unslop-review
Has your agent rewrite code review comments to sound human. It cuts out formalities and gets straight to the point, indicating the issue and the fix.
What it does
This tool helps rewrite code review comments to sound more like a human teammate and less like a corporate robot. It removes unnecessary phrases and gets straight to the point: where the issue is, what the issue is, and how to fix it.
When to use it
- ✓When you want your code review comments to sound more natural.
- ✓To remove corporate jargon and excessive politeness from feedback.
- ✓To make comments direct, indicating the location, the problem, and the solution.
Example of what to ask your AI
“Make this code review comment sound more human: 'I noticed that perhaps we could consider adding a null check here, as it could potentially lead to issues in some scenarios.'”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool removes introductory phrases and excessive politeness. It identifies the exact code location, clearly describes the problem, and suggests a specific fix.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill unslop-reviewOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/unslop-review/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Unslop Review
When to Use
Use this skill when you need rewrites code review comments so they read like a human teammate wrote them. Cuts corporate-AI throat-clearing ("I noticed...", "I was wondering if perhaps...", "It might be worth considering..."). Each comment is direct: location, the issue, a concrete fix. Use when user says...
Purpose
Rewrite or generate PR review comments that sound like a teammate, not a politeness engine. Direct on the issue, concrete on the fix, kind on the human.
Trigger
/unslop-review, /review, "review this PR", "code review", "humanize review", "de-slop this comment", "make this feedback sound human". Auto-trigger when reviewing pull requests.
Format
Default shape: L<line>: <severity prefix> <observation>. <fix>.
Severity prefixes (optional but use them when severity matters):
bug:— code is broken or will breakrisk:— works today, fragile tomorrow (perf, race, missing test)nit:— style, naming, dead code, "while you're here"q:— genuine question, not a hidden complaint
Multi-file: <file>:L<line>: <severity> <observation>. <fix>.
Range: L88-140: ... when the issue spans lines.
Rules
Drop
- Throat-clearing: "I noticed that...", "It seems like...", "It looks like to me..."
- Stacked hedging: "I was wondering if perhaps we might want to potentially..."
- Polite-padding: "I would kindly suggest...", "just a small suggestion..."
- Per-comment praise: "Nice work on this function but...", "Great pattern, however..."
- Restating the diff: "Here on line 42 you have a function called
getUserwhich returns..." - Bare opinion without a fix: "This is bad" with no suggestion
Keep
- Exact line numbers and ranges
- Identifiers in backticks:
findUser,req.body.id - Concrete fix or concrete question
- "Why" only when the fix isn't obvious
Tone
Human, not corporate. "This throws if X" not "It may potentially be worth considering that this could throw under certain conditions." Calibrated uncertainty is fine ("I think", "probably") — performative softening is not.
Auto-clarity (use full prose, not one-liners)
- Security findings (CVE-class, auth, secrets)
- Architecture disagreements that need a real discussion
- Onboarding context for a new contributor
- When the answer is genuinely "this is fine"
In those cases use a short paragraph, then resume terse for the rest.
Examples
Bad → good
Bad:
I would kindly suggest that we might want to potentially consider adding a null check here as it could maybe lead to issues in some scenarios.Good:
L42: bug: \findUser` returns undefined when no match. Guard before `user.email` or early-return 404.`Bad:
Great work on this implementation! However, I think we could potentially enhance readability by considering a refactor of this function.Good:
L88-140: nit: this function does validation, I/O, and mapping. Splitting them would make the happy path easier to follow. Happy to pair on a cut if helpful.Bad:
I noticed that there's no retry logic here which could be problematic.Good:
L23: risk: no retry on 429. Wrap the call in \withBackoff(3)` so we don't drop legitimate requests.`Bad:
This implementation leverages a robust caching strategy.Good: (delete — empty praise. If the caching is genuinely interesting, explain why specifically.)
Approval
If the change is solid and you have nothing concrete: LGTM on its own line. No boilerplate.
Boundaries
- Comments only. No commits, no
git push, no auto-approve, no linter runs. - Output is paste-ready: one comment per line, or a clearly separated list.
- Severity must be honest. Don't downgrade a
bugto anitto soften the message.
Limitations
- Use this skill only when the task clearly matches its upstream source and local project context.
- Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
- Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
FAQ
What are 'severity prefixes'?
These are short labels like 'bug:', 'risk:', 'nit:', or 'q:' that indicate the seriousness of the issue (error, risk, style detail, or question).
What does the tool NOT do?
It does not make commits, auto-approve code, run linters, or replace tests or security reviews.
How does the tool handle praise?
It removes generic praise. If praise is genuinely important, it should be explained with specific details about why.
Details
Original description by the author
Rewrites code review comments so they read like a human teammate wrote them. Cuts corporate-AI throat-clearing (\"I noticed...\", \"I was wondering if perhaps...\", \"It might be worth considering...\"). Each comment is direct: location, the issue, a concrete fix.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4450
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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