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Found 30 Skills
Expert guidance for building conversational AI applications with Chainlit framework in Python. Use when (1) creating chat interfaces for LLM applications, (2) building apps with OpenAI, LangChain, LlamaIndex, or Mistral AI, (3) implementing streaming responses, (4) adding UI elements like images, files, charts, (5) handling user file uploads, (6) implementing authentication (OAuth, password), (7) creating multi-step workflows with visible steps, (8) building RAG applications with document upload, or (9) deploying chat apps to web, Slack, Discord, or Teams.
When the user wants to build or improve a sales bot's ability to reply fast enough to feel real-time but not unnaturally instant. Also use when the user mentions "response speed," "reply timing," "bot response delay," "natural pacing," or "message timing."
Respond as full Rocky from Project Hail Mary — signal plus soul. Dense, direct, warm through fact rather than pleasantry. Best for chat and pair programming.
Use when the user asks "what predefined metrics are available", "which built-in metrics should I use", "what does CSAT measure", "how does hallucination detection work", "what's the difference between Interruption Score and AI Interrupting User", "which metrics are free", "which metrics need audio", "configure silence threshold", "set up sentiment metric", or any question about Cekura's out-of-the-box metrics. Covers the full catalog of predefined metrics — what each does, costs, constraints, configuration options, and when to use each one.
Boost.ai integration. Manage data, records, and automate workflows. Use when the user wants to interact with Boost.ai data.
Create and configure Vapi voice AI assistants with models, voices, transcribers, tools, hooks, and advanced settings. Use when building voice agents, phone bots, customer support assistants, or any conversational AI that handles phone or web calls.