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Find your next superpower If you are an agent, refer to our llms.txt for full access.Generates scientific diagram drafts using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.7 Flash for quality review. Refines when the review requests improvement, with at most two generations. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Supports research talks, conference presentations, seminars, and thesis defenses. Provides slide structures, design templates, timing guidance, and visual validation.
Builds slide decks and presentations for research talks. Used for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
Builds publication figures with multi-panel layouts, error bars, significance markers, and colorblind-safe styling. Supports PDF, EPS, and TIFF export.
Creates and audits truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use it for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.
Uses a two-stage process of research-backed outlining followed by full-paragraph manuscript writing. Covers IMRAD structure, citation styles, figures and tables, and CONSORT, STROBE, and PRISMA reporting guidelines.
Drafts, revises, and audits scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.
Supports sequence analysis, alignments, phylogenetic trees, alpha and beta diversity, and UniFrac metrics. Includes PCoA ordination, PERMANOVA, and FASTA and Newick input and output.
Supports machine learning in Python with scikit-learn. Applies when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
Creates and maintains coarse product scope in docs/scope/. Supports planning a product or next slice, adding a named feature, and reconciling shipped work to queue what comes next.
Addresses new features, UX behavior, architecture changes, unclear success criteria, and competing approaches before coding. Distinguishes these from mechanical refactors, known fixes, and unambiguous configuration changes.
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