Expert AI data analyst that analyzes any CSV, Excel or tabular dataset. Paste or upload data and get: 📊 Column profiling & statistics · 💡 Key patterns & anomalies · ⚠️ Anomaly detection · ✅ Data quality score · 🔗 Correlation insights · 🚀 Actionable recommendations. Works with sales, financial, survey, log, and any structured data. Educational purposes only.
A full analysis pipeline that turns a dataset into a finished report, not just charts. Hand it a CSV, Excel, Parquet, or JSON file and it cleans the data, scores its quality, runs EDA, and where the data supports it, trains a prediction model with SHAP attribution and a backtested forecast. You get a trust score, ranked findings, and recommendations as a Word doc, an interactive HTML report, and an optional PowerPoint deck. The numbers come from code, so they stay consistent, never invented.
Use when your Japanese draft still reads AI-written. For bloggers, PR teams, marketers, and students: paste the text and get a human-sounding rewrite, plus a diff of every AI tell removed. It replaces vague AI phrasing — 浮き彫りになった, 多面的な, 〜ではないでしょうか, 今後の展開が期待される — with concrete facts and one clear voice. Facts are never invented or changed, only the wording. Output = the rewritten full text + a change log of what was fixed.
Verify that an AI agent changed only the approved SEO fields in your Shopify product CSV. Deterministic, fail-closed validation with JSON and Markdown evidence.
Stop copy-pasting prompts from a doc. Research Writing Assistant routes your academic writing task to the right expert prompt automatically — translation (ZH↔EN, LaTeX & Word), polishing, compression, expansion, de-AI, figure generation, caption writing, experiment analysis, mock review, and chart recommendation. Built from workflows used at MSRA, ByteDance Seed, and top CS PhD programs.
Check AI-generated claims against your approved Fact Sheet and flag changed quantities, expanded scope, dropped attribution, and unsupported mappings — deterministic, local, and no AI guessing.
This skill is based on practical Power Platform delivery patterns for safely creating, reviewing, and modifying Power Automate cloud-flow JSON in Dataverse solution exports. It focuses on issues that often break real client automations: runAfter paths, renamed action references, connection references, hardcoded values, TRY/CATCH logging, import safety, validation, and rollback notes.
I run an AI-focused Medium blog and built this pipeline from real posts I kept rewriting by hand. It does multi-source research with a checkpoint you approve, an outline checkpoint before drafting, a mandatory humanizer pass (kills em-dashes and AI-tell phrasing), a readability/SEO quality check, plagiarism-prep chunking, and a publish-ready export bundle. You control the pace: allow once, allow always, edit, or send it back for a redo at every step.
Catch AI hallucinations before you hit publish. This skill scans articles, blog posts, marketing copy, and AI-drafted text for unsourced stats, vague "studies show" claims, fabricated quotes, and inflated social proof numbers. It flags each claim by risk level and offers three modes: a fast pre-publish scan, a detailed claim-by-claim report, or honest rewrites. Built-in safeguard: it never invents a source or "verified" status to fill a gap — unverifiable stays labeled unverifiable.