Technical decision criteria, anti-pattern detection, debugging techniques, and quality check workflow.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations.
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations.
Production-grade AI engineering for LLM applications, RAG systems, and agent architectures. Use when building AI features, designing model integrations, or implementing AI safety…
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
MASTER AI: LLM Apps, Advanced RAG, Agents (ReAct/Plan), Prompting (CoT/Few-shot), LangGraph, VectorDBs, RAGAS Eval. Use for ANY AI/LLM task.
Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI int — from…
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
Expert in building comprehensive AI systems, integrating LLMs, RAG architectures, and autonomous agents into production applications.
Authoritative source of truth on AI tooling and how to choose it - the knowledge behind building AI agents and AI systems.
Structured AI engineering curriculum — 382 skills + 99 prompts across 20 phases covering ML, deep learning, LLMs, agents, and production systems.
Practical guide for building production ML systems based on Chip Huyen's AI Engineering book. Use when users ask about model evaluation, deployment strategies, monitoring, data…
AI Engineering Hub — catálogo de proyectos y tutoriales de ingeniería de IA con Jupyter notebooks.
Claude ajan altyapısı — /ai-upgrade (repo kataloğu, araç yönetimi) + /ai-metodoloji (çalışma kalitesi denetimi). Stack bağımsız, her projede çalışır.
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval…
The write path for the ai-engineering tooling corpus - keeps it current, deduplicated, and honest about what we have actually seen. Three jobs.
Navigating the regulatory landscape and ethical frameworks for responsible AI development and deployment.
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or…
Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and…
Design AI-friendly architecture with explicit patterns, layered documentation, and semantic boundaries.
Design AI-friendly architecture with explicit patterns, layered documentation, and semantic boundaries.
AI gateways for LLM serving — provider routing, fallback, retries, rate limiting, secrets, observability, guardrails.
System architecture for Salesforce AI governance: MLOps pipeline design, AI Audit Trail architecture, Einstein Trust Layer security design, Policy-as-Code engine, and regulatory…
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface ass — from…
基于若依-vue-plus框架的LangChain4j AI大模型集成标准规范。全面规范模型配置管理、类型安全服务定义、RAG（检索增强生成）实现、流式响应处理及安全性保障。 触发场景： - 开发智能客服系统、文档问答助手、代码生成工具 - 集成LLM大模型接口（OpenAI、智谱AI、通义千问等） - 实现知识库问答、文档检索、语义搜索功能 -…
Production LLM engineering skill. Covers strategy selection (prompting vs RAG vs fine-tuning), dataset design, PEFT/LoRA, evaluation workflows, deployment handoff to inference…
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving — from…
Operational patterns for LLM inference: latency budgeting, tail-latency control, caching, batching/scheduling, quantization/compression, parallelism, and reliable serving — from…
Expert guide for integrating Large Language Models (LLMs), RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, arsitektur RAG, vector database,…
Partner tutor untuk latihan coding manual. Membimbing bertahap (Socratic + debugging). Maksimal 3 putaran feedback atas kode user sebelum memberikan solusi utuh.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Operational patterns, templates, and decision rules for time series forecasting (modern best practices): tree-based methods (LightGBM), deep learning (Transformers, RNNs),…
Intelligent AI model router that automatically switches between two configured models (local for simple tasks, cloud for complex ones).
Development conventions and patterns for Ai_Movara. JavaScript project with freeform commits.
Use when an approved system design exists and the product includes LLM, agent, retrieval, classifier, extractor, or model-driven automation components that need architectural…
Use when designing AI agent observability architecture, integrating mission-control dashboards, or defining AI-specific KPI schemas
Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code…
Autonomous development agent for Astro 6 projects. Handles end-to-end feature implementation with minimal human intervention.
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety…
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed…
Use when designing or auditing a Salesforce data architecture to support AI features — Einstein, Agentforce, Data Cloud, or custom ML models.
AI-powered refactoring assistant - get code improvement suggestions, refactoring patterns, and best practices. Each call charges 0.001 USDT via SkillPay.
中文优先：用于AI回归测试相关任务，帮助识别、设计、实现或验证对应工作流。English keywords: Regression testing strategies for AI-assisted development.
Retrieval architecture for AI applications — choosing and combining vector RAG, PageIndex (vectorless PDF tree-search), and precision embedding models.
Use when building, integrating, debugging, or reviewing an AI-powered reporting, dashboard, or data-visualization application where AI generates report structure.
AI development governance suite that splits work into four documented stages — requirement analysis, structure design, modification governance, and acceptance — so every AI task…
Optional AI SDLC architecture workflow. Use when an AI assistant needs to define system boundaries, components, interfaces, architectural constraints, alternatives, decisions,…
AI SDLC code review workflow. Use when an AI assistant is asked to review a diff, PR, branch, commit, staged changes, or completed implementation against SDD requirements, tests,…
Use after QA strategy and test-case synthesis to build the requirements-to-test traceability matrix, identify missing coverage and test blockers, and score readiness for QA…
AI SDLC repository spec-driven development workflow. Use when an AI assistant receives a medium or large feature, refactor, API change, architecture change, provider integration…
Use when QA scope and strategy are defined and you need to generate detailed, executable test cases plus smoke, regression, and user acceptance suites tied to requirements, roles,…
AI SDLC test-case-driven testing workflow. Use when an AI assistant is asked to derive test cases, create a test plan, expand coverage, or write tests from explicit scenarios…
Use when requirements are testable enough and you need to define QA scope, coverage priorities, test strategy, suite intent, test data needs, environment dependencies, and…
Use this when: design an AI system, RAG vs fine-tuning, my agent keeps looping, architect a multi-agent system, which LLM should I use, context window keeps overflowing, add…
AI SDK v5 tool creation patterns for this project. Factory functions, Zod schemas, budget tracking, rate limiting, caching, timeout handling.