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Found 4,094 Skills
Fast, low-cost exploration of Robonet trading resources. Browse 8 data tools to explore available trading pairs, technical indicators, Allora ML topics, existing strategies, and backtest results. All tools execute in <1 second with minimal cost (free to $0.001). Use this skill first before building or testing strategies to understand what resources are available.
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, functional annotation, or gene set analysis.
Controls Windows Remote Desktop sessions for automation, testing, and remote administration. Use when the user needs to connect to Windows machines via RDP, take screenshots, click, type, or interact with remote Windows desktops.
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check, HuggingFace inspection and validation, codebase exploration, tao-core configuration and native trainer implementation, ONNX export plus TensorRT deploy integration, packaging and L0 testing, container-based end-to-end validation, and (conditional) accuracy/latency tuning. Supports classification, object detection, semantic / instance / panoptic segmentation, zero-shot detection, and depth estimation.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Comprehensive Java development skill based on Alibaba Java Coding Guidelines (Songshan Edition). Use when writing, reviewing, or refactoring Java code to ensure compliance with industry best practices. Triggers on: (1) Writing new Java code (.java files), (2) Reviewing existing Java code, (3) Refactoring Java projects, (4) Database design with MySQL, (5) API design and implementation, (6) Unit testing, (7) Concurrent programming, (8) Security implementation, or any Java development tasks requiring adherence to coding standards.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, or feature flags.
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.
Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research operations.
MiniQMT Xuntou Quantitative Trading Interface, based on the XtQuant Python library, supports market data acquisition (K-line, tick data, financial data, etc.) and trading operations (order placement, order cancellation, querying assets/orders/positions) for A-shares, futures, and options. It is used when users need to obtain real-time/historical market data from MiniQMT, conduct quantitative trading, or perform backtesting.
Pre-launch checklist for shipping a new website. Orchestrates analytics setup (GA4, PostHog, Google Search Console, Ahrefs), legal compliance, security headers and audit, SEO and GEO with keyword research validated against Google Trends (robots.txt, sitemaps, llms.txt, AI policy, schema markup, hreflang), copywriting consistency via a TONE.md and a humanizer pass in the matching language, OpenGraph and social previews, full favicon set with manifest, quality gates (Lighthouse, Core Web Vitals, WCAG accessibility, mobile testing), and setup of a weekly SEO agent. Use this skill whenever the user mentions launching a site/app, deploying a domain to production, pre-launch audit, shipping a marketing/docs/SaaS site or lead magnet, or says "checklist for the site", "ready to ship", "before I go live", "audit before launch", "ready for prod", or asks for a site review.