Skip to content
All Skills

Unslop Review

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 "humanize review", "de-slop PR comment", "make this feedback sound human", "review this PR", "code review", "/review", "/unslop-review". Auto-triggers when reviewing PRs.

Software Engineering|v1|Updated 7/14/2026|GitHub source
MCP get_skill({ skillId: "unslop-review-9fd64f7d" })

Use this skill with your agent

Create a free account and connect via MCP

Get Started Free
# Unslop Review

## 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 break
- `risk:` — 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 `getUser` which 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 `bug` to a `nit` to soften the message.
#broad-capability#content-quality#code#review

Related Skills

More skills in Software Engineering

Accessibility Standards

Comprehensive web accessibility standards based on WCAG 2.2 AA, with 38+ anti-patterns, legal enforcement context (EAA, ADA Title II), WAI-ARIA patterns, and framework-specific fixes for modern web frameworks and libraries.

#github-copilot#accessibilityMIT

Accord

Authoring unified specification packages across Business/Development/Design teams via staged elaboration (L0 Vision → L1 Requirements → L2 Team Detail → L3 Acceptance Criteria). No code. Use when authoring cross-team specs, building L0-L3 packages, or aligning Biz/Dev/Design on a single source of truth.

#broad-capability#developmentMIT

Acquire Codebase Knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

#github-copilot#documentationMIT

Acreadiness Assess

Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.

#github-copilot#planningMIT

Acreadiness Generate Instructions

Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.

#github-copilot#skillMIT

Acreadiness Policy

Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.

#github-copilot#planningMIT