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Found 1,055 Skills
Apply Model-First Reasoning (MFR) to code generation tasks. Use when the user requests "model-first", "MFR", "formal modeling before coding", "model then implement", or when tasks involve complex logic, state machines, constraint systems, or any implementation requiring formal correctness guarantees. Enforces strict separation between modeling and implementation phases.
This skill should be used when the user asks to "humanize text", "make this sound more human", "detect AI writing", "fix AI-sounding content", "copy edit for naturalness", "rewrite to sound less robotic", "check if this sounds AI-generated", or needs guidance on making written content feel authentically human while preserving its original tone.
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
Form a high-level investment committee consisting of three virtual experts modeled after legendary investors (Buffett, Wood, Druckenmiller) to conduct independent multi-round adversarial debates. True independent thinking is achieved through physically isolated Gemini API calls, and final resolutions are formed via voting. Use when evaluating investment decisions, reviewing stock research reports, or seeking multi-perspective analysis on public companies.
Configure LangChain local development workflow with hot reload and testing. Use when setting up development environment, configuring test fixtures, or establishing a rapid iteration workflow for LangChain apps. Trigger with phrases like "langchain dev setup", "langchain local development", "langchain testing", "langchain development workflow".
Tavily AI search API integration via curl. Use this skill to perform live web search and RAG-style retrieval.
module1~6 학습 내용을 복습하고 개념 연결성, 적용 판단력, 실행 계획까지 종합 점검하는 마무리 스킬.
Many turns in one call. Instant communication. No round-trips.
Query fan-out coverage for AI visibility. Covers semantic variation analysis and sub-question targeting.
Run application agents through SpendGuard with strict hard budget caps. Use when setting up `spendguard-sidecar`, creating agent IDs, setting or topping budgets, sending OpenAI/Grok/Gemini/Anthropic calls through SpendGuard endpoints, and troubleshooting budget enforcement errors like insufficient budget, in-flight lock conflicts, missing `x-cynsta-agent-id`, or remote pricing signature failures.
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".