write, Bash, Grep, Glob, Edit, Task
user-invocable
false
routing.triggers
review comments, fix temporal references, comment quality, stale comments, outdated comment
routing.category
code-quality
routing.pairs_with
code-quality, review

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.

Show full SKILL.md (312 more words)Show less
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

© notque, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in skills/code-quality/comment-quality of notque/vexjoy-agent.

  • SKILL.md
  • references/error-handling-patterns.md
  • references/examples.md
  • references/go-comment-patterns.md
  • references/performance-patterns.md
  • references/preferred-patterns-language-specific.md
  • references/preferred-patterns.md
  • references/temporal-keywords.txt
  • scripts/validate.py

Open the folder on GitHubat commit 5218674

Compare with similar skills

Comment Quality next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Comment Quality compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Comment Quality this skillnotque/vexjoy-agent435—~2kAutomated safety check: NotesMIT
Diagram Designcathrynlavery/diagram-design44k1 repos~7.5kAutomated safety check: PassMIT
Simple Englishmoeru-ai/airi50k2 repos~4.6kAutomated safety check: PassMIT
Get API Docs with chubandrewyng/context-hub14k2 repos~775Automated safety check: PassMIT
Doc SyncJetBrains/ideavim10k2 repos~2.6kAutomated safety check: PassMIT
Mailspring App ScreenshotsFoundry376/Mailspring18k—~1.4kAutomated safety check: PassGPL-3.0

Similar skills

  • Diagram Design

    cathrynlavery/diagram-design

    Creates branded diagrams, from architecture, flowchart and sequence to charts and maps, as self-contained HTML with inline SVG, with import from draw.io, Mermaid and Excalidraw.

    44k GitHub starsUsed in 1 repo~7.5k tokens
    DevelopmentAuto-check passed
  • Simple English

    moeru-ai/airi

    Write or rewrite technical text with the rules of ASD-STE100 Simplified Technical English so it is clear, unambiguous, and free of AI slop.

    50k GitHub starsUsed in 2 repos~4.6k tokens
    DevelopmentAuto-check passed
  • Get API Docs with chub

    andrewyng/context-hub

    Fetches current documentation for third-party APIs and SDKs with the chub CLI before the agent writes code against them, instead of relying on remembered API shapes.

    14k GitHub starsUsed in 2 repos~775 tokens
    DevelopmentAuto-check passed
  • Doc Sync

    JetBrains/ideavim

    Official

    Keeps IdeaVim documentation in sync with code changes. An agent skill from JetBrains/ideavim.

    10k GitHub starsUsed in 2 repos~2.6k tokens
    DevelopmentAuto-check passed
  • Mailspring App Screenshots

    Foundry376/Mailspring

    Captures screenshots of the running Mailspring dev app for docs, PRs or visual checks by launching it with a debugging port, driving the UI and clipping to an element.

    18k GitHub stars~1.4k tokensUpdated today
    DevelopmentAuto-check passed
  • Draw.io Diagram Studio

    Agents365-ai/drawio-skill

    Creates and edits editable draw.io diagrams from descriptions, code, infrastructure files, SQL and API schemas, with sync, review, test and export tools.

    10k GitHub stars~2.4k tokensUpdated 5 days ago
    DevelopmentAuto-check: notes

More from notque/vexjoy-agent

All 61 skills in this repo
  • Game Asset Generator

    notque/vexjoy-agent

    Deterministic palette/matrix pixel art (not AI). An agent skill from notque/vexjoy-agent.

    435 GitHub stars~2.3k tokensUpdated 4 days ago
    Auto-check: notes
  • PR Workflow

    notque/vexjoy-agent

    Pull request lifecycle: commit, codex review, sync, review, fix, status, cleanup, and PR mining.

    435 GitHub stars~2.8k tokensUpdated 4 days ago
    Auto-check: notes
  • Architecture Deepening

    notque/vexjoy-agent

    Improve architecture across modules by deepening interfaces.

    435 GitHub stars~3.3k tokensUpdated 4 days ago
    Auto-check: notes
  • Code Quality

    notque/vexjoy-agent

    Code quality: cleanup, linting, formatting, quality gates. An agent skill from notque/vexjoy-agent.

    435 GitHub stars~1.5k tokensUpdated 4 days ago
    Auto-check: notes
  • Codebase Analyzer

    notque/vexjoy-agent

    Statistical rule discovery from Go codebase patterns. An agent skill from notque/vexjoy-agent.

    435 GitHub stars~2k tokensUpdated 4 days ago
    Auto-check: notes
  • Content

    notque/vexjoy-agent

    Content operations: editorial calendar, marketing, publishing, social media management.

    435 GitHub stars~2.9k tokensUpdated 4 days ago
    Auto-check: notes

Categories

Questions about Comment Quality

What does Comment Quality do?

Review and fix temporal references in code comments. An agent skill from notque/vexjoy-agent. Comment Quality is an agent skill from notque/vexjoy-agent. Review and fix temporal references in code comments.

When should I use Comment Quality?

Comment Quality fits situations like: tasks that involve Technical documentation.

How do I install Comment Quality in Claude Code?

Run `npx skills add notque/vexjoy-agent --skill comment-quality -a claude-code`. Or copy the skill folder (skills/code-quality/comment-quality in notque/vexjoy-agent) into .claude/skills/comment-quality in your project. Claude Code loads it when a task matches its description.

How do I install Comment Quality in Codex?

Run `npx skills add notque/vexjoy-agent --skill comment-quality -a codex`. Or copy the skill folder (skills/code-quality/comment-quality in notque/vexjoy-agent) into .agents/skills/comment-quality in your project. Codex loads it when a task matches its description.

Can I use Comment Quality in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add notque/vexjoy-agent --skill comment-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/comment-quality, .gemini/skills/comment-quality, .github/skills/comment-quality and .opencode/skills/comment-quality in your project.

What does Comment Quality need to run?

Going by SKILL.md and its folder, Comment Quality needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Bash, Grep, Glob, Edit, Task.

Does Comment Quality access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Comment Quality safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Comment Quality use?

Comment Quality is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Comment Quality use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Comment Quality?

Skills that share tags, products or a category with Comment Quality: Diagram Design (cathrynlavery/diagram-design, 44k stars), Simple English (moeru-ai/airi, 50k stars), Get API Docs with chub (andrewyng/context-hub, 14k stars) and Doc Sync (JetBrains/ideavim, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Comment Quality?

notque (a GitHub user) maintains it in notque/vexjoy-agent, which has 435 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on October 3, 2026.

Source: notque/vexjoy-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.