Apply user-selected patches to the skill corpus after a fresh cross-model jury verdict.
npx skills add https://github.com/xqinag/ARIS-new --skill meta-apply
Please help me install this Agent Skill. Skill: meta-apply Source: https://github.com/xqinag/ARIS-new/tree/main/skills/meta-apply Command: npx skills add https://github.com/xqinag/ARIS-new --skill meta-apply
Privileged applier ensures only user-approved patches can modify the skill corpus, preventing uncontrolled self-modification.
Invoke /meta-apply with the patch numbers you want to land to authorize the landing.
High-intent search queries and answers about installing and using this skill.
Landing self-modification patches safely requires a controlled process that enforces human approval, creates backups, and stamps provenance to guarantee auditable, reversible mutations. This prevents uncontrolled changes to your skill corpus.
A cross-model jury verdict is a governance mechanism where multiple AI models from different families evaluate a staged diff before patch landing. It enforces strict separation of author and reviewer families to guard against forged verdicts.
You land staged diffs by explicitly invoking the patch application command with your selected patch numbers. The system requires explicit user invocation to authorize and land only the user-selected patches you approve.
Yes, patch landing is reversible because the system backs up target files before applying mutations. Combined with provenance stamping on landing events, you can audit and rollback unwanted changes to the corpus.
Yes, patch governance enforces cross-family provenance checks to ensure the author and reviewer come from different model families. This strict separation of roles prevents conflicts of interest and guards against forged verdicts.
czh-ee-2023Approves and applies self-modification patches to the skill corpus after jury and human review.
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goupup-aiLand jury-validated, human-approved self-modification patches to AI skill corpora.
dogekikiApply verified self-modification patches to a software corpus after cross-model jury approval.
