Alterlab Deep Research
Skill ActivePart of the AlterLab Academic Skills suite for faculty and researchers. Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
To provide faculty and researchers with a powerful, multi-agent AI team capable of conducting comprehensive academic research across diverse methodologies and topics.
Features
- 13 specialized agents for academic research workflows
- 7 distinct research modes (full, quick, review, lit-review, fact-check, Socratic, systematic review)
- Handles systematic reviews with optional meta-analysis (PRISMA compliant)
- Offers Socratic guided research dialogue for clarifying research questions
- Supports literature monitoring and handoff to paper writing skills
Use Cases
- Conducting a complete research project from question formulation to report
- Getting guided assistance for developing research questions and methodology
- Performing systematic literature reviews and meta-analyses
- Quickly obtaining a research brief on a topic under time constraints
- Getting professional review feedback on a completed research paper
Non-Goals
- Replacing human researchers or advisors
- Performing primary data collection or laboratory experiments
- Providing definitive answers without supporting evidence or analysis
Trust
- warning:Issues Attention2 issues were opened and 0 closed in the last 90 days, indicating slow maintainer engagement with reported problems.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
Similar Extensions
Literature Review
100Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
Survey Theoretical Literature
99Survey and synthesize theoretical literature on a specific topic, identifying seminal papers, key results, open problems, and cross-domain connections. Use when starting research on an unfamiliar theoretical topic, writing a literature review for a paper or thesis, identifying open problems and research gaps, finding cross-domain connections, or evaluating the novelty of a proposed theoretical contribution against existing work.
Notion Research Documentation
100Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.
AlterLab Literature Review
95Part of the AlterLab Academic Skills suite. Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
OraClaw Forecast
100Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.
SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.