What is the benefit of multi-model deliberation?

Multi-model deliberation reduces individual model bias and hallucination by requiring multiple AI perspectives to reach a consensus, ensuring higher reliability for code verification.

How does the skill handle versioning and reproducibility?

The skill utilizes snapshot pinning via Git commit SHAs to ensure that every verification result is tied to a specific state of the codebase for a clear audit trail.

Does it support specific security audits?

Yes, you can use the 'rubric_focus' parameter to specifically target Security, Performance, or Accessibility during the verification process.

Can I integrate this skill into my CI/CD pipeline?

Yes, the skill provides standardized exit codes (0 for PASS, 1 for FAIL, 2 for UNCLEAR) specifically designed for integration into automated build and deployment workflows.

LLM Council Verification

Name: LLM Council Verification
Author: amiable-dev

byamiable-dev

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보안 및 테스팅

Verifies code and documentation using multi-model deliberation to ensure high-confidence consensus on implementation quality.

소개

The LLM Council Verification skill enhances Claude Code with a sophisticated multi-model deliberation system for validating technical work. By moving beyond single-model assessments, it facilitates a 'council' of models to reach consensus on the correctness, security, and completeness of code changes or documentation. This skill is particularly useful for high-stakes environments where objective validation is required, providing machine-actionable JSON verdicts, confidence scores, and detailed audit trails that can be integrated directly into CI/CD pipelines or manual review workflows.

주요 기능

Multi-model consensus for objective validation
Targeted rubrics for Security, Performance, and Accessibility
Machine-actionable exit codes for CI/CD integration
1 GitHub stars
Structured JSON verdicts with confidence scoring
Snapshot-based reproducibility via Git commit SHAs

사용 사례

Automated quality gating in production deployment pipelines
Validating security-critical code changes before commit
Auditing technical documentation for completeness and accuracy

소개

주요 기능

Multi-model consensus for objective validation
Targeted rubrics for Security, Performance, and Accessibility
Machine-actionable exit codes for CI/CD integration
1 GitHub stars
Structured JSON verdicts with confidence scoring
Snapshot-based reproducibility via Git commit SHAs

사용 사례

Automated quality gating in production deployment pipelines
Validating security-critical code changes before commit
Auditing technical documentation for completeness and accuracy