Agent Skills Hub

secure-claude-code

Monitor Runwall security posture, enabled guardrails, and recent audit logs for Claude Code, Codex, and MCP-based development environments.

systematic-debugging

A rigorous, four-phase methodology to enforce systematic root cause analysis before applying any code fixes.

clawsec-nanoclaw

Security advisory monitoring for NanoClaw WhatsApp bots, providing vulnerability scanning, skill safety checks, and integrity protection through MCP tools.

toh-framework

Security-first auditing framework for AI-generated code. Provides multi-level protection including hardcoded secret detection, dangerous pattern identification, and comprehensive vulnerability audits for modern web applications.

metrics-dashboard

Design comprehensive product metric dashboards, define KPIs, and establish monitoring plans with data-driven visualization, alert thresholds, and framework integration.

Auditing Security

Perform systematic security audits, vulnerability scanning, and risk assessments with OWASP-aligned methodology for robust code protection.

guard

Epistemic safety analysis for JSON data in prompts to prevent LLM hallucinations and reasoning errors when handling incomplete or large-scale datasets.

meteorology-driver-classification

Classify and group meteorological and environmental variables into specific driver categories for consistent attribution analysis and environmental modeling.

lomb-scargle-periodogram

Analyze periodic signals in unevenly sampled astronomical time series data using the Lomb-Scargle periodogram method with the lightkurve library.

Python Security Scan

Advanced Python security vulnerability scanner for Flask, Django, and FastAPI projects. Audits OWASP Top 10, dependencies, hardcoded secrets, and framework-specific flaws.

kpi-dashboard-design

Master KPI dashboard design with proven metrics frameworks, SMART goals, and hierarchy patterns to drive business performance from executive insights to operational monitoring.

scikit-learn

Classical machine learning with scikit-learn. Use for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building robust ML pipelines in Python.