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Found 1,684 Skills
Implementing providers for Beluga AI v2 registries. Use when creating LLM, embedding, vectorstore, voice, or any other provider.
Use when implementing RL algorithms, training agents with rewards, or aligning LLMs with human feedback - covers policy gradients, PPO, Q-learning, RLHF, and GRPOUse when ", " mentioned.
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
AI and ML expert including PyTorch, LangChain, LLM integration, and scientific computing
Optimizes markdown documents for token efficiency, clarity, and LLM consumption. Use when (1) a markdown file needs streamlining for use as LLM context, (2) reducing token count in documentation without losing meaning, (3) converting verbose docs into concise reference material, (4) improving structure and scannability of markdown files, or (5) preparing best-practices or knowledge docs for agent consumption.
Epistemic verification framework for AI-generated assertions. Requires evidence before acting on LLM claims about code behavior, system state, API responses, or factual statements. Use when an AI agent makes claims that will drive decisions, before acting on research results, or when an agent asserts something is true without showing evidence.
MixSeek-Coreで利用可能なLLMモデルの一覧を表示します。「使えるモデル」「モデル一覧」「どのモデルがある」「モデルを取得」「APIからモデル」といった依頼で使用してください。API経由でプロバイダー別のモデル情報を動的取得し、推奨設定、互換性情報を提供します。
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
Access real-time, continuously refreshed investment context through the Primary Logic External API under /v1. Use when asked to power Codex, Claude Code, OpenClaw, or custom agents with LLM-ranked relevance and impact signals from podcasts, articles and news, X/Twitter, Kalshi, Polymarket, earnings calls, filings, and other monitored sources across public and private companies for decision support or user-controlled trading workflows.
Diseño de prompts para LLMs: system prompts, few-shot examples, chain-of-thought, RAG, structured outputs.
Learn how to manage conversation context in AMCP to avoid LLM API errors from exceeding context windows. This skill covers SmartCompactor strategies, token estimation, configuration, and best practices.