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Found 6,275 Skills
Go concurrency patterns including goroutine lifecycle management, channel usage, mutex handling, and sync primitives. Use when writing concurrent Go code, spawning goroutines, working with channels, or documenting thread-safety guarantees. Based on Google and Uber Go Style Guides.
Monorepo development guidelines using Tamagui, Turbo, Next.js, Expo, Supabase, and cross-platform best practices.
Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.
Transforms conversations and discussions into structured documentation pages in Notion. Captures insights, decisions, and knowledge from chat context, formats appropriately, and saves to wikis or databases with proper organization and linking for easy discovery.
AI-powered crypto trading agent via natural language. Use when the user wants to trade crypto (buy/sell/swap tokens), check portfolio balances, view token prices, transfer crypto, manage NFTs, use leverage, bet on Polymarket, deploy tokens, set up automated trading strategies, submit raw transactions, execute calldata, or send transaction JSON. Supports Base, Ethereum, Polygon, Solana, and Unichain. Comprehensive capabilities include trading, portfolio management, market research, NFT operations, prediction markets, leverage trading, DeFi operations, automation, and arbitrary transaction submission.
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing context usage, or implementing conversation history management.
Use when user needs PostgreSQL database administration, performance optimization, high availability setup, backup/recovery, or advanced PostgreSQL feature implementation.
Prioritize features and define MVP boundaries based on problem framing and user models. Use when a user has validated their problem and understands their users but needs to decide what to build first. Outputs feature priorities, MVP scope, and explicit cuts that feed into PRD generation.
This skill provides comprehensive knowledge for building static websites with Hugo static site generator. It should be used when setting up Hugo projects (blogs, documentation sites, landing pages, portfolios), integrating Tailwind CSS v4 for custom styling, integrating headless CMS systems (Sveltia CMS or TinaCMS), deploying to Cloudflare Workers with Static Assets, configuring themes and templates, and preventing common Hugo setup errors. Use this skill when encountering these scenarios: scaffolding new Hugo sites, choosing between Hugo Extended and Standard editions, integrating Tailwind CSS v4 with Hugo Pipes, configuring hugo.yaml or hugo.toml files, integrating PaperMod or other themes via Git submodules, setting up Sveltia CMS or TinaCMS for content management, deploying to Cloudflare Workers or Pages, troubleshooting baseURL configuration, resolving theme installation errors, fixing frontmatter format issues (YAML vs TOML), preventing date-related build failures, setting up PostCSS with Hugo, or setting up CI/CD with GitHub Actions. Keywords: hugo, hugo-extended, static-site-generator, ssg, go-templates, papermod, goldmark, markdown, blog, documentation, docs-site, landing-page, sveltia-cms, tina-cms, headless-cms, cloudflare-workers, workers-static-assets, wrangler, hugo-server, hugo-build, frontmatter, yaml-frontmatter, toml-config, hugo-themes, hugo-modules, multilingual, i18n, github-actions, version-mismatch, baseurl-error, theme-not-found, tailwind, tailwind-v4, tailwind-css, hugo-pipes, postcss, css-framework, utility-css, hugo-tailwind, tailwind-integration, hugo-assets
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage
GitHub Spec-Kit integration for constitution-based spec-driven development. 7-phase workflow (constitution, specify, clarify, plan, tasks, analyze, implement). Use when working with spec-kit CLI, .specify/ directories, or creating specifications with constitution-driven development. Triggered by "spec-kit", "speckit", "constitution", "specify", references to .specify/ directory, or spec-kit commands.