'ingestion' 검색 결과

Helps developers send conversion and event data to Google Ads, Google Analytics, and Floodlight through the Data Manager API. It covers destination setup, data formatting, validation, migration from other APIs, and checking processing results.

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• Implement offline conversion uploads to Google Ads
• Send recommended or custom GA4 events
• Migrate event ingestion from another Google API

Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API. Triggers: "azure-monitor-ingestion", "LogsIngestionClient", "custom logs", "DCR", "data collection rule", "Log Analytics".

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

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• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Prepare a local development environment for the Data Manager API with authentication and library installation guidance. It covers five programming languages and companion utilities for formatting, hashing, and encrypting user identifiers before ingestion.

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• Preparing a local environment for the Data Manager API
• Installing client and identifier utility libraries
• Configuring local credentials and access scopes

Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). Triggers: "LogsIngestionClient java", "azure monitor ingestion java", "custom logs java", "DCR java", "data collection rule java".

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Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).

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Open-format data ingestion for NV oOS Pro toolkits — the reusable pattern behind the F&B data-ingestion track (Proposal 078). Covers structural (header) matching over filename mapping, the configurable table_map + header-matcher + name-guess resolution order and the slug-first regression, the multi-format loader (CSV/TSV/JSON/JSONL/XLSX/ODS/PDF/photo), the Observation + terminology-map layer (vitals/FHIR analogue) with raw preservation, dedupe/retention/out-of-band banding, complete-by-default/resumable (offset) imports, and the read-only ingest-review dashboard anatomy (edit_posts review vs manage_options write, dry-run-first). Use when adding or extending data ingestion to a toolkit, ingesting heterogeneous owner files, wiring XLSX/PDF/photo imports, building an ingest/review dashboard, or debugging partial/truncated imports.

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Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

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• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Convert a research paper into structured files covering its problem, claims, concepts, experiments, and figure or table evidence. Validate the generated directory, ingest it into OpenViking, and check that the target resource was created.

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• Import a paper PDF into OpenViking
• Organize a paper's claims and experimental evidence
• Preserve figures and tables as evidence files

Prepare readable Markdown from HTTPS article pages using an optional external Defuddle cleaner. The workflow checks URLs, requires network consent and executable review, previews extracted content, and can retain the cleaned source separately from wiki ingestion.

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• Extract readable Markdown from a web article
• Prepare a web source for later wiki ingestion
• Save cleaned content with source metadata

Analyze Axiom data ingestion and query usage to identify unused columns and field values. Build cost dashboards and spike monitors, then summarize recommendations for dropping or sampling data with estimated savings.

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• Find unused data that contributes to Axiom costs
• Track ingestion trends and projected spending
• Set up alerts for ingestion and query cost spikes

Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.

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Guides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the API. Don't use for implementing audience or event ingestion logic (use the data-manager-api-audience-ingestion or data-manager-api-event-ingestion skills instead).

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Use when enabling Data Cloud (Data 360) in an org, creating data spaces, assigning permission sets, registering ingestion API sources, or configuring activation targets. Triggers: 'enable data cloud', 'data spaces setup', 'data cloud permission sets', 'ingestion API connected app', 'activation target setup', 'data cloud licensing', 'dedicated home org vs existing org'. NOT for data streams or DLO-to-DMO mapping — use data/data-cloud-data-streams. NOT for identity resolution rulesets — use admin/data-cloud-identity-resolution.

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Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.

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Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.

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Security checks for repository ingestion — scans repos for poisoned examples, dependency and supply-chain attacks, file/path traversal, format-based attacks, and enforces quarantine-before-commit. Load as part of the secure-* skill sequence whenever an agent reads, ingests, or learns from a GitHub repository. Also load when the user asks to check a repo for poisoned code, scan dependencies, verify supply chain safety, check for path traversal, scan repo files for attacks, or audit a repo before ingestion. Covers Issues 3, 4, 7, 8 from the agent security threat model: poisoned training data, dependency attacks, file/path attacks, and format-based attacks.

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Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a central platform for collecting, analyzing, and sharing cyber threat information. Connect MISP, OpenCTI, TheHive, and Cortex to import feeds, add context to suspicious indicators, manage cases, and track platform activity.

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• Build a central threat intelligence platform
• Automate threat feed ingestion
• Add context to suspicious IP addresses and domains

Build automated pipelines that collect threat indicators, such as malicious IP addresses and file hashes, from multiple feeds. Standardize formats, remove duplicates, assess confidence, and distribute indicators to security monitoring systems for matching and alerts.

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• Automating threat feed ingestion
• Standardizing and deduplicating threat indicators
• Distributing indicators to multiple monitoring systems

Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.

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• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps