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Found 498 Skills
Optimize programmatic SEO pages for visibility and citation in AI-generated answers from ChatGPT, Perplexity, Google AI Overviews, and other LLM-powered search. Use when optimizing for LLM citation, implementing llms.txt, configuring AI crawler access, structuring content for AI extraction, or when the user asks about generative engine optimization (GEO), AI search visibility, or getting cited by AI.
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For parasite SEO strategy, use parasite-seo. For GitHub, use github-seo.
AI citability scoring and optimization. Analyzes web page content to determine how likely AI systems (ChatGPT, Claude, Perplexity, Gemini) are to cite or quote passages from the page. Provides a citability score (0-100) with specific rewrite suggestions.
LeetCode-style PyTorch interview practice environment with auto-grading for implementing softmax, attention, GPT-2 and more from scratch.
Audit experiment integrity before claiming results. Uses cross-model review (GPT-5.4) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.
imagine is a multi-provider command-line tool for generating and editing images via Google Gemini, Google Vertex AI, and OpenAI (gpt-image-2).
Optimize content for AI search engines including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Microsoft Copilot. Covers generative engine optimization (GEO), AI citability audits, content structuring for extraction, schema markup, bot access configuration, and monitoring. Use when optimizing for AI search, AI overviews, generative search, LLM visibility, semantic search, entity optimization, or when user mentions AI SEO, GEO, Perplexity citations, ChatGPT visibility, or AI-generated answers.
Generate publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to `paper-illustration`, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.
Desktop automation CLI for AI agents (macOS, Linux, Windows). Screenshot, click, type, scroll, drag with native Zig backend. Use this skill when automating desktop apps with computer use models (GPT-5.4, Claude). Covers the screenshot-action feedback loop, coord-map workflow, window-scoped screenshots, and system prompts for accurate clicking.
Run an independent code review using the OpenAI Codex CLI in headless mode. Gets a second opinion from a different model family (GPT-5/o3) on recent changes, a PR, a commit, or the whole app — covering bugs, regressions, security, data consistency, UX/state bugs, performance risks, and testing gaps. Saves a severity-prioritised report to .jez/reviews/. Triggers: 'codex review', 'review with codex', 'second opinion on this code', 'independent code review', 'what does codex think', 'get codex to review'.
Write a high-quality prompt for any LLM or AI assistant — Claude, Claude Code, ChatGPT, Gemini, Cursor, Windsurf, Copilot, or any coding / chat agent. Use this skill whenever the user asks to write, improve, refine, shorten, or rewrite a prompt; asks "how should I phrase this for [model]" or "what's a good prompt for [task]"; describes a task they want an AI to do but hasn't yet formulated it as a prompt; or pastes an existing prompt and asks for revision. Based on Boris's (Anthropic, Claude Code creator) prompt methodology — short and accurate prompts, plan-before-code, feedback loops, persistent context in files. The universal principles (short, plan-first, feedback-loop, no-padding) apply to any LLM; the Claude-Code-specific anchors (CLAUDE.md, @file, slash commands) only apply when the target is Claude Code. If the user's intent is unclear (target model, deliverable, scope, or whether the AI has a way to self-verify is missing), ask 1–3 targeted clarifying questions via AskUserQuestion before writing the prompt.
Multi-model deep review of the Ralph bd graph and plan via three parallel opencode processes (claude opus, gemini, gpt). Use for high-stakes runs where cross-model consensus reduces single-model bias.