Skill UI

Executes a structured VC valuation workflow that ingests SaaS or startup metrics, runs DCF, comparable revenue multiples, and SaaS health models via Python, and synthesizes a defensible valuation, runway, and capital-efficiency narrative for users planning fundraising or benchmarking.

Hard screening skill runs deterministic VC analyses where Claude gathers context, Python scores/formats, and the pipeline produces auditable verdicts with investment thesis guidance.

Guides analysts through soft-mode startup screening by extracting company profiles, scoring eight VC dimensions, applying four investor lenses, and delivering a PASS/DECLINE verdict with a concise memo.

polars-bio accelerates genomic interval math and bioinformatics I/O by combining Polars, Arrow, and DataFusion to handle BED/VCF/BAM/GFF/FASTA streams, SQL queries, and cloud storage with overlap, coverage, and nearest APIs plus streaming fallbacks.

Search historical Lark video conferences, retrieve meeting minutes, AI summaries, action items, transcripts, and query participant snapshots for completed meetings.

Provides an expert Spring Boot 4 testing guide that explains the test pyramid, slice annotations, AssertJ-centric assertions, MockMvcTester, RestTestClient, Testcontainers, and practical coverage goals.

High-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames. Supports overlap, nearest, merge, coverage, complement and subtract for BED/VCF/BAM/GFF files, with streaming and cloud-native access as a faster bioframe alternative.

A Python library for reading, querying, filtering, and writing genomic file formats including SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data. Supports pileup, coverage analysis, indexing, and CRAM reference operations with HTSlib integration. Ideal for bioinformatics workflows requiring low-level streaming access to genomic data.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This skill provides a comprehensive Python guide to building a production-ready Virtual Cloud Network (VCN) on Oracle Cloud Infrastructure (OCI). It details the entire process from scratch, including creating the VCN, setting up Internet and NAT Gateways, configuring route tables, and defining Network Security Groups (NSGs). This is essential for engineers needing to provision complex, secure, and highly functional network architectures for development or production use.

Collects comprehensive diagnostics from an unresponsive Oracle Cloud Infrastructure (OCI) compute instance. This tool gathers critical data—including serial console logs, cloud-init logs, VCN flow logs, and CPU metrics—into a single, self-contained tar archive. Use this when an instance is failing, stuck in provisioning, or showing underlying infrastructure errors, providing support teams with all necessary information for root-cause analysis.