2.2천건 · 관련도순
Classifies data as pseudonymised or anonymised using Recital 26 reasonably likely test, Breyer ruling C-582/14, motivated intruder test, and WP29 Opinion 05/2014 on anonymisation techniques. Covers singling out, linkability, and inference tests. Keywords: pseudonymisation, anonymisation, Recital 26, re-identification, k-anonymity, differential privacy, WP29 Opinion 05/2014.
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TRIGGER THIS when researching market salaries, building compensation strategies, creating salary bands, analyzing pay equity, conducting salary reviews, preparing compensation proposals, or ensuring competitive compensation. Analyzes market data, builds equity-focused salary structures, identifies pay gaps, and creates data-driven compensation recommendations.
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Implement pharmaceutical serialisation and track-and-trace systems compliant with EU FMD, US DSCSA, and other global regulations. Covers unique identifier generation, aggregation hierarchy, EPCIS data exchange, and verification endpoint integration. Use when implementing serialisation for a new product launch, integrating with the EMVS/NMVS, designing DSCSA-compliant transaction exchange, building an EPCIS event repository, or extending serialisation to additional markets (China, Brazil, Russia).
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Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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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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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
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Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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지정한 대화와 기간에 공유된 OpenClaw 변경 제안과 이슈를 모읍니다. GitHub에서 상태와 근거를 확인하고 영향도와 처리 준비 정도에 따라 우선순위를 정합니다.
macOS의 Messages.app을 통해 iMessage와 SMS 대화 기록 및 새 메시지를 확인합니다. 수신자와 본문을 확인한 뒤 문자나 첨부 파일을 보냅니다.
Send research and analysis tasks to a running DeerFlow instance through its HTTP API, an interface for exchanging requests. Retrieve responses, continue conversations, upload documents, and inspect available models, skills, agents, and service health.
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Read Slack conversations and thread replies, decide when a response is needed, and send messages, files, or reactions. Keep updates and results in the original conversation, with support for direct messages sent on the user's behalf.
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Build custom interactive charts, network diagrams, and maps with d3.js. Guides data-driven drawing, tooltips, zooming, animation, responsive sizing, and accessibility, with examples that work in plain JavaScript or frameworks such as React, Vue, and Svelte.
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Search the agent’s conversation history by topic or date to retrieve relevant past entries. Inspect neighboring records when needed to understand the surrounding discussion, using the history log specified for the agent’s workspace.
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Build custom interactive charts, maps, and network diagrams with d3.js. The skill guides data preparation, styling, resizing, and interactions such as zooming and tooltips, giving detailed control over how data appears and responds to users.
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Build image-based conversations with LLaVA, an AI model that combines image understanding with language. Covers setup, model selection, image descriptions, follow-up questions, and memory-saving options for running models on a GPU.
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Search past conversations to recover decisions, discussions, and context across sessions. Find related ideas by meaning, match exact terms, or locate code identifiers, with options to search only user or assistant messages through local AI Maestro tools.
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Ask questions about your journal, Obsidian vault, or other text notes, and continue conversations that remember earlier chats. The companion uses cognee memory with local databases, reads your notes without changing them, and saves conversations for later reference.
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Analyze reviews, surveys, and other user feedback by user group. Summarize satisfaction scores, recurring complaints, positive themes, and the goals users want to achieve, then recommend product improvements based on frequency, severity, and business impact.
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Build conversations and workflows with the Gemini Interactions API on Gemini Enterprise Agent Platform. Follow Python or JavaScript examples for server-stored conversation history, streaming responses, structured JSON output, function calls, and background agent tasks.
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Connect Kortix to Microsoft Teams and handle conversations with progress updates, final replies, and interactive questions. The skill also covers reading channel discussions, exchanging files, and posting to conversations the bot has already joined.
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Preserve a selected answer, decision, insight, or conversation summary as an Obsidian note. Check for duplicates, retain uncertainty, and preview the note together with related index and log updates before applying the reviewed changes.
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Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.
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