Skill UI

Guides diagnosing Buttercup CRS Kubernetes issues across the crs namespace by triaging pod states, analyzing logs, checking resource pressure, monitoring Redis, and inspecting queues for cascading failures in services such as redis, build-bot, scheduler, and UI.

Automates Ocrspace tasks via Composio’s toolkit and Rube MCP, guiding tool discovery, connection checks, schema-compliant execution, and stateful workflows for reliable OCR operations.

GeoPandas extends pandas for geospatial vector data, enabling reading/writing shapefiles, GeoJSON, GeoPackage and PostGIS, performing CRS management, spatial joins, overlays, buffering, area/distance calculations, and producing static or interactive choropleth maps.

This guide details configuring ModSecurity WAF and OWASP Core Rule Set (CRS) for comprehensive web application security auditing. It covers essential steps such as initial deployment, rule tuning to minimize false positives, configuring detailed audit logging, and integrating logs into SIEM systems for robust threat detection.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

Guidance and local audit tooling for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, and vector data I/O, covering reproducible environments, CRS and antimeridian rules, correctness gates, and privacy safeguards.

This skill provides a comprehensive workflow for verifying the integrity of a system's boot chain using TPM 2.0. It covers local validation by reading Platform Configuration Registers (PCRs) and replaying the event log, and remote attestation by generating and checking signed quotes. Use cases include enforcing Zero Trust device posture, detecting firmware tampering, or establishing secure secret baselines.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

This comprehensive guide outlines the best practices for preparing a complete and reproducible research package for submission to the Journal of Banking & Finance (JBF) following Elsevier Option C guidelines. It details requirements for data availability statements, proper structuring of code and data access materials, and best methods for managing proprietary or restricted datasets (such as CRSP or Compustat), ensuring research transparency and verifiability.

A comprehensive guide for conducting advanced empirical analysis required for top-tier finance journals. It covers assembling multi-source firm panels (Compustat, CRSP, etc.), constructing complex corporate finance variables, implementing advanced econometric techniques (high-dimensional fixed effects, clustered errors), and systematically planning robustness checks and replication strategies.

A comprehensive guide for authors submitting to the Journal of Corporate Finance (JCF). It details how to comply with Elsevier's Option C data availability requirements, focusing on drafting the data availability statement and structuring the research archive. It provides best practices for handling licensed, proprietary data (e.g., Compustat, CRSP) by recommending the sharing of construction code and access instructions, rather than raw data deposits.

This comprehensive guide assists in executing and documenting rigorous empirical finance research, tailored for top-tier journals like JFQA. It covers the entire pipeline, including complex financial data construction (CRSP, Compustat), advanced data cleaning (winsorizing, outlier handling), and sophisticated econometrics (fixed effects, clustered standard errors, Fama-MacBeth). The core focus is achieving absolute reproducibility, ensuring that all filters, robustness checks, and analytical steps are meticulously documented for archival and peer review.