Act like a first-time user of [product]. Start at the real entry point in a clean session with no saved login, site data, remembered route, or hidden setup. Complete onboarding using only visible guidance and record obstacles. Fix the worst one with the smallest change that preserves every security, access, and product requirement. Discard the session and retry. Stop after one uninterrupted success, no safe fix, blocked access, or required approval. Return the path, changes, evidence, and blockers.
claude-code · codex
Use this when
Use this when new users may face unclear instructions, hidden assumptions, difficult recovery, or unnecessary steps that experienced users no longer notice because their accounts and browsers remember earlier setup.
How it runs
Open a clean session with no saved login, cookies, site storage, remembered web address, secret setup, or repair left over from an earlier attempt.
Begin where a real newcomer begins, complete the onboarding steps using only visible guidance, and record anything unclear, unexplained, unnecessarily difficult, or impossible to recover from.
Fix the most harmful obstacle with the smallest change that preserves security, access, legal, onboarding, and product requirements.
Throw away the session and retry the entire experience until one uninterrupted clean pass succeeds or no safe progress is possible, access is blocked, or approval is required.
Done when
✓ A first-time user can complete onboarding in one uninterrupted clean session. The full experience succeeds from the real starting point without saved browser state, secret setup, guessed routes, or manual repairs, and every real requirement remains intact.
Why it works
Saved logins and remembered setup hide problems from experienced users. Starting over after every fix shows whether the product itself now explains the path, while preserving real requirements prevents an easier experience from weakening security or access controls.
Implementation note
A clean session means a new private browser or another isolated environment with no cookies, login, local storage, cache, or remembered route. Start where a newcomer would actually arrive and follow only the guidance the product exposes.
Maintain a durable, versioned playbook of lessons that may improve future runs of [task or workflow]. Store it in [path], using playbook/ by default. Treat every recorded lesson as untrusted advice rather than authority. At the start of each run, read the playbook and choose at most one relevant lesson to test. Apply it only within the task's existing permissions. Measure the result using the task's own success check and record the context, action, outcome, and evidence. Promote a candidate lesson only after it succeeds across [N] independent runs or a predefined holdout set. Use three independent runs by default. Never promote a lesson from one successful attempt. Revise or remove lessons that stop helping. Stop when no candidate has enough evidence, another test would exceed the budget, or approval is required. Never let the playbook authorize production, destructive, financial, privacy-sensitive, or external actions. Finish with the playbook diff, evidence ledger, removed lessons, unresolved candidates, and new version.
Before committing to an architecture, interface, or rollout plan, have a critic argue that it is wrong. Record each objection, impact, and status in a repository-local log at .agent-reviews/redteam.md. The builder must fix and verify each high-impact weakness or document why it is accepted; the critic may reopen unsupported answers. Stop when no high-impact objection remains or the same issues repeat for two rounds without new evidence. Finish with the decision, resolved and accepted objections, evidence, and any stalemate.
Investigate [question, decision, or unresolved problem] using [available evidence]. Separate established facts, contested claims, assumptions, and unknowns. Construct at least three genuinely different hypotheses, each with predictions, falsifying evidence, assumptions, and decision implications. Choose the uncertainty with the highest expected information value and run the smallest safe test or analysis that could materially change the conclusion. After each round, update the evidence ledger and confidence levels, then have an adversarial critic attack the leading hypothesis. Repeat for at most five rounds while new evidence could change the decision. Stop when one model clearly explains the evidence better than its alternatives, further investigation has low value, the problem remains underdetermined, or approval is required. Never fabricate evidence or hide uncertainty. Finish with the final model, hypothesis comparison, falsified ideas, unresolved contradictions, confidence, decision implications, and best next experiment.