Results for “celltypist”

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

Generate a complete TypeSpec declarative agent with instructions, capabilities, and conversation starters for Microsoft 365 Copilot.

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.

Identifies a medication from a photo and generates a genotype-informed dosage card using CPIC guidelines and real 23andMe data.

Model and solve LP, MILP, and QP problems using NVIDIA cuOpt's GPU-accelerated solver via Python, C/C++, or CLI interfaces.

Research-driven strategist who produces content strategies, scripts, and copy. Use for competitor analysis, trend research, content planning, scripts, copywriting, or brief validation — or when the user asks for the Strategist.

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data, enabling efficient access to cell metadata, gene expression slices, summary counts, and embeddings without downloading whole datasets.

Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a.

Designs complex generic types, refactors `any` types to strict alternatives, creates type guards and utility types, and resolves TypeScript compiler errors.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Perform fast genomic interval operations (overlap, nearest, merge, coverage, cluster, complement, subtract, count-overlaps), multi-format bioinformatics I/O, DataFusion SQL, and pileup on Polars DataFrames via the polars-bio library, serving as a scalable alternative to bioframe and bedtools.

Simulator.Company digital-twin & actor-agent specialist — talk to an agent AS an agent and delegate work to it. An agent is ANY actor whose `description` holds an "# Agent" competency profile (what it does, what it knows, whether it fits a task). The common case is a person: every workspace user has a 1:1 twin actor (`systemObjType="user"`) carrying that profile. But any actor can be an agent — a service/bot twin, a team or department, an organization, a process. This skill discovers the agent (`findAgent`), loads its profile (`getAgent`), adopts it as the persona, then either does the task, finds a better-suited agent, or hands the decision to the user (for a person: a task or a p2p message; for a non-person: propose another executor or run/trigger it as an actor). It is the actor-analog of `simulator-skills` (the `Skills`-form registry), but the registry is the workspace's agent actors. Use when the user wants to "delegate", "assign this to <someone/something>", "can <X> do this", "who/what should do this",

ML / research person pack for Content Factory. Use when the user says person ml, @person-ml, ML person, research scientist person, or LLM researcher person. Auto-loads ml-research-engineer, llm-researcher, ai-research-scientist plus experiment-tracking, evaluation, cf-llm-model-usage.

Analyzes uploaded data files with full Python access, producing cleaned datasets, statistical reports, charts, and dashboards via the CellCog coding agent.