Provide implementations of advanced data structures
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a5c-ai/babysitter1.9KUpdated Sep 17, 2026
FreePublished on GitHub
adversarial-review
Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.
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See how to installFiles included (2)
- SKILL.md1.4 KB
- README.md627 B
SKILL.md (original)
Review the instructions the agent will receive before you install.
- For final comprehensive cross-unit review
- When verifying spec compliance of any implementation
Key Differences from Collaborative Review
| Aspect | Collaborative | Adversarial |
|---|---|---|
| Goal | Help improve code | Verify spec compliance |
| Verdict | Suggestions | Binary PASS/FAIL |
| Evidence | Optional | Required (file:line) |
| Reviewer | Can be reused | Must be fresh |
| Context | Shared | Independent |
Fresh Reviewer Rule
On re-review after FAIL, a NEW reviewer instance spawns with no memory of the previous review. This prevents anchoring bias where a reviewer fixates on previously identified issues.
Anti-Patterns
- Reusing reviewers after FAIL
- Passing previous findings to new reviewers
- Providing subjective or advisory feedback
- Accepting partial compliance as PASS
Tool Use
Invoke as part of: methodologies/metaswarm/metaswarm-execution-loop (Phase 3)
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