adversarial-reviewer

adversarial-reviewer is a Multi-agent review & consensus skill for AI agents, published by itsmeadarsh2008 in the backlit-kbd repository on GitHub. Skillspace rates adversarial-reviewer D (63/100) and it passed the safety scan; the rating is based on static analysis only.

A self-review skill that attacks Mirage's own output from four adversarial perspectives — security researcher, failure analyst, scale engineer, and future maintainer — before presenting it to the developer. Also enforces explicit uncertainty quantification on every claim: distinguishing what is verified, inferred, or hypothetical. ALWAYS activate after any implementation of: authentication, payment logic, data mutations, file handling, external API calls, or any feature the developer has flagged as high-stakes. Also activate when the developer asks "is this safe?", "will this scale?", "what could go wrong?", or "are you sure about this?" This skill is the difference between Mirage presenting its first draft and presenting a production-ready implementation that has already been internally stress-tested.

Grade

D

Quality

63 / 100

Safety

Passed

GitHub stars

27

Unproven: rated by static analysis only, not yet compared head-to-head.

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