oximyhq/oximy-reality-checks
Ten evidence-led checks for understanding what AI work touched, changed, remembered, and actually completed.
Reconstruct a completed, failed, expensive, or confusing agent session to find where its trajectory broke and what one change would prevent recurrence. Use when investigating post-hoc session diagnosis, tool-loop analysis, goal drift, premature completion, or repeated human rescue. Do not use as employee performance scoring.
Compare a workflow before and after AI assistance to determine whether work disappeared or moved into review, waiting, correction, exception handling, or rework. Use when someone asks whether AI improved an end-to-end process rather than one production step. Do not claim causal impact from an uncontrolled before-and-after comparison.
Reconcile an AI agent's completion claims with durable evidence in the relevant system of record. Use when someone asks whether agent work actually finished, shipped, sent, changed, or reached production. Do not use a passing local check as proof of an unobserved remote or real-world state.
Compare an AI-produced artifact with the accepted final artifact and quantify the human correction and review it required. Use when someone asks whether AI saved work or shifted effort into editing, review, rework, or escalation. Do not infer time saved, authorship, or causality from a text diff alone.
Compare written AI policy with observed agent configuration and runtime evidence, producing a non-accusatory mismatch ledger. Use when someone wants to test whether approved tools, data rules, permissions, retention, review gates, or prohibited actions match practice. Do not use individual activity as a proxy for intent or misconduct.
Compare an agent's configured capabilities with observed tool use and propose a least-privilege configuration for defined workflows. Use when someone wants to reduce agent permissions, MCP access, filesystem scope, network reach, or approval bypasses. Do not apply permission changes or infer safe denial from non-use alone.
Inspect text or files before they are shared with an AI service, make a task-preserving redacted copy, and explain residual exposure. Use when someone asks whether material is safe to paste or upload, or wants sensitive content sanitized for AI use. Do not treat the result as legal approval or a guarantee of vendor handling.
Decide whether an installed agent skill should be kept, narrowed, revised, merged, quarantined, or retired using real activation and outcome evidence. Use when skills are stale, conflicting, noisy, costly, unused, or suspected of worsening work. Do not delete or rewrite a skill without explicit approval.
Audit persistent information an AI agent can reuse across sessions, including provenance, sensitivity, staleness, contradictions, scope, and deletion evidence. Use when someone asks what an assistant remembers about them or a project, or wants a memory cleanup plan. Do not delete or rewrite memories without explicit approval.
Reconstruct the data journey of a specific AI task from local traces, tool configuration, logs, and artifacts. Use when someone asks what an agent read, transmitted, processed, stored, remembered, displayed, or reported. Do not infer vendor retention or deletion from local traffic evidence alone.