Automate multi-agent adversarial verification for bug finding and code review.
npx skills add https://github.com/aphamm/.dotfiles --skill tribunal-aphamm
Please help me install this Agent Skill. Skill: tribunal Source: https://github.com/aphamm/.dotfiles/tree/main/agents/skills/tribunal Command: npx skills add https://github.com/aphamm/.dotfiles --skill tribunal-aphamm
This Skill provides a rigorous, adversarial verification system for thorough analysis, identifying and verifying issues in code, documents, or proposals.
Run the tribunal skill on your codebase to automatically detect and verify issues.
High-intent search queries and answers about installing and using this skill.
Adversarial analysis automates a multi-agent verification process for code review and security audits by utilizing specialized agents to ensure exhaustive recall, precise pruning, and calibrated judgment. This systematically identifies and verifies bugs or vulnerabilities in your target scope.
The best way to find logical flaws and bugs is running an automated adversarial verification process on your codebase. This approach uses specialized agents for recall, precision, and calibration to systematically detect and verify issues.
To set up a security audit, you must provide a defined target scope and domain-specific context. The adversarial verification process then uses this input to systematically search for and verify security vulnerabilities.
Yes, you can use this for critical document analysis and proposal verification. The adversarial collaboration process is designed to rigorously identify and verify issues across documents, proposals, and code.
Yes, effective code review requires domain-specific context along with a defined target scope. Providing this context allows the specialized agents to accurately calibrate judgments and verify potential vulnerabilities.
dtsongTransform natural language project descriptions into structured engagement briefs.
unnamedmistressFix code defects and security vulnerabilities from QA audits with minimal patches.
bahayonghangSearch and validate code patterns using Tree-sitter structural queries.
dreadnodeFilter low-quality and false positive vulnerability findings from scanners and agent output.
