Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Add descriptions for new models available in the HuggingFace router to chat-ui's prod.yaml and dev.yaml.
Fetch models from router
WebFetch https://router.huggingface.co/v1/models
Extract all model IDs from the response.
Read current configuration
chart/env/prod.yamlMODELS JSON array in envVarsIdentify missing models Compare router models with prod.yaml. Missing = in router but not in prod.yaml.
Research each missing model For each missing model, search the web for its specifications:
Write descriptions Match existing style:
Examples:
"Flagship GLM MoE for coding, reasoning, and agentic tool use.""MoE agent model with multilingual coding and fast outputs.""Vision-language Qwen for documents, GUI agents, and visual reasoning.""Mobile agent for multilingual Android device automation."Update both files Add new models at the TOP of the MODELS array in:
chart/env/prod.yamlchart/env/dev.yamlFormat:
{ "id": "org/model-name", "description": "Description here." }
Commit changes
git add chart/env/prod.yaml chart/env/dev.yaml
git commit -m "feat: add descriptions for N new models from router"
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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