Babysitter adversarial-review
Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/methodologies/metaswarm/skills/adversarial-review" ~/.claude/skills/a5c-ai-babysitter-adversarial-review && rm -rf "$T"
manifest:
library/methodologies/metaswarm/skills/adversarial-review/SKILL.mdsource content
Adversarial Review
Overview
Independent adversarial code review checking spec compliance. Uses binary PASS/FAIL verdicts (not subjective feedback) with required file:line evidence citations.
When to Use
- After quality gates pass in the execution loop
- 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