Is your AI fair, grounded, honest, and defensible?
Free & open · no signup · read-only — every stage ends by asking · nothing leaves your machine
The responsibility pass for an AI product: fairness across users, grounded and honest outputs, prompt-injection resistance, transparency, and the provenance of the data behind the model.
The conductor fetches each brief in turn and writes its report before moving on — later briefs can build on earlier findings. Or copy any single stage to run it alone.
5 reports in reports/, plus the run's own INDEX.md: FAIRNESS.md, GROUNDING.md, REDTEAM.md, TRANSPARENCY.md, PROVENANCE.md. Feed them to the optional Studio to turn findings into commits, or run 28 · Roadmap Synthesis to merge them into one plan.
Copy the conductor into your agent inside the repo you want checked. It runs each Goal Prompt in sequence, honoring each one's ask-first rule.
Find the code that ranks, scores, or filters people, list the exact fields it consumes, and check whether any eval ever sliced results by group — suspicions labeled.
Whether the product's AI outputs are grounded in real sources or confidently invented — and whether it cites, hedges, and refuses when it should.
Attack the product's own AI like an adversary — make it ignore instructions, leak secrets, or misuse tools — and find where untrusted input can hijack it.
Diff what the UI says about the AI against what the code does — disclosure strings quoted, dropped confidence traced, retention claims checked against real payloads.
Whether the data used to train, fine-tune, or retrieve is licensed, clean, and free of the PII and contamination that create legal and quality risk.