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Found 1,931 Skills
Audit and improve SwiftUI runtime performance. Use for slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
Autonomous ML experimentation framework by Andrej Karpathy. AI agent autonomously modifies train.py, runs 5-minute GPU experiments, evaluates with val_bpb, and commits only improvements via git ratcheting — so you wake up to 100+ experiments and a better model. Use when setting up autoresearch, writing program.md directives, interpreting results, configuring hardware, or running overnight autonomous ML experiments. Triggers on: autoresearch, autonomous ml experiments, overnight gpu experiments, karpathy autoresearch, train.py experiments, val_bpb, program.md research directives, ai runs experiments.
Find and read academic papers: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), and read with structured analysis (3-level strategy). Use when: finding papers, reading a paper, related work, citation analysis, research trends, SOTA results, datasets. Do NOT use for generating literature survey reports (use research-survey), generating research ideas (use research-ideation), writing a paper's Related Work section (use paper-writing), comparing/ranking research ideas (use research-ideation), or planning paper structure (use paper-planning).
Interpret the three core financial statements (income statement, balance sheet, cash flow statement) to assess business health and performance. Use this skill when the user needs to read financial statements, understand profitability vs cash flow, evaluate a company's financial position, or prepare for investor/board meetings — even if they say 'explain these financials', 'are we making money', 'read this annual report', or 'what do these numbers mean'.
Build credit scoring models to predict default probability from borrower characteristics. Use this skill when the user needs to assess creditworthiness, build a credit scorecard, or evaluate lending risk — even if they say 'predict default risk', 'credit scoring', or 'loan approval model'.
Audit Lightning Web Components for SLDS compliance and produce a scored quality report. Runs the SLDS linter, analyzes CSS for theming hook usage and pairing, checks HTML for accessibility attributes, and scores findings across categories into an overall grade. Use when asked to "score my component", "SLDS scorecard", "quality report", "audit SLDS compliance", "how good is my SLDS", "check component quality", "rate my component", "evaluate my component", "is this component ready to ship?", "look at my LWC for issues", "audit this before I submit", "review my component before code review", or any time a user wants a quality assessment or production-readiness check on an LWC or SLDS component. Not for fixing violations (use uplifting-components-to-slds2) or building new components (use applying-slds).
Audit a GitHub repository's security posture and hardening gaps across branch protection, CODEOWNERS, GitHub Actions, publish/release integrity, collaborator access, security features, and dependency review. Use when reviewing or hardening a repo, assessing GitHub configuration, checking CI/CD or Actions security, evaluating supply-chain posture, preparing maintainer-facing security todos, or when the user says "repo security posture", "audit my repo", "harden this GitHub repo", "actions security", "protect against a compromised maintainer", or points at a GitHub URL and asks what to fix.
Smart contract development advisor based on Trail of Bits' best practices. Analyzes codebase to generate documentation/specifications, review architecture, check upgradeability patterns, assess implementation quality, identify pitfalls, review dependencies, and evaluate testing. Provides actionable recommendations.
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
Find, evaluate, and maintain high-quality external resources for JavaScript concept documentation, including auditing for broken and outdated links
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
Comprehensive guide for this Neovim configuration - a modular, performance-optimized Lua-based IDE. Use when configuring plugins, adding keybindings, setting up LSP servers, debugging, or extending the configuration. Covers lazy.nvim, 82+ plugins across 9 categories, DAP debugging, AI integrations, and performance optimization.