Skill UI

This guide details the comprehensive process of using the Linux Audit framework (auditd) to detect advanced security threats. It covers deploying specific rules to monitor critical system files, detecting unauthorized privilege escalation, process injection, and suspicious system calls. Users can then leverage ausearch and aureport to efficiently query, reconstruct timelines, and summarize findings for forensic analysis and compliance auditing.

This skill details the deployment and advanced querying techniques for Arkime (Moloch), enabling comprehensive full packet capture network traffic analysis. Users can leverage the Arkime API v3 to search connections, download PCAPs, detect sophisticated C2 beaconing, and analyze connection patterns and anomalies across DNS, HTTP, and TLS traffic. Essential for security monitoring and network forensics.

Automate Amplitude product analytics by orchestrating Composio's Rube MCP toolkit to send events, resolve user activity, manage cohorts, and sync identities while always querying RUBE_SEARCH_TOOLS for current schemas.

This utility collects comprehensive diagnostic evidence for Algolia support tickets or internal troubleshooting. It gathers index statistics, API key access control lists (ACLs), recent query logs, and network diagnostic information using API endpoints and shell scripting. Use it when diagnosing search performance or unexpected service behavior.

This skill provides a complete solution for deploying Glean custom connectors as scheduled jobs on container platforms (e.g., Cloud Run). It covers building a production-ready Docker image to manage document ingestion, custom data source connectivity, and search query execution. Includes robust health checks and rolling update strategies to ensure continuous, high-availability indexing for enterprise search environments.

This workflow provides a comprehensive solution for keeping an Algolia search index synchronized with external source data. It covers critical tasks such as full reindexing (zero downtime), efficient partial updates to minimize latency, managing synonyms for better search relevance, and setting up advanced query rules. Ideal for maintaining robust, real-time search capabilities for SaaS applications.

A comprehensive guide to reducing Algolia operational costs. This guide details the pricing structure based on search requests and records, providing advanced techniques such as implementing virtual replicas instead of standard replicas, utilizing multi-query search for request batching, and implementing client-side caching. It also includes strategies for auditing, cleaning up unused indices, and monitoring usage via the Analytics API.

A comprehensive guide for migrating search functionality from alternatives like Elasticsearch, Typesense, or Meilisearch to Algolia. It details the necessary steps, including data migration, query translation (e.g., ES to Algolia), and implementing a zero-downtime swap using the Strangler Fig pattern.

This tool provides comprehensive observability for enterprise search systems (Glean). It tracks critical metrics such as search latency (p95), zero-result query rates, indexing throughput, and stale content ratios. By monitoring connector sync status and detecting indexing degradation, it allows teams to proactively maintain search relevance and ensure sub-second response times, preventing silent data quality decay.

A comprehensive tool for interacting with Notion via its API. It allows users to perform workspace-wide searches, run complex queries against structured databases using compound filters (AND/OR), retrieve specific pages, and deeply traverse and extract all nested block content. Supports pagination for handling large datasets, enabling full data visibility and structured data extraction.

A comprehensive guide and implementation pattern for querying, searching, and paginating OneNote content using the Microsoft Graph API. This skill addresses the limitations of the standard OneNote search endpoint, providing robust methods for cross-notebook querying, advanced OData filtering on metadata, and client-side full-text indexing for body content. Ideal for building complex knowledge retrieval systems that handle large, distributed result sets.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.