Extract commitment kernels and score semantic preservation with Jaccard similarity.
npx skills add https://github.com/SunrisesIllNeverSee/moses-claw-gov --skill coverify
Please help me install this Agent Skill. Skill: coverify Source: https://github.com/SunrisesIllNeverSee/moses-claw-gov/tree/main/skills/coverify Command: npx skills add https://github.com/SunrisesIllNeverSee/moses-claw-gov --skill coverify
Semantic commitment can drift or be leaked when signals are transformed; CoVerify provides a lightweight instrument to extract and compare the commitment kernel.
Run commitment_verify.py extract "Agents must always verify lineage before executing." to extract the commitment kernel from a sample signal.
High-intent search queries and answers about installing and using this skill.
To verify semantic commitment preservation, you extract an irreducible commitment kernel from input text and score its Jaccard similarity against the transformed output to detect intent drift or leakage.
Ghost-token analysis identifies cascade-risk leakage patterns and cross-model structural holes to validate governance and model swap workflows where preserving original intent is critical.
You audit model swaps by running cross-model comparisons and maintaining an input-output ledger to detect semantic commitment loss, structural holes, or transformation leakage across pipelines.
You need commitment kernel extraction when validating governance workflows, model swaps, or transformation pipelines to quantify exactly how much semantic intent is preserved or lost.
Yes, Jaccard similarity quantifies the preservation of semantic commitment across cross-model comparisons, directly highlighting structural holes and cascade-risk leakage patterns in audit workflows.
Commitment kernel auditing focuses strictly on quantifying semantic preservation via Jaccard similarity, meaning it detects drift and leakage but does not automatically correct transformation pipeline failures.
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