197 skills · sorted by relevance
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots. Triggers: "voice live", "real-time voice", "VoiceLiveClient", "VoiceLiveSession", "voice assistant .NET", "bidirectional audio", "speech-to-speech".
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Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
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Have a fast, conversational analysis with the AWS DevOps Agent. Use for cost optimization, architecture review, topology mapping, knowledge / runbook discovery, security audits, dependency questions, and quick diagnostics — anything that needs a 5-30 second answer rather than a 5-8 minute deep investigation. Trigger words include cost, optimize, review, architecture, topology, what runbooks, show me, compare, audit, what if.
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spaCy NER model training and entity extraction for conversational AI
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NVIDIA NeMo Guardrails configuration for conversational safety and control
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PII detection and redaction utilities for privacy-compliant conversational AI
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Microsoft AutoGen multi-agent configuration for conversational AI systems
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Design conversational assistants with context management, response generation, and LLM integration. Use when building chatbots or planning assistant application architecture.
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Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.
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Guided, conversational walkthrough that explains code, UI, UX, features, or .copilot-tracking artifacts with navigable evidence links, a deep review before explaining, and a reconciled decisions-and-changes ledger. Use when the user wants to understand how something works or why it was changed.
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Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.
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Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to CSV or whatever output format the user prefers. Supports calibration mode for prompt refinement.
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Render a two-host podcast ad from a brand config and dialogue script. Includes the working planner, full-frame and split-screen assembly, captions from measured character timings, brand end card, approved paid-step adapters, and 65 quality-check falsification cases. Use for short conversational ads with two stable hosts in one room.
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Conversational search runtime: send messages, keep sessions consistent, and verify retrieval behavior and responses.
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Use after a phase executes, when the user wants to test the built features themselves. Runs one conversational UAT test at a time into {phase}-UAT.md and, when something fails, diagnoses it and queues fix plans for /gsd-execute-phase.
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Conversational intake for cold email campaigns. Interviews the user about their business, ICP (Ideal Customer Profile), offer, lead magnets, and hard vs soft filters. Outputs a structured `client-profile.yaml` that every other skill in the repo reads from. Use at the very start of a new campaign or when onboarding a new business. Triggers on "onboard me", "set up my ICP", "new client", "new business intake", "help me define my ICP".
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Build a WhatsApp marketing strategy — WhatsApp Business setup, broadcast campaigns, automated flows, customer service, drip sequences, and conversational marketing. Use when the user says "WhatsApp marketing", "WhatsApp Business", "WhatsApp campaigns", "WhatsApp broadcasts", "WhatsApp automation", "WhatsApp drip", "market on WhatsApp", "WhatsApp for business", "WhatsApp chatbot", "WhatsApp customer service", "WhatsApp API", "messaging marketing India", or wants to use WhatsApp as a marketing and customer communication channel. Especially relevant for brands in India, the Middle East, Southeast Asia, Brazil, and Africa.
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BDD requirements discovery methodology - Example Mapping, Three Amigos, conversational patterns, Given-When-Then translation, and collaborative specification
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Translates or rewrites Chinese text into English using a 'critical dialogue' tone, characterized by engaging questions, analytical commentary, and a conversational flow while preserving citations and facts.
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Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
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Applies QML best practices when producing or working with QML source code. Use whenever QML code is the primary subject: writing, reviewing, fixing, refactoring, optimizing, or debugging QML files, components, or bindings. Do NOT trigger for purely conversational QML questions where no code is produced or examined (e.g. "explain how anchors work").
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Guide a user conversationally through generating a new ad video from a template video and a user-provided product image. The host agent performs video understanding, detailed analysis, and rewrite planning; BytePlus Seedance is used only for final video generation with local product images as generation references.
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Use when you need to generate Architecture Decision Records (ADRs) for a Java project through an interactive, conversational process that systematically gathers context, stakeholders, options, and outcomes to produce well-structured ADR documents. This should trigger for requests such as Generate ADR; Create Architecture Decision Record; Document architecture decision; Architecture Decision Record for Java. Part of Plinth Toolkit
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