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All Skills

Total 55,931 skills, AI & Machine Learning has 9310 skills

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Showing 12 of 9310 skills

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AI & Machine Learningthesysdev/skills

thesys-c1-genui

Provides comprehensive guidance for building AI-powered Generative UI applications with the Thesys C1 API and GenUI SDK. Use it when developing interactive UI components, chat interfaces, dashboards, or any application that benefits from dynamically generated React interfaces from natural language prompts.

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23
AI & Machine Learningerichowens/some_claude_sk...

color-theory-palette-harmony-expert

Expert in color theory, palette harmony, and perceptual color science for computational photo composition. Specializes in earth-mover distance optimization, warm/cool alternation, diversity-aware palette selection, and hue-based photo sequencing. Activate on "color palette", "color harmony", "warm cool", "earth mover distance", "Wasserstein", "LAB space", "hue sorted", "palette matching". NOT for basic RGB manipulation (use standard image processing), single-photo color grading (use native-app-designer), UI color schemes (use vaporwave-glassomorphic-ui-designer), or color blindness simulation (accessibility specialists).

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23
AI & Machine Learninganthropics/knowledge-work...

data-context-extractor

Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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23
1 scripts/Checked
AI & Machine Learningfleek-fitness/vox-skills

vox-best-practice

vox.ai 개발 베스트 프랙티스를 적용한다. (1) 한국어 음성 에이전트 system prompt 설계/작성/리팩터링(템플릿, {{...}} 변수 주입, 필러 옵션, Character normalization, 도구/무음 액션, 테스트/운영), (2) vox MCP 서버(https://mcp.tryvox.co/, Streamable HTTP, OAuth 또는 API token)를 ChatGPT/Claude Desktop/Claude Code/Cursor/OpenCode/Codex/VS Code Copilot 등에 연결할 때 사용한다.

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23
AI & Machine Learningmindrally/skills

deep-learning

Comprehensive deep learning guidelines for neural network development, training, and optimization.

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AI & Machine Learningborghei/claude-skills

data-scientist

Expert data science covering machine learning, statistical modeling, experimentation, predictive analytics, and advanced analytics.

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23
AI & Machine Learningboshu2/agentops

swarm

Spawn isolated agents for parallel task execution. Local mode auto-selects Codex sub-agents or Claude teams. Distributed mode uses tmux + Agent Mail (process isolation, persistence). Triggers: "swarm", "spawn agents", "parallel work".

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1 scripts/Attention
AI & Machine Learningfaionfaion/faion-network

faion-claude-code

Claude Code: skills, agents, hooks, commands, MCP servers, IDE integrations.

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23
AI & Machine Learningjulianobarbosa/claude-cod...

repomix

Pack entire codebases into AI-friendly files for LLM analysis. Use when consolidating code for AI review, generating codebase summaries, or preparing context for ChatGPT, Claude, or other AI tools.

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23
AI & Machine Learningsickn33/antigravity-aweso...

youtube-summarizer

Extract transcripts from YouTube videos and generate comprehensive, detailed summaries using intelligent analysis frameworks

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2 scripts/Attention
AI & Machine Learningboshu2/agentops

inject

Inject relevant knowledge into session context from .agents/ artifacts. Triggers: "inject knowledge", "recall context", SessionStart hook.

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23
AI & Machine Learningmrgoonie/claudekit-skills

context-engineering

Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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2 scripts/Checked
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