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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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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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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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Extract clean Markdown from a web page URL using Defuddle. The skill provides a command example for an Obsidian source-ingestion workflow and identifies Sources/Web/ as the typical destination for web source material.
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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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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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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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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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Develop and troubleshoot plugins that connect external memory services to Nexent. Define plugin settings, implement search and ingestion, map errors, test with simulated network responses, and verify deployment, authentication, and runtime monitoring.
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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.
中文简介正在准备中。当前显示原文说明。
Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
中文简介正在准备中。当前显示原文说明。
Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
中文简介正在准备中。当前显示原文说明。
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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