Skill UI

OpenSearch is a distributed search and analytics engine integrated into KubeSphere's WizTelemetry Observability Platform. It is designed to efficiently store, index, and analyze vast amounts of observability data, including Kubernetes logs, auditing events, system metrics, and application notifications. Used for building comprehensive monitoring dashboards and deep operational insights.

This skill outlines the principle of mandatory, evidence-based verification before declaring any work (such as a feature, bug fix, or task) complete or successful. It mandates that assertions of success must be backed by repeatable evidence, such as passing test outputs, clean build logs, or successful execution reports. It is critical for PR reviews, task closure, and maintaining high engineering rigor.

This technique focuses on extracting highly distinctive and unique personal details—such as daily habits, specific routines, or unique life experiences—from personal blog posts. Instead of relying on generic topic keywords, it leverages these unique details to construct precise, quoted search queries, significantly improving the accuracy of information retrieval across vast web corpora.

WizTelemetry Logging is a comprehensive extension component within the KubeSphere Observability Platform. It provides robust capabilities for collecting, processing, and storing logs from container environments (stdout/stderr) and local disk files. Designed for system monitoring, it integrates with OpenSearch and supports advanced features like log query APIs, making it essential for maintaining system visibility and debugging in Kubernetes clusters.

Evolver is a sophisticated self-evolution engine designed for advanced AI agents. It analyzes runtime logs and operational history to autonomously identify performance bottlenecks, failures, and opportunities for improvement. It applies protocol-constrained evolution, communicating exclusively via a local Proxy Mailbox to interact with the EvoMap Hub for asset submission and task claiming.

Medchem Filtering Toolkit is a Python library offering drug-likeness heuristics, structural alert and PAINS filters, group detection, catalogs, and complexity scoring to triage compound libraries and prioritize leads in drug discovery workflows.

A comprehensive set of rules and constraints designed to optimize AI interaction in technical domains. It guides the agent on intelligent input filtering (handling logs, Git, codebases), resolving ambiguity, and generating surgically precise, structured outputs. Ideal for complex debugging, refactoring, and long-session development tasks.

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.

A comprehensive tool designed to collect all diagnostic evidence for troubleshooting failed or problematic Apify Actor runs. This bundle gathers run metadata, detailed logs, dataset samples, usage statistics, and environment information, making it essential for preparing detailed support tickets or performing in-depth debugging.

This comprehensive bundle collects all necessary diagnostic information for Bright Data troubleshooting. It captures proxy connectivity details, zone status, response headers, and relevant error logs. Use this tool when encountering persistent connectivity issues or when preparing a detailed technical support ticket for Bright Data support.

This tool collects comprehensive diagnostic data from ClickHouse system tables. It gathers crucial metrics, query logs, merge statuses, and disk usage information. It is essential for troubleshooting performance bottlenecks, preparing detailed support tickets, or investigating complex database infrastructure issues.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.