Paste a Methods section — or start from just a research question — and get back a severity-rated validity audit, mapped to the checklist your journal actually mandates.
Causal claims from cross-sectional data. A mediator treated as a control. Saturation claimed without a method. These send the paper back before review.
Measurement invariance untested. α reported without ω. A control variable that isn't one. HTMT ignored. Fixable, but the reviewer will catch it.
Report effect sizes. Add a flowchart. Cite the threshold you used. One-sentence fixes that signal rigor without changing the argument.
α > .7, r > .6, κ > .7 are conventions, not laws. The pack tells you which, with the citation, so you can defend a number to a reviewer instead of parroting it.
Reviewers don't audit against a textbook; they audit against STROBE or COREQ. Every report ends with a gap list keyed to item numbers you can act on today.
Likert-as-interval, saturation vs information power, McDonald's ω over α, HTMT over Fornell-Larcker, "control for everything" as a mistake. Where generic AI advice is confidently wrong, the pack gets it right.
Not a wall of nitpicks. A ranked list of which one sinks the paper, which three are one-sentence fixes, and which ones to leave alone because the reviewer won't care.
Process tracing — the four evidential tests (hoop, smoking gun, straw-in-the-wind, doubly decisive), mechanism specification, DA-RT transparency. QCA — calibration, necessity vs sufficiency, consistency and coverage. A narrated mechanism is not a traced one, and a high-consistency solution can be a calibration artifact.
Formal models, conceptual frameworks, critical essays — load-bearing assumptions, derivation soundness, the tautology trap (claims true by definition marketed as discoveries), knife-edge robustness, and falsifiability judged against the model's declared purpose. It won't lecture a fables-style modeler about testability, and won't let an organizing framework wear a predictive abstract.
Paste your Methods section — or a proposal, or a pre-registration — and get the full report in about two minutes. Ideal for a resubmission or a deadline.
Start from your research question. The pack walks you through the design one step at a time — beginning at conceptualization — and helps you fix problems before you've collected a single data point.
Quantitative, qualitative, mixed-methods, case-based (process tracing + QCA), and pure theory (formal models, frameworks, essays). Install in Claude Code / Cursor, or paste as a system prompt anywhere. The router picks the right one if you're not sure.
For DeepSeek, Kimi, GLM, Qwen, ChatGPT, and any OpenAI-compatible API.
5 fast-mode audits of realistic manuscripts (including a process-tracing and a theory audit) + 1 coach-mode walk-through of designing a study from scratch.
Run it yourself, on paper, when you don't want to fire up an AI.
Light and dark variants. Follows the same logic as the audit skills.
For every platform. No DRM, no tracking, no subscription.
Unzip the download, then tell Claude Code, Cursor, or Codex: "Install Reviewer Zero."
Using ChatGPT, DeepSeek, or Kimi? Copy the prompt file from the download, paste it into your AI. No install needed.
It runs on an LLM, so it can miss things and raise false alarms — every finding is a prompt for your judgment, not a verdict. And it's built to help you think about your study, not to write it for you: the drafts it produces are scaffolding to rewrite in your own voice, and you should disclose AI use as your journal requires.
One-time purchase. Free updates. Refunds under Gumroad's standard terms.
Get Reviewer Zero — $199