Plugins

9 plugins

Azure API Center Management

For Azure developers managing API Center resources with Python and .NET SDKs.

4 skills · plugin

Analyze Single-Cell RNA-Seq

Analyze single-cell RNA-seq data using Scanpy, including quality control, normalization, clustering, marker gene identification, and visualization.

9 skills · plugin

@brycewang-stanford

Twelve-skill bundle covering the Cell manuscript lifecycle: workflow router, scope/significance fit, single-narrative framing, the Highlights + eTOC + Graphical Abstract trio, the ≤150-word Summary, main-text writing, display items, STAR Methods + Key Resources Table, data/code availability, Cell Press author–date references, submission preflight + cover letter, and reviewer rebuttal.

9 skills · plugin

ISO 27001 Audit Pipeline

Pressure-test an ISMS and generate audit evidence for ISO 27001 certification readiness.

9 skills · plugin

@matteobortolazzo

cenci workflow layer: portable engineering conventions and Claude Code's gated GitHub ticket-to-PR pipeline

27 skills · plugin

Robotik-Recht Deutschland/EU: Maschinenverordnung, KI-VO, Produkthaftung, ProdSG, Datenschutz, CRA, Data Act, CE, Marktüberwachung, Unfälle, Rückruf, Verträge und Robotik-Testakte.

2 skills · plugin

@brycewang-stanford

Twelve HRI-specific skills for the ACM/IEEE International Conference on Human-Robot Interaction and its interdisciplinary, human-subjects-centered evidence culture, grounded in the HRI 2026/2027 calls, humanrobotinteraction.org, the ACM Digital Library, IEEE Xplore, and dblp.

2 skills · plugin

@brycewang-stanford

Twelve DAC-specific skills for the ACM/IEEE Design Automation Conference (the Chips to Systems Conference) and its double-blind Research Manuscript track, grounded in the DAC 2026 (63rd) call, dac.com, IEEE CEDA, ACM SIGDA, the ACM Digital Library, and dblp.

2 skills · plugin

@alirezarezvani

C Level Advisor

33 C-level advisory skills + c-level-agents plugin layer: virtual board of directors (CEO, CTO, COO, CPO, CMO, CFO, CRO, CISO, CHRO) plus General Counsel, CDO, CAIO, CCO, and VP of Engineering (DORA delivery throughput analyzer, engineering hiring funnel calculator with conversion + pipeline gap, eng team structure designer with squad/tribe + manager-trigger), executive mentor, founder coach, orch

27 skills · plugin

Results for “ce”

11 skills

Universal Single Cell Annotator

Annotates single-cell RNA-seq data by scoring marker genes, transferring labels with CellTypist, or reasoning over cluster markers with an LLM.

Annotates single-cell RNA-seq data by scoring marker genes, transferring labels with CellTypist, or reasoning over marker lists with an LLM.

Tao Train Centerpose

Train, evaluate, export, and run inference for CenterPose models used in 6-DoF object pose estimation with keypoint regression.

Estimate cell state transitions from unspliced/spliced mRNA dynamics using scVelo, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data.

30.2k · bundle

Analyze RNA velocity in single-cell RNA-seq data with scVelo, estimating cell state transitions from unspliced/spliced mRNA dynamics, inferring trajectory directions, computing latent time, and identifying driver genes.

More results

Predicts tissue or cell-type gene expression (log TPM and TPM) from a TSS-centered DNA sequence using the hosted Genomic Intelligence G0 Expression model, conditioned on a free-text cell-type description.

Provides deep generative models for single-cell omics analysis, including probabilistic batch correction, transfer learning, differential expression, and multi-modal integration.

30.2k · bundle

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Computes the HARE Score, an entity- and relation-centric metric for evaluating machine-generated histopathology reports against ground truth, using GatorTronS+SapBERT embeddings and relation F1.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

30.2k · bundle

Replaces GIoU with Complete IoU (CIoU) loss in PyTorch object tracking or detection tasks, combining overlap area, center-point distance, and aspect-ratio similarity for improved bounding-box regression.