How to Use /Goal to Do More With AI

What It Covers

The /goal command in OpenAI Codex and Claude Code represents a shift from turn-based AI interaction to autonomous looping agents. Rather than prompting for results step-by-step, users define a finish-line contract with verifiable success criteria, letting the AI self-evaluate and iterate until completion across coding and knowledge work tasks.

Key Questions Answered

• •Prompt vs. Goal distinction: A /goal is not a larger prompt — it functions as a finish-line contract specifying what should be true at completion, how success gets verified, and what constraints must hold throughout. The AI loops autonomously, checking evidence against the defined endpoint after each step rather than waiting for human feedback to continue.
• •Six-part goal structure: Effective /goal prompts define six elements: the outcome (what should be true), verification surface (tests, artifacts, citations proving completion), constraints (what must not regress), boundaries (permitted files and tools), iteration policy (how to decide next steps), and a block-stop condition (when no defensible path remains and the agent should halt).
• •Goldilocks scope rule: Goals set too narrowly — fix this one line — prevent the agent from discovering upstream dependencies causing the real issue. Goals set too broadly — improve the whole system — make it impossible to define concrete completion evidence. The target scope should be specific enough to verify but wide enough to allow investigative flexibility.
• •Knowledge work applications: Ten non-coding task categories suit the /goal primitive: literature reviews, market landscapes, vendor evaluations, due diligence, claim audits, policy research, interview synthesis, timeline reconstruction, spreadsheet audits, and strategy memos. The qualifying pattern is when the output needs to function as an auditable ledger — tracking what was checked, supported, contradicted, and unverified — rather than a single-pass answer.
• •User-supplied rubrics unlock knowledge work goals: Many knowledge work goals require the user to define success criteria rather than referencing external standards. Hiring criteria, vendor scorecards, editorial standards, lead qualification rules, and investment diligence priorities all represent cases where the user must articulate measurable, AI-testable conditions — and doing so transforms a standard prompt task into a repeatable, autonomous review process.