Loading...
Loading...
Found 1,783 Skills
Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs interaction), INP debugging (latency breakdown + attribution), and cross-skill integration with loading, interaction, and media skills. Use when the user asks about Core Web Vitals, LCP optimization, layout shifts, or interaction responsiveness. Compatible with Chrome DevTools MCP.
Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.
Use when the user asks about grid trading, ETH/USDC bot, automated trading on Base, grid bot status, trade history, PnL report, or mentions running/stopping/monitoring the grid bot. Covers: grid tick execution, start/stop daemon, status/report/history, market analysis, deposit tracking, retry failed trades. Do NOT use for manual token swaps or DeFi lending — use strategy-auto-rebalance for yield optimization.
Comprehensive AEM (Adobe Experience Manager) architecture design skill for enterprise implementations. Use this skill when building AEM components, designing content architecture, configuring Dispatcher caching, creating dialogs with Granite UI, implementing Sling Models, OSGi services, servlets, or ClientLibs. Covers component development, performance optimization, caching strategies, security best practices, and CI/CD with Cloud Manager. Supports both AEM 6.5 and AEM as a Cloud Service. Also creates architecture diagrams (Mermaid, ASCII) for AEM systems. Triggers on requests for AEM development guidance, component creation, dialog design, caching configuration, architectural decisions, or diagram/visualization requests for AEM projects.
Systematic 4-phase codebase exploration: Detect, Explore, Map, Summarize. Use when starting work on an unfamiliar codebase, onboarding to a new project, reviewing a repository for the first time, or building context before debugging or code review. Use for "explore codebase", "what does this project do", "understand architecture", or "onboard me". Do NOT use for modifying files, running applications, performance optimization, or deep domain analysis.
When the user wants to build or improve a sales bot's ability to respect prospect time zones for outreach. Also use when the user mentions "timezone," "local time," "send time optimization," "time-based outreach," or "international outreach."
SEO patterns, conventions, and audit for web applications. Use when implementing meta tags, structured data, Core Web Vitals, sitemaps, Open Graph, auditing SEO, or optimizing pages for search engines. Triggers on "audit SEO", "check SEO", "review SEO", or tasks involving search optimization, schema markup, or social sharing meta tags.
Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.
Comprehensive guide for building high-performance Solana programs using Pinocchio - the zero-dependency, zero-copy framework. Covers account validation, CPI patterns, optimization techniques, and migration from Anchor.
Analyze text content using both traditional NLP and LLM-enhanced methods. Extract sentiment, topics, keywords, and insights from various content types including social media posts, articles, reviews, and video content. Use when working with text analysis, sentiment detection, topic modeling, or content optimization.
Autonomous LLM training optimization with GPU support. Runs 5-minute training experiments, measures val_bpb, keeps improvements or reverts — repeat forever. Use this skill when the user asks to "train a model autonomously", "optimize LLM training", "run ML experiments", "autoresearch with GPU", "optimize val_bpb", "autonomous ML training", "LLM pretraining loop", "setup ML autoresearch", "GPU training experiments", "pretrain from scratch", "speed up training", "lower my loss", "GPU optimization", "CUDA training", or mentions "train.py", "prepare.py", "bits per byte", "val_bpb", "NVIDIA GPU training", "RTX training", "H100 training", "autonomous model training", "consumer GPU training", "low VRAM training". Always use this skill when the user wants to autonomously optimize any ML training metric.
Use when app feels slow, memory grows, battery drains, or diagnosing ANY performance issue. Covers memory leaks, profiling, Instruments workflows, retain cycles, performance optimization.