adversarial-iteration

adversarial-iteration is a Multi-agent review & consensus skill for AI agents, published by boxlite-ai in the boxlite repository on GitHub. Skillspace rates adversarial-iteration D (61/100) and it passed the safety scan; the rating is based on static analysis only.

Use when iterating on a fix after an adversarial code review (Codex, human reviewer) surfaces design-level findings — not typos, lint, or formatter work. Frames the review/fix loop as implement -> [review -> REFLECT -> reproducer -> fix -> verify -> handoff] where each round runs in a fresh agent to prevent context accumulation. Provides a four-item cheap-checks list (timeline draw, upstream contract read, full lifecycle trace, variant enumeration) that catches recurring failure classes. No git commits during the loop — all changes commit together after zero-finding termination. Trigger when a review pass surfaces 2+ findings on a single change, when sibling bugs of an "already-fixed" issue keep appearing across rounds, or when the user asks "why did I miss this?". Do NOT use for single-line fixes where the cause is obvious from the diff, or for initial implementation work (use CLAUDE.md rules).

Grade

D

Quality

61 / 100

Safety

Passed

GitHub stars

2,186

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

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.