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Azure Event Hubs TypeScript SDK

azure-eventhub-ts

sickn33/agentic-awesome-skills

This skill covers using the Azure Event Hubs SDK for TypeScript to build high-throughput event streaming applications. It includes real-time data ingestion, sending events to specific partitions, receiving events with checkpointing for production reliability, and handling batch processing options. Ideal for IoT telemetry and real-time data pipelines.

Azure Monitor Log Ingestion

azure-monitor-ingestion-java

sickn33/agentic-awesome-skills

This skill provides a Java SDK client library for sending custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE). It covers installation, client creation, synchronous and asynchronous uploads, error handling, and best practices for ingestion.

Azure Monitor Ingestion SDK Python

azure-monitor-ingestion-py

sickn33/agentic-awesome-skills

This Python SDK enables developers to send custom logs to Azure Monitor Log Analytics workspaces using the Logs Ingestion API. It supports authentication via DefaultAzureCredential, handles async/sync uploads, manages batching, and includes error handling for partial failures. Ideal for cloud observability and log management workflows.

Batch Extraction

batch-extraction

Extract text and data from many files at once with shared config, per-file overrides, bounded parallelism, and error recovery. Supports PDFs, DOCX, XLSX, and more, with JSON or text output for LLM ingestion.

ClickHouse Analytics Patterns

cc-skill-clickhouse-io

sickn33/agentic-awesome-skills

Patterns and best practices for ClickHouse analytics, including table design, query optimization, aggregations, materialized views, and bulk data ingestion for high-performance OLAP workloads.

Text Chunking for LLM

Guide for splitting extracted text into chunks for LLM context windows or RAG ingestion. Covers chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone chunk command.

Clarity Gate RAG Verification

sickn33/agentic-awesome-skills

Pre-ingestion verification skill for RAG systems that checks documents for epistemic clarity, flags unmarked assumptions, and generates CGDs to reduce hallucination risk before knowledge base ingestion.

Table Extraction from Documents

extracting-tables

Extract structured tabular data from PDFs, spreadsheets, and images using layout-aware detection and configurable table models, with markdown or JSON cell output for LLM ingestion and downstream processing.

YouTube Transcript to Markdown Vault Ingestion

sickn33/agentic-awesome-skills

This skill extracts full transcripts and rich metadata from a single YouTube video URL. It uses yt-dlp to download subtitles, cleans VTT timing markers, and pulls video details (title, channel, date). The output is a structured, queryable Markdown file, ideal for populating a personal knowledge vault and enabling downstream knowledge graph extraction or content repurposing.

Push Metadata and Logs to Monte Carlo

monte-carlo-push-ingestion

sickn33/agentic-awesome-skills

An expert agent designed to collect critical data assets—including metadata, data lineage, and query logs—from any data warehouse platform. It uses the push ingestion API to send this data directly to Monte Carlo. This method is essential for integrating data sources that cannot be reliably covered by standard pull collectors, such as custom lineage paths or historical query logs.

OneRoster CSV Validator

oneroster-csv-validator

sickn33/agentic-awesome-skills

Validates 1EdTech/IMS Global OneRoster v1.1 and v1.2 CSV roster sets for manifest integrity, bulk vs delta strictness, foreign key references, and encoding sanitization. Use for pre-flight checks before SIS/Clever/ClassLink ingestion.

Text Chunking for LLM Context

This skill enables splitting extracted text into optimized chunks for LLM context windows or RAG ingestion pipelines. It supports configurable chunk sizes, overlap settings, and various chunker types including text, markdown, YAML, and semantic splitting. Users can utilize token-based sizing for specific models or standalone commands for pre-existing text. Ideal for preparing documents for vector stores or summarization tasks.

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