Run multi-model adversarial reviews with cross-critique and calibrated synthesis.
npx skills add https://github.com/makinux/adversarial-panel --skill adversarial-panel
Please help me install this Agent Skill. Skill: adversarial-panel Source: https://github.com/makinux/adversarial-panel/tree/main Command: npx skills add https://github.com/makinux/adversarial-panel --skill adversarial-panel
This Skill reduces the risk of confidently wrong answers by having multiple models independently review, challenge, and revise a response before it is synthesized. It is designed for high-stakes, contested, or verifiable questions where a single-pass answer may miss blind spots.
Ask the skill to run an adversarial review of your claim or decision and return the synthesized answer with disagreements, confidence, and falsification conditions.
High-intent search queries and answers about installing and using this skill.
To get a second opinion on architecture decisions or technical forecasts, run a multi-model adversarial review where independent panelists generate answers, cross-critique assumptions, and synthesize a calibrated conclusion.
Adversarial cross-critique is a process where multiple models attack weak claims and test assumptions by reproduction rather than assertion, improving root-cause analysis by falsifying unsupported hypotheses before final synthesis.
You need a red team review for research conclusions when dealing with high-stakes, contested, or verifiable questions where a single-pass answer risks missing critical blind spots and producing confidently wrong results.
You synthesize disagreements without averaging by applying a facilitated synthesis that combines live disagreements, confidence levels, and falsification conditions into one calibrated result, ensuring independent reasoning is preserved.
Yes, multi-model adversarial review works for confidence calibration by having models independently review and challenge responses, then synthesizing the agreements and disagreements into a calibrated result with explicit falsification conditions.
RyanDev1stReview engineering proposals through cross-model adversarial analysis and generate failure hypothesis reports.
leynosFormulate falsifiable debugging hypotheses and plan falsification tests for software issues.
shushuznEvaluate scientific claims by critiquing methodology, bias, and statistical reasoning.
nearaiQuery multiple LLMs in parallel and synthesize consensus-based answers.
