Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mohitmishra786/anti-vibe-skills --skill code-review-challengergit clone --depth 1 https://github.com/mohitmishra786/anti-vibe-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mohitmishra786/anti-vibe-skills/code-review-challenger)<a href="https://agentmods.dev/skills/mohitmishra786/anti-vibe-skills/code-review-challenger"><img src="https://agentmods.dev/badge/skills/mohitmishra786/anti-vibe-skills/code-review-challenger/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mohitmishra786/anti-vibe-skills/code-review-challenger"><img src="https://agentmods.dev/badge/skills/mohitmishra786/anti-vibe-skills/code-review-challenger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.01247 |
| Opus 5 | $0.00037 | $0.00624 |
| Sonnet 5 | $0.00015 | $0.00249 |
| Haiku 4.5 | $0.00007 | $0.00125 |
Grade A, and why
code-review-challenger scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Asks | Purpose |
|---|---|
| "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 Asks | Purpose |
|---|---|
| "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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 120 lines · 74 tokens per session scan A 096027080971
code-review-challenger is a skill published in the GitHub repository mohitmishra786/anti-vibe-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 74 tokens to every session and 1,247 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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