Three independent AI reviewers check your blog draft for generic filler, trademark or copyright red flags, and skeptic-bait claims, then cross-examine.
Audit Excel workbooks before you trust, send, or deliver them. Detect formula, structure, data-quality, hidden-content, dependency, validation, and consistency risks with traceable evidence — without modifying the original file.
At McKinsey, a recommendation was only as good as the research under it. So I learned to do it right: scope the real question, pull from independent sources, triangulate what's actually true, and ground every claim in evidence. This Skill runs that discipline for you — breaking down a question, comparing sources, verifying claims, and writing a structured report with citations you can check. Decisions on evidence, not vibes.
Turn a vague success claim into one sourced metric, then blur a client name lacking permission.
Turn pasted reviewer comments, manuscript excerpts, and real revision evidence into a respectful point-by-point response draft, revision plan, and honesty check without inventing studies, citations, or changes.
So what's your argument? If that comment keeps showing up, this is why. I read your draft and tell you whether it has an actual argument, meaning a claim a knowledgeable reader could honestly disagree with. If it does, I show you where it is and how well it's defended. If it doesn't, I find where it's hiding and show you what to do about it.
Turn pasted AI evaluation cases into an evidence-bounded triage brief that separates expected and observed behavior, hypotheses, and missing checks without claiming a root cause or fix.
Academic sanity-check with interactive HTML dashboard. Detects look-ahead bias, over-parameterization, and benchmarks against 10 canonical blueprints via animated SVG gauges and color-coded KPI cards. Parses natural language, code, or CSV uploads. Output auto-adapts (EN/ZH-TW/ZH-CN). Built on De Prado (2018). For students and strategy developers. Not financial advice.
Your backtest looks strong. But can you trust the evidence behind it? Check for holdout contamination, time leakage, cost-stress failures, concentration risk, and reproducibility issues — before you rely on the result.