Skill UI

This skill provides structured procedures and Python examples for detecting internal data exfiltration. It analyzes endpoint, cloud, and email logs by establishing behavioral baselines and performing statistical anomaly detection. Use cases include investigating insider threats, monitoring DLP policy violations, and building robust User and Entity Behavior Analytics (UEBA) rules for SOC teams.

This skill acts as a critical bridge between the conceptual idea phase and the automated execution phase. It ingests structured experiment plans (e.g., EXPERIMENT_PLAN.md), parses objectives, metrics, and methodology. It then implements the necessary code (training, evaluation), performs internal code reviews, and deploys the experiments to collect initial results, advancing the research workflow.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

This utility displays the precise, real-time token usage and estimated cost savings for the current session. It reads directly from the internal session log, ensuring objective and accurate data without requiring AI computation. Triggered by /caveman-stats, it provides immediate performance metrics to monitor API costs and usage patterns.

Provides comprehensive skills and references for writing, debugging, and optimizing DAX within semantic models. It guides users through systematic performance tuning using a tiered framework, covering engine internals, best practices, and profiling techniques for both local and remote models.