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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/BEKO2210/Firstbrain --skill ai-ml命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
基于 SOC 职业分类
正在显示 SKILL.md
| name | ai-ml |
| description | AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | ml-data-science |
| risk | safe |
| source | personal |
| tags | ["skill","ai-ml","ml-data-science"] |
Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.
Use this workflow when:
ai-product - AI product developmentai-engineer - AI engineeringai-agents-architect - Agent architecturellm-app-patterns - LLM patternsUse @ai-product to design AI-powered features
Use @ai-agents-architect to design multi-agent system
llm-application-dev-ai-assistant - AI assistant developmentllm-application-dev-langchain-agent - LangChain agentsllm-application-dev-prompt-optimize - Prompt engineeringgemini-api-dev - Gemini APIUse @llm-application-dev-ai-assistant to build conversational AI
Use @llm-application-dev-langchain-agent to create LangChain agents
Use @llm-application-dev-prompt-optimize to optimize prompts
rag-engineer - RAG engineeringrag-implementationembedding-strategies - Embedding selectionvector-database-engineer - Vector databasessimilarity-search-patterns - Similarity searchhybrid-search-implementation - Hybrid searchUse @rag-engineer to design RAG pipeline
Use @vector-database-engineer to set up vector search
Use @embedding-strategies to select optimal embeddings
autonomous-agents - Autonomous agent patternsautonomous-agent-patterns - Agent patternscrewai - CrewAI frameworklanggraph - LangGraphmulti-agent-patterns - Multi-agent systemscomputer-use-agents - Computer use agentsUse @crewai to build role-based multi-agent system
Use @langgraph to create stateful AI workflows
Use @autonomous-agents to design autonomous agent
ml-engineer - ML engineeringmlops-engineer - MLOpsmachine-learning-ops-ml-pipeline - ML pipelinesml-pipeline-workflow - ML workflowsdata-engineer - Data engineeringUse @ml-engineer to build machine learning pipeline
Use @mlops-engineer to set up MLOps infrastructure
langfuse - Langfuse observabilitymanifest - Manifest telemetryevaluation - AI evaluationllm-evaluation - LLM evaluationUse @langfuse to set up LLM observability
Use @evaluation to create evaluation framework
prompt-engineering - Prompt securitysecurity-scanning-security-sast - Security scanningdevelopment - Application developmentdatabase - Data managementcloud-devops - Infrastructuretesting-qa - AI testing