10 AI‑Powered Skills That Seamlessly Automate the Entire Research Process

The author recounts a conversation with a supervisor who wondered why his research output had accelerated, and admits that most of the research pipeline has been handed over to AI—not just simple translation assistance, but a full suite of AI capabilities covering problem formulation, literature review, experimental design, coding, data analysis, manuscript writing, mock peer review, and rebuttal response.

Typical time allocation for a paper (illustrated in the original list):

Literature search & organization: 2‑3 weeks

Problem definition & proposal writing: 1‑2 weeks

Coding, running experiments, tuning: 1 week to 3 months

Writing (abstract to conclusion): 2‑4 weeks

Pre‑submission checks, formatting, additional experiments: 1‑2 weeks

Rejection → new target → resubmission: repeat the cycle

Only about 30 % of the total effort is genuine “thinking”; the remaining 70 % consists of repetitive, procedural tasks that AI can automate.

The author has compiled the ten most useful AI research skills into a public website, each entry providing a functional description, applicable scenarios, a copy‑paste installation guide, and concrete prompt examples. The collection contains over 700 sub‑modules and spans all major disciplines, and every skill has been personally tested.

Skill 1 – General Research Workflow : Given a research direction, the AI helps clarify the problem and novelty, plan the paper structure, write each section (abstract, introduction, methods, experiments), simulate reviewer comments, verify citation relevance, and even generate a pre‑submission “mock review” checklist.

Skill 2 – Automated ML Research Workflow : Described as a “PhD‑level cheat‑code,” it takes a topic and proceeds through literature scanning, novelty generation, code‑base analysis, experiment design, automated execution, result analysis, manuscript drafting, mock peer review, and rebuttal drafting. The author notes that human checkpoints are still advisable before finalizing innovations, code changes, or large‑scale experiments.

Skill 3 – Natural‑Science Skill Library : Contains 148 skills covering bioinformatics, cheminformatics, and drug discovery. Example: a single‑cell analysis prompt that invokes Scanpy, differential expression, pathway enrichment, and scientific visualization, then outputs a reproducible Python script.

Skill 4 – Social‑Science Empirical Research : Supports DID, IV, RDD, PSM, synthetic control methods across economics, management, and sociology. The AI can check data structure, define treatment/control groups, produce descriptive statistics, test parallel trends, run baseline regressions, conduct robustness and placebo checks, analyze heterogeneity, and output reproducible code, tables, and figures in Stata, R, or Python.

Skill 5 – Medical/Biomedical Skill Library : Over 550 skills from PubMed search to meta‑analysis, including survival analysis, Cox regression, Kaplan‑Meier curves, ROC analysis, nomograms, immune infiltration, WGCNA, etc. The site explicitly warns that AI‑generated conclusions must be reviewed by clinicians and statisticians before any clinical decision.

The remaining five skills are listed in a table:

🌱 Chinese comprehensive research skill library – literature polishing, bilingual translation, PPT generation, mock review.

⚡ AI research engineering skill library – 98 skills for model fine‑tuning, distributed training, inference deployment.

✍️ Lightweight paper‑writing skill – end‑to‑end support for ML/CV/NLP papers from abstract to conclusion.

🗂️ Research project management system – integrates Zotero and Obsidian to organize the whole research lifecycle.

🔧 Chinese engineering 3‑in‑1 – MATLAB simulation, signal processing, and paper‑to‑PPT conversion for engineering graduate students.

To obtain the skills, readers are instructed to scan a QR code, add a small assistant on a messaging platform, and send the keyword “科研skills,” after which the assistant provides the website address and a quick‑start guide.

In the closing remarks, the author emphasizes that AI skills do not guarantee “paper‑after‑paper” success; the core of research remains the researcher’s judgment, creativity, and experimental validity. However, by offloading repetitive, time‑consuming tasks, AI frees scholars to focus on the truly important questions—this is AI‑assisted research, not AI‑doing research.