agent_orchestration
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
audience_intelligence
Analyzes target audience demographics, psychographics, behaviors, and platform preferences to inform influencer selection and campaign strategy. Essential foundation for effective influencer marketing.
cold_email
Generate personalized cold email sequences (7-14 emails) with A/B test subject lines, follow-up timing recommendations, and integrated social proof. Creates multi-touch campaigns optimized for response rates. Use when users need outbound email campaigns, sales sequences, or lead generation emails.
competitor_intelligence
Analyzes competitor SEO and GEO strategies including their ranking keywords, content approaches, backlink profiles, and AI citation patterns. Reveals opportunities to outperform competition.
email_composer
Profesyonel e-posta yazma, business communication ve template oluşturma rehberi.
llm_evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
multi_agent_patterns
Çoklu agent mimarisi tasarımı, orchestration patterns ve agent collaboration rehberi.
prompt_engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
prompt_optimizer
Mevcut promptların token kullanımını ve başarı oranını optimize etme.
rag_architecture
Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
rag_implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
tailwind_mastery
Tailwind CSS v4, design tokens, responsive patterns ve utility-first CSS best practices.
agents_md
AGENTS.md dosyaları oluşturma, monorepo yapılandırma ve agent instruction yönetimi rehberi.
moai-ref-llm-security
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agent-architecture-audit
Audit LLM and agent applications for wrapper regressions, prompt or memory contamination, tool discipline failures, hidden repair loops, and output rendering corruption. Use before shipping agent features or when an agent works in a direct model call but fails inside the product.
llm-structured-output
Design prompts, schemas, validation, and recovery logic for reliable machine-readable model outputs. Use when generating JSON, typed objects, extraction results, tool arguments, or any output another system must parse safely.
batch-api-orchestrator
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extended-thinking-architect
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