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.
agents_md
AGENTS.md dosyaları oluşturma, monorepo yapılandırma ve agent instruction yönetimi rehberi.
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.
litellm
When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.
oracle-codex
This skill should be used when the user asks to "use Codex", "ask Codex", "consult Codex", "Codex review", "use GPT for planning", "ask GPT to review", "get GPT's opinion", "what does GPT think", "second opinion on code", "consult the oracle", "ask the oracle", or mentions using an AI oracle for planning or code review. NOT for implementation tasks.
