Skill UI

This component is an essential extension for KubeSphere Observability Platform, designed for comprehensive collection, processing, and storage of Kubernetes and KubeSphere audit events. It supports various sinks, including OpenSearch and Doris, and provides an API for querying historical audit data, ensuring robust auditing capabilities for platform operations.

This tool provides a read-only view over per-turn decision logs, aggregating data on skill co-occurrence patterns, meta-skill usage statistics, and router fixtures. It returns a structured JSON summary, making it ideal for analyzing historical skill usage, optimizing workflows, and feeding insights into downstream LLM models.

This tool analyzes historical sprint velocity data to generate a comprehensive engineering team health report. It moves beyond simple charts by diagnosing underlying delivery patterns, identifying risks like chronic over-commitment, and assessing story point calibration. The report provides actionable recommendations for improving capacity planning and team predictability.

Access comprehensive real-time and historical market data across major global exchanges, including HK, US, A-share, and Singapore. This skill provides quotes, K-line charts, order book depth, trade ticks, market sentiment indices, and sophisticated cross-market analysis tools like ADR premium and FX carry trade frameworks. Ideal for quantitative analysis, technical trading, and monitoring global financial movements.

This tool generates sophisticated trading signals by leveraging an advanced anomaly detection engine. It scans financial assets for various market anomalies—such as spikes, drifts, and oscillations—and classifies them. It then integrates neural network prediction and searches historical pattern databases to output highly ranked, actionable trading signals, complete with confidence scores and target parameters.

This skill generates a comprehensive, live snapshot of your marketing performance by pulling data from all connected analytics platforms (e.g., Google Analytics, Meta, LinkedIn). It compares current KPIs against defined brand targets, historical benchmarks, and industry averages. Use this for daily health checks, trend monitoring, and identifying immediate actionable insights across all channels.

Performs advanced semantic search across all stored brand knowledge, including guidelines, campaign learnings, and competitor insights. By combining vector databases, knowledge graphs, and local indices, it provides comprehensive, context-aware answers to complex natural language questions, ensuring users make decisions based on the brand's entire historical knowledge base.

This guide provides detailed standards for planning, auditing, and executing data analysis for sociological manuscripts, specifically tailored to the standards of the American Journal of Sociology (AJS). It stresses that analysis must credibly support a theoretical claim, rather than simply showcasing technical proficiency. Key areas covered include quantitative robustness checks, comparative-historical logic, ethnographic procedure, and the crucial principle of triangulation across diverse data sources.

A comprehensive guide for structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

A comprehensive guide detailing the rigorous methodologies for conducting and reporting high-level academic research. It covers norms for quantitative, comparative-historical, and computational analyses, emphasizing transparency, reproducibility, robustness checks (e.g., confounders, alternative specifications), and linking claims directly to evidence, suitable for top-tier social science journals.

This skill creates a standardized, persistent workspace (desk) for managing long-running projects or complex workstreams. It sets up a structured directory containing a detailed journal for historical context and a dedicated signals folder for structured data output, ensuring continuity across multiple sessions.

Provides comprehensive guidelines for authors submitting empirical, quantitative, or theoretical growth papers. It details how to package data sources, code pipelines, calibration files, and historical/geospatial artifacts to meet stringent reproducibility standards for publishing journals.