How to Use /Goal to Do More With AI

The "slash goal" primitive represents a fundamental shift in AI interaction, moving from traditional turn-based prompting to autonomous, loop-based execution. By defining clear success criteria and finish-line evidence, users enable AI agents to iterate, self-evaluate, and refine their work without constant human intervention. While initially popularized in coding environments like Codex and Claude Code, this paradigm is increasingly applicable to complex knowledge work, including claim audits, market landscapes, and literature reviews. Effective implementation requires moving beyond simple instructions to providing structured rubrics and verifiable artifacts that allow the model to determine when a task is complete. This evolution in agentic workflows empowers users to offload multi-step processes, provided the objective remains durable and the success metrics are inspectable, ultimately increasing efficiency in tasks that require iterative investigation and evidence-based verification.