TobiasBlask

Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

Activate when the user needs to generate, refine, or evaluate academic figures, diagrams, or statistical plots. Uses PaperBanana to transform text descriptions or data files into publication-quality illustrations via direct Python API call. Fallback: matplotlib/seaborn.

Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me

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 wit

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

Activate when the user needs to evaluate whether a research idea is worth pursuing, brainstorm new research directions, or stress-test a paper concept before committing. This is Phase 0 of the paper machine — the gate that decides whether the full 8-phase pipeline should run. Also activates standalone via /evaluate-idea. Integrates Carlini's research philosophy (conclusion-first test, taste for pr

Activate when the user wants to export a completed paper draft to production-ready LaTeX (.tex) and PDF. Converts draft.md + references.bib + figures/ into a complete arxiv-style LaTeX project with properly resolved \citep/\citet citations, booktabs tables, figure environments, and compiled PDF output.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring

Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Za

Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological net

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

ALWAYS activate when the user needs to find, organize, review, or synthesize academic literature. Uses academic APIs (Semantic Scholar, OpenAlex, CrossRef, arXiv) via scripts/academic_search.py. Handles search strategy, snowballing, screening, concept matrices, narrative synthesis, and literature monitoring (detecting new publications since last search). NEVER use web scraping for paper discovery

Interact with Zotero reference management libraries using the pyzotero Python client. Retrieve, create, update, and delete items, collections, tags, and attachments via the Zotero Web API v3. Use this skill when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automati

Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant p

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like "what if...", "what would happen if...", "what are the possibilities", "explore scenarios", "scenario analysis", "possibility space", "what could go wrong", "best case / worst case", "risk analysis", "contingency planning", "strategic options", or any que

Activate when the user wants to audit a paper's empirical or technical claims against a linked code repository — checking whether experiments, datasets, models, metrics, and hyperparameters described in the paper actually exist and match the code. Produces a structured audit report classifying each claim as CONFIRMED, PARTIAL, MISSING, or MISMATCH, with file/line evidence. Useful for reproducibili

Activate when the user needs to manage multi-author collaboration on a paper. Tracks author contributions using the CRediT taxonomy, manages responsibility assignments, documents the human-AI division of labor, and produces an author contribution statement ready for submission.

Activate when the user needs to create conference presentation slides from a completed paper. Extracts key content, designs a slide structure, generates slide content with speaker notes, and produces a presentation-ready markdown file. Supports IS/CS conference formats (15-20 min presentations).

Activate when the user needs to systematically screen papers for a Systematic Literature Review (SLR). Implements the PRISMA-compliant screening pipeline: define inclusion/exclusion criteria, title/abstract screening, full-text screening, quality assessment, and PRISMA flow diagram generation. Takes the literature_base.csv from Phase 1 (Reconnaissance) and produces a filtered, documented, auditabl

Activate when the user needs to verify citations, check source accuracy, or validate that referenced papers actually support the claims attributed to them. Retrieves source abstracts and full text (for open-access papers), compares each citation claim against actual source content, and produces a structured verification report with classifications (VERIFIED, PLAUSIBLE, MISMATCH, UNVERIFIABLE, NOT

Activate when the user needs to analyze how their paper positions itself relative to the closest existing work. Builds a differentiation matrix, identifies unique positioning, and generates a positioning statement that strengthens the contribution argumentation. Complements the theory-engine by focusing on the competitive landscape of related papers.

ALWAYS activate when the user writes, drafts, or revises any part of an academic paper. This is the core skill for overcoming writer's block and producing complete first drafts. Provides concrete sentence-level templates, paragraph formulas, and section blueprints for IS/WI/BWL research papers. Works for journal papers (MISQ, BISE, EJIS), conference papers (ICIS, ECIS, WI), and working papers.

Se activa cuando el usuario necesita seleccionar, justificar, describir o ejecutar una metodología de investigación. Proporciona orientación para la selección de métodos, plantillas completas para secciones de métodos, criterios de calidad y recomendaciones de herramientas, cubriendo enfoques como SLR, cualitativos, cuantitativos, DSR y métodos mixtos.

Activate when the user wants to prepare a paper for submission to a specific venue. Handles venue-specific formatting validation, anonymization checks for double-blind review, cover letter generation, suggested reviewer identification, and submission checklist completion. Produces a submission-ready package.

Activate when the user needs to select a theoretical lens, formulate a research gap, derive hypotheses or design principles, or write a contribution statement. Provides concrete theory-to-paper templates, not abstract advice.

Se activa para simular una revisión por pares doble ciego de un artículo antes de su envío o de compartirlo. Lee el borrador, genera dos informes de revisores independientes al estilo de las principales conferencias de SI/CS y los guarda como simulated_reviews.md, formateado para /respond-re.

Se activa para el análisis de datos cualitativos, incluyendo transcripciones de entrevistas y respuestas de encuestas abiertas. Maneja la sumarización estructurada, codificación temática y análisis inter-casos, diseñado para superar las limitaciones de la ventana de contexto trabajando con resúmenes compactos.

Se activa cuando el usuario proporciona comentarios de revisores o coautores (PDF anotado, comentarios pegados o informe de revisor) y desea implementar revisiones. Extrae puntos de revisión, los mapea a ubicaciones en paper.tex, clasifica acciones, implementa cambios, recompila y genera un registro de cambios + latexdiff, manejando el ciclo completo de revisión desde la retroalimentación hasta los cambios confirmados.