k-dense-ai
177 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
k-dense-aiscientific-schematics
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.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscientific-slides
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.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscientific-visualization
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.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscientific-writing
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.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscikit-bio
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscikit-learn
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.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscikit-survival
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.
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- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscvelo
Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.
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- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiscvi-tools
Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aiseaborn
Creates Seaborn statistical visualizations with pandas integration for distributions, relationships, categorical comparisons, regression displays, pair plots, and heatmaps. Supports function and objects interfaces with explicit aggregation, uncertainty, and missing-data handling. Best suited to static exploratory plots; plotly covers interactive figures and scientific-visualization covers publication styling.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aishap
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aisimpy
Builds, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy. Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output analysis.
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- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aistable-baselines3
Trains and evaluates single-agent reinforcement learning with Stable Baselines3 (PPO, SAC, DQN, TD3, DDPG, A2C), Gymnasium custom environments, vectorized rollouts, callbacks, and checkpoint normalization. Applies to reproducible RL experiments, continuous control, discrete actions, and SB3-Contrib recurrent or masked policies.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aistatistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-le
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aistatistical-power
Calculates sample sizes and statistical power for study planning. Applies when someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for complex designs — logistic/Poisson regression,
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aistatsmodels
Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aisympy
Performs exact symbolic mathematics with SymPy for algebra, calculus, equation solving, symbolic linear algebra, physics, and lambdify or LaTeX code generation. Use when a task needs symbolic results, explicit assumptions, or exact arithmetic; use NumPy or SciPy for purely numerical workloads.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitamarind
Provides access to a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitellurium
Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitiledbvcf
Stores and retrieves genomic variant calls with TileDB-VCF. Use for indexed single-sample VCF/BCF ingestion, incremental cohorts, region and sample queries, streaming results, allele statistics, QC, and VCF/BCF export locally or through TileDB Cloud.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitimesfm-forecasting
Performs zero-shot time-series forecasting with Google's TimesFM, including regular-grid CSV preparation, quantile forecasts, XReg covariates, and held-out evaluation. Uses the Apache-licensed TimesFM 2.5 checkpoint by default and documents the distinct TimesFM 3.0 multivariate API and weight-license requirements.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitorch-geometric
Supports PyTorch Geometric (PyG) graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torchgeometric, not for general NetworkX analytics or non-graph PyTorch models.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitorchdrug
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.
k-dense-aitransformers
Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.
- installations
- 0
- GitHub Stars
- 54,6 k
- Mis à jour
- 19 oct.