# Reward Seeking Evidence Review

> Review evidence and calibrate claims about reward-seeking, reward hacking, grader targeting, evaluation gaming, or oversight-dependent behavior. Use before saying a model is safe, aligned, deceptive, reward-seeking, or improved; also use to distinguish executed behavior, code-path evidence, assumptions, and inconclusive null results.

- Skill: `jpoindexter/reward-seeking-evidence-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add jpoindexter/reward-seeking-evidence-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jpoindexter/reward-seeking-evidence-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jpoindexter (https://skillmd.com/u/jpoindexter)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jpoindexter/reward-seeking-evidence-review

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# Reward-Seeking Evidence Review

Build a claim ledger:

| Claim | Distribution | Evidence | Status | Does not establish |
| --- | --- | --- | --- | --- |

Tag status as `executed`, `code_path`, `inferred`, or `assumed`. A passing unit
test for an evaluator proves evaluator plumbing, not model behavior. A behavioral
shift proves sensitivity in that condition, not a stable global objective.

Check:

- model/provider/version and harness version are pinned;
- task distribution, authority pair, prompt channel, tool policy, and date exist;
- positive and negative controls worked;
- feature opportunity, invalids, refusals, and uncertainty are reported;
- final actions and receipts support the claim independently of chain-of-thought;
- a null is not caused by failed activation or low power;
- the result replicated on a changed surface form;
- an independent decision-maker, not the evaluated grader alone, owns release.

Rank the strongest defensible claim first. Downgrade overbroad language and state
the exact next experiment needed to close each gap.