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Oximy Reality Checks

ChatGPT plugin

Verify consequential AI work

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Rank by placement

#5374of 6,0223places up24h—7d
#5,349#5,427#5,505#5,583Sep 13Sep 19Sep 25Oct 1Oct 7Oct 12

Rank in Productivity

#1195of 1,3711places up24h—7d
#1,191#1,206#1,221#1,236Sep 13Sep 19Sep 25Oct 1Oct 7Oct 12

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Productivity1,195th of 1,371 by placement
#PluginPosition7d
1192R8∆V3◇K6·X9_Q2§Z7#5368—
1193Q4 SALES ENGINE™#5371—
1194Interactive xAPI Designer#5372—
1195Oximy Reality ChecksThis page#5374—
1196Product Support Investigator#5380—
1197WishCraft#5381—
1198Freelancer Tax Organizer#5382—

All 1,371 Productivity plugins

Ten local-first checks for consequential AI work. Trace where data moved, review what an agent can remember, compare AI drafts with accepted work, right-size agent access, diagnose failed runs, and verify claimed outcomes against durable systems of record. Every check separates evidence from inference, preserves unknowns, and ends with a visible completion criterion.

Example prompts

  • “Check whether this agent task actually finished.”
  • “Review this material before I share it with AI.”
  • “Find where this agent run first went wrong.”

Skills

  • agent-autopsy

    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.

  • bottleneck-shift

    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.

  • did-it-land

    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.

  • human-review-tax

    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.

  • policy-vs-reality

    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.

  • right-size-my-agent

    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.

  • safe-to-paste

    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.

  • skill-sunset

    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.

  • what-does-my-ai-remember

    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.

  • where-did-my-data-go

    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.

Categories
Made by
Oximy · Website
Sign-in
On Install
Capabilities
Read, Analyze, Write local reports
Availability
Available
Version
0.1.2
Keywords
ai, audit, evidence, privacy, security, workflows
Created
September 15, 2026
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