Embed discipline rules into multi-agent pipeline phases with standardized metadata.
npx skills add https://github.com/vinhnxv/rune --skill discipline-vinhnxv
Please help me install this Agent Skill. Skill: discipline Source: https://github.com/vinhnxv/rune/tree/main/plugins/rune/skills/discipline Command: npx skills add https://github.com/vinhnxv/rune --skill discipline-vinhnxv
Discipline Engineering provides a verifiable, proof-based framework to ensure every task in a multi-agent system is decomposed, understood, verified, enforced, and accountable to the original specification.
Load the discipline SKILL to auto-load proof rules and enforce spec-compliance across agent tasks.
High-intent search queries and answers about installing and using this skill.
To enforce spec compliance in multi-agent pipelines, you need a proof-based discipline framework that embeds verification rules into every task phase. This ensures agents remain accountable to the original specification through machine-verifiable proofs.
Multi-agent verification is a process that ensures tasks are decomposed, understood, and verified against specifications. It works by applying a five-layer discipline model—Decomposition, Comprehension, Verification, Enforcement, and Accountability—across agent teams.
To prevent agents from rationalizing outputs, you apply anti-rationalization rules alongside spec-continuity checks during task decomposition. This enforces machine-verifiable proofs, holding every agent accountable to the original pipeline specification.
Yes, proof-based discipline orchestrates spec-compliant agent tasks across design, decomposition, verification, and enforcement phases. It embeds discipline rules into every stage to ensure standardized metadata outputs and pipeline accountability.
Limitations of applying discipline rules include the overhead of maintaining machine-verifiable proofs and spec-continuity across highly complex pipelines. It requires strict adherence to the five-layer model to prevent enforcement gaps.
Building spec-compliant multi-agent systems requires a framework that supports a five-layer discipline model and machine-verifiable proofs. This ensures tasks are properly decomposed, verified, and enforced against the original specification.
michael-conradEnforce design-before-code and verification-before-completion discipline in OpenCode tasks.
SatoryKonoExecute the py-debug-bot profile to validate BioETL workflow constraints.
davidmatousekLoad on-demand domain references for tachi orchestrator pipeline phases.
anotbAutomate review of adverse-action notices for regulatory compliance.
