「ingestion」の検索結果

Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow

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Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.

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Build event streaming applications using Azure Event Hubs SDK for JavaScript (@azure/event-hubs). Use when implementing high-throughput event ingestion, real-time analytics, IoT telemetry, or event...

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Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.

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Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

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Use when working with Coralogix — coralogix observability platform for log analytics, metrics, tracing, security, and alerting. Covers log querying with DataPrime, metric exploration, alert management, parsing rules, and data usage insights. Use when searching Coralogix logs, investigating application errors, managing alert rules, or analyzing log ingestion patterns.

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Use when working with Chronicle — google Chronicle SIEM security operations, threat detection, asset investigation, and log analysis. Covers UDM search queries, detection rule management, asset and IOC lookups, reference list management, and retrohunt execution. Use when investigating security events, reviewing detection rules, searching for IOCs, or analyzing Chronicle data ingestion health.

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Use when working with Druid — apache Druid datasource analysis, ingestion task management, supervisor status, segment management, and query performance.

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NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering workflows.

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Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.

日本語の概要は準備中です。原文の説明を表示しています。

Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.

日本語の概要は準備中です。原文の説明を表示しています。

High-throughput event streaming and real-time data ingestion.

日本語の概要は準備中です。原文の説明を表示しています。

bu skill covers Dağıt:ing Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL Tespit queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

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This skill covers deploying Microsoft Sentinel as a cloud-native SIEM and SOAR platform for centralized security operations. It details configuring data connectors for multi-cloud log ingestion, writing KQL detection queries, building automated response playbooks with Logic Apps, and leveraging the Sentinel data lake for petabyte-scale threat hunting across AWS, Azure, and GCP security telemetry.

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Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Convert external documents — specs, contracts, reports — to markdown for knowledge base ingestion.

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Extract clean title, summary, markdown sections, and source metadata from a public documentation page for RAG ingestion.

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Convert a document to clean markdown suitable for chunking and embedding in a RAG pipeline.

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Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.

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High-throughput event streaming and real-time data ingestion.

日本語の概要は準備中です。原文の説明を表示しています。