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Found 599 Skills
Execute Google Gemini CLI for large-context code analysis, multimodal reasoning, and repository-scale reviews. Also use for delegating tasks requiring 1M token context windows or Gemini-specific capabilities.
Think carefully no matter what question you answer. Before answering any question or performing any task, conduct in-depth analysis and reasoning first.
Saves, retrieves, and searches structured context (reasoning, decisions, trade-offs) behind AI-generated code, linked to git commits. Use when the developer says "save this session with GitWhy", asks about past context or decisions, or wants to push context to a PR. Also use after significant implementation work, debugging sessions, or architecture decisions.
Define and manage cognitive patterns for agent reasoning and decision-making
Sequential Thinking MCP and UltraThink mode for deep analysis, complex problem decomposition, and structured reasoning workflows. Use when performing multi-step analysis, architecture decisions, technology selection trade-offs, breaking change assessment, or when --ultrathink flag is specified. Do NOT use for simple decisions or straightforward implementation tasks.
Generate text content using Google Gemini models via scripts/. Use for text generation, multimodal prompts with images, thinking mode for complex reasoning, JSON-formatted outputs, and Google Search grounding for real-time information. Triggers on "generate with gemini", "use gemini for text", "AI text generation", "multimodal prompt", "gemini thinking mode", "grounded response".
Step-by-step analysis for complex problems — multi-step reasoning, hypothesis verification, adaptive planning with revision.
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
You are **App Store Optimizer**, an expert app store marketing specialist who focuses on App Store Optimization (ASO), conversion rate optimization, and app discoverability. You maximize organic do...
Etsy search optimization — long-tail keywords, tag research, competitor analysis, seasonal trends
Reset the FPF reasoning cycle to start fresh
Reduce a webpage to a structural skeleton with semantic tokens. Two-phase pipeline: Phase 1 injects a browser script that tokenizes content ({TEXT}, {HEADING:n}, {IMAGE:WxH}, {CTA:label}, {LINK:label}, {INPUT:type}, {VIDEO}, {ICON}). Phase 2 applies LLM structural reasoning to collapse repeated patterns ({REPEAT:N}), remove decorative wrappers, strip utility classes, and produce skeleton.html + manifest.json. Use when migrating pages to EDS, analyzing page structure, extracting page blueprints, or preparing input for GenAI block generation. Triggers on: reduce page, page skeleton, page blueprint, extract structure, tokenize page, page reduction, structural skeleton, reduce URL.