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-aitreatment-plans
Formats and structurally validates local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aiumap-learn
Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aiuncertainty-and-units
Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibili
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aiusfiscaldata
Queries the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aivaex
Processes large tabular scientific datasets with Vaex expressions, filtered views, streamed statistics, binned visualizations, and file conversion. Use for larger-than-RAM HDF5, Arrow, CSV, or Parquet analysis, virtual feature engineering, or Vaex ML preprocessing; distinguishes these operations from estimators and conversions that materialize data.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aivenue-templates
Prepares journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Supports selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aiwaypoint-bio
Supports work with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the waypoint CLI from the waypoint-bio package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables i
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aiwhat-if-oracle
Supports structured what-if scenario analysis for research planning, experimental contingencies, and scientific project decisions. Explores favorable, reference, adverse, wild-card, contrarian, and second-order scenarios with explicit assumptions, evidence, and decision triggers. Use to stress-test a research plan under uncertainty; scenario narratives do not estimate causal effects or calibrated forecast probabilities.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日
k-dense-aizarr-python
Stores and queries chunked N-D scientific arrays with Zarr-Python 3, including codecs, sharding, S3/GCS storage, and NumPy/Dask/Xarray integration. Use for array layout, bounded I/O, format migration, or scientific metadata preservation.
- 導入数
- 0
- GitHub Stars
- 5.5万
- 更新日
- 10月19日