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Found 516 Skills
Expert knowledge for Azure AI Personalizer development including troubleshooting, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when tuning exploration/apprentice mode, single vs multi-slot calls, model export, quotas, or local inference SDK, and other Azure AI Personalizer related development tasks. Not for Azure AI services (use microsoft-foundry-tools), Azure AI Search (use azure-cognitive-search), Azure AI Metrics Advisor (use azure-metrics-advisor), Azure AI Anomaly Detector (use azure-anomaly-detector).
Manages persistent Knowledge Graph for specifications. Caches agent discoveries and codebase analysis to remember findings across sessions. Validates task dependencies, stores patterns, components, and APIs to avoid redundant exploration. Use when: you need to cache analysis results, remember findings, reuse previous discoveries, look up what we found, spec-to-tasks needs to persist codebase analysis, task-implementation needs to validate contracts, or any command needs to query existing patterns/components/APIs.
Design and conduct mixed methods research using convergent, explanatory sequential, or exploratory sequential strategies with genuine integration of qualitative and quantitative strands. Use this skill when the user needs to choose a mixed methods design, integrate qualitative and quantitative data at design, methods, or interpretation levels, justify mixing on pragmatist grounds, or when they ask 'which mixed methods design should I use', 'how do I integrate qual and quant findings', or 'is running both qual and quant enough to be mixed methods'.
Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.
Score a single draft against the rubric. **Output only to the console, no file writing, no prediction**. Trigger phrases: "Score this [path]"/"score this [path]"/"Score this draft"/"Let's score first". It's a lightweight exploratory action before cheat-predict.
Invoke IMMEDIATELY via python script when user requests codebase understanding, architecture comprehension, or repository orientation. Do NOT explore first - the script orchestrates exploration.
Analyzes events through futures lens using scenario planning, trend analysis, weak signals, drivers of change, and forecasting methods (exploratory, normative, backcasting). Provides insights on possible futures, emerging trends, disruptive forces, strategic foresight, and alternative scenarios. Use when: Strategic planning, emerging trends, technology assessment, long-term planning, uncertainty navigation. Evaluates: Trends, weak signals, drivers of change, plausible futures, strategic options, uncertainty ranges.
Z.AI CLI providing: - Vision: image/video analysis, OCR, UI-to-code, error diagnosis (GLM-4.6V) - Search: real-time web search with domain/recency filtering - Reader: web page to markdown extraction - Repo: GitHub code search and reading via ZRead - Tools: MCP tool discovery and raw calls - Code: TypeScript tool chaining Use for visual content analysis, web search, page reading, or GitHub exploration. Requires Z_AI_API_KEY.
This skill should be used when conducting comprehensive research on any topic using the OpenAI Deep Research API. It automates prompt enhancement through interactive clarifying questions, saves research parameters, and executes deep research with web search capabilities. Use when the user asks for in-depth analysis, investigation, research summaries, or topic exploration.
Exploratory discussion pattern for unsolved problems. Replicate the thinking of Staff+ engineers: "When there's no clear answer, expose blind spots by confronting diverse perspectives." True multi-agent discussions where experts directly engage with each other through team-based + messaging architecture.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, "just one quick fix" seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
Interactive web apps for data science: Streamlit, Panel, and Gradio. Use for prototyping ML models, creating data exploration dashboards, and sharing insights with non-technical stakeholders.