SignalLayerLabs/Marginal
MARGINAL is an open-source runtime governor for AI coding agents that observes their work, detects repetitive actions with no progress, and earns limited authority to stop them based on evidence.
MARGINAL is designed to prevent AI agents from repeating work that changes nothing. It starts in Shadow Mode and only earns the right to enforce restrictions after collecting sufficient evidence of repetitive, unproductive actions. This tool is particularly useful for managing AI agents like Codex, Claude Code, and OpenCode, ensuring they operate efficiently and only take action when necessary.
MARGINAL is useful for AI agent governance, ensuring that agents do not repeat unproductive actions, verifying the effectiveness of AI-generated code, and enforcing policies based on evidence of no progress.
MARGINAL does not handle credentials or private keys directly. It avoids using raw prompts, commands, or outputs as evidence. All network communication is designed to be privacy-preserving, with optional sharing modes that do not expose private data.
Summary and analysis are original content generated by AI Skills Rank. The skill's source text is not reproduced here — view it on the linked repository.