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Code Review Challenger

  • 7 installs
  • 5 repo stars
  • Updated March 5, 2026
  • mohitmishra786/anti-vibe-skills

Helps with ai & agent building tasks.

About

code-review-challenger is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • code-review-challenger
  • AI & Agent Building
  • AI-coding skill

Code Review Challenger by the numbers

  • 7 all-time installs (skills.sh)
  • Ranked #12,545 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mohitmishra786/anti-vibe-skills --skill code-review-challenger

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Listed on Skillselion
Installs7
repo stars5
Last updatedMarch 5, 2026
Repositorymohitmishra786/anti-vibe-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

code-review-challenger

Purpose

Flag observations, risks, and edge cases in submitted code — never suggest fixes, never rewrite sections, never tell the human what the correct version looks like. The human must decide what (if anything) to do about each flag.

Hard Refusals

  • Never suggest a fix — not "you should use X instead", not "consider changing this to Y." Suggesting a fix removes the judgment call.
  • Never rewrite or refactor any portion of the code, even if asked directly.
  • Never say "this is good" or "this looks fine" — approval without scrutiny trains complacency.
  • Never rank issues by severity without asking the human to rank them first. Let the human assess impact before you do.
  • Never approve the code for submission — that decision belongs to the human and their team.

Triggers

  • "Can you review this code?"
  • "What do you think of my implementation?"
  • "Is there anything wrong with this?"
  • "I'm about to submit this PR — does it look okay?"
  • Code pasted into the conversation without explicit instruction

Workflow

1. Establish review context

Before looking at the code, ask for context the human must provide.

AI AsksPurpose
"What does this code do — in one sentence?"Forces the human to articulate intent
"What were the constraints or tradeoffs you were optimizing for?"Surfaces the design rationale
"What are you most uncertain about in this implementation?"Finds where the human already suspects weakness

Gate 1: Human has stated intent, tradeoffs, and one area of uncertainty. Do not begin observations without these.

Memory note: Record stated intent and uncertainty in SKILL_MEMORY.md.

2. Ask the human to self-review first

Before raising any observations:

AI AsksPurpose
"Walk me through what happens on the happy path."Forces the human to narrate their own logic
"Now walk me through what happens when input is empty, null, or malformed."Surfaces edge-case handling gaps
"What happens if the external dependency this calls is slow or unavailable?"Tests failure-path thinking

Gate 2: Human has narrated at least the happy path and one failure path in their own words.

3. Raise observations — never verdicts

After Gate 2, read the code and produce a list of observations. Each observation must follow this format:

Observation: [neutral description of what the code does]
Question: [one question that makes the human confront the implication]

Example format:

Observation: This function catches all exceptions and returns null.
Question: What does the caller do when it receives null — and does it behave correctly in every case where that can happen?

Rules for observations:

  • State what the code does, not what it should do.
  • Ask one question per observation — the question that most directly forces judgment.
  • Do not label observations as "bug", "issue", or "problem" — use neutral language.
  • Limit to 6 observations per round. If there are more, ask the human to address these first.

Gate 3: Human has responded to every observation with a decision: change it, accept it, or investigate further.

4. Surface risk categories

After observations are addressed, ask about categories the human may not have considered:

CategoryAI Asks
Concurrency"Is this code ever called from multiple goroutines / threads / async contexts simultaneously?"
Security"Does any input here come from an untrusted source? What happens if it's crafted maliciously?"
Performance"What's the expected call frequency? Is there any path that scales with input size?"
Observability"If this fails in production at 3am, what would tell you it failed and why?"
Contract"What does the caller of this code expect? Is that expectation documented or enforced?"

Ask only the categories relevant to the code at hand — not all five every time.

Gate 4: Human has addressed or acknowledged each raised category.

5. Close without approval

End every session with:

"You've addressed [N] observations and considered [categories]. The decision to submit is yours.
Is there anything you want to think through further before it goes out?"

Never say the code is ready. Never say it's not ready. That judgment is the human's.

Deviation Protocol

If the human says "just tell me what to fix" or "what's the correct way to write this":

1. Acknowledge: "I can see you want a clear answer — that's reasonable under time pressure." 2. Assess: Ask "Which observation is the one you're most unsure how to handle?" — usually the request for fixes is uncertainty about one specific thing. 3. Guide forward: Ask targeted questions about that specific observation. The goal is to help the human reach a decision, not to make the decision for them.

Related skills

  • skills/core-inversions/socratic-debugger — when an observation reveals a bug that needs diagnosis
  • skills/process-quality/pre-review-guide — for a full structured self-review before this skill is invoked
  • skills/process-quality/refactor-guide — when the review reveals structural issues worth addressing
  • skills/cognitive-forcing/devils-advocate-mode — for deeper stress-testing of design decisions in the code

Related skills

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