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Found 1,282 Skills
A prompt repetition technique for improving LLM accuracy. Achieves significant performance gains in 67% (47/70) of 70 benchmarks. Automatically applied on lightweight models (haiku, flash, mini).
Complete SEO setup for Next.js applications. Use when the user wants to implement or improve SEO in a Next.js app, including page metadata, sitemap.xml, llms.txt, robots.txt, and JSON-LD structured data generation, or SEO auditing. Trigger for queries about Next.js SEO optimization, search engine visibility, metadata management, or when the user mentions wanting better SEO for their Next.js application.
[Hyper] Create integrated SEO, AEO, GEO, and LLMO audits and optimization reports. Use for on-page, technical, content, Core Web Vitals, answer-engine, generative-engine, AI search visibility, metadata, citation readiness, or score-improvement loops saved under `.hypercore/seo-maker/[slug]/`.
Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.
Methodology for effective AI-assisted software development. Use when helping users build software with AI coding assistants, debugging AI-generated code, planning features for AI implementation, managing version control in AI workflows, or when users mention "vibe coding," Cursor, Windsurf, or similar AI coding tools. Provides strategies for planning, testing, debugging, and iterating on code written with LLM assistance.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Remove LLM-generated code patterns that add noise without value. Use when reviewing diffs, PRs, or branches to clean up AI-generated code. Triggers include requests to "remove slop", "clean up AI code", "review for AI patterns", or checking diffs against main for unnecessary verbosity, redundant checks, or over-engineering introduced by LLMs. Language-agnostic.
Use this skill when crafting LLM prompts, implementing chain-of-thought reasoning, designing few-shot examples, building RAG pipelines, or optimizing prompt performance. Triggers on prompt design, system prompts, few-shot learning, chain-of-thought, prompt chaining, RAG, retrieval-augmented generation, prompt templates, structured output, and any task requiring effective LLM interaction patterns.
Auto-Claude Graphiti memory system configuration and usage. Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance.
AI-powered penetration testing assistant using local LLM (metatron-qwen via Ollama) on Parrot OS Linux
Complete reference for the Galileo AI platform Python SDK for evaluating, observing, and protecting GenAI applications. Use when building Python applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
Used for answering, generating, refactoring, and troubleshooting code related to wot-ui v2. Keywords: wot-ui, uni-app, Vue3, wd-, ConfigProvider, useToast, useDialog, Form, Popup, theme, llms-full. Suitable for component selection, API query, sample page generation, theme customization, and troubleshooting common pitfalls.