Skill UI

This skill guides the setup of Host-Based Intrusion Detection Systems (HIDS) using industry-standard tools like Wazuh, OSSEC, or AIDE. It covers essential security practices, including File Integrity Monitoring (FIM) for critical directories, monitoring system calls, detecting configuration changes, and identifying rootkits. It is crucial for meeting regulatory compliance requirements (e.g., PCI DSS) and significantly enhancing overall endpoint security posture.

This comprehensive skill guides the setup and deployment of File Integrity Monitoring (FIM) using AIDE on Linux systems. It covers generating the initial baseline database, configuring monitoring rules, running scheduled checks via cron, and parsing change reports to detect unauthorized file modifications. Essential for meeting strict compliance requirements and enhancing endpoint security.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This skill allows users to extract structured data (text, tables, attributes) from rendered web pages. It enhances traditional web scraping by ensuring all extracted strings pass mandatory AIDefence PII checks and prompt-injection gates before being stored or returned to the model. It supports building reusable extraction templates and running one-shot queries.

Automates the process of filling out web forms using a structured field map (field_name → value). This tool supports using stored templates for known forms, or falling back to analyzing the page accessibility snapshot if no template exists. It includes mandatory PII screening (aidefence_has_pii) and handles form submission, making it ideal for automating sign-ups, checkouts, and data entry tasks.

This powerful utility automates the initial authentication process against a target website. It captures the resulting session cookies, sanitizes them rigorously using AIDefence to protect raw secrets, and then securely vaults a reusable cookie handle. This enables subsequent automation skills to seamlessly continue operations using the authenticated session context without needing to re-run the entire login flow.

This guide outlines the comprehensive process for submitting and evaluating Computer Aided Verification (CAV) research artifacts. It details the criteria for earning three badges (Available, Functional, Reusable), the two-phase review structure (smoke-test and full review), and best practices for packaging scientific tools, including the use of containers, pinned benchmarks, resource limits, and proof witnesses. This ensures reproducibility and rigor in academic research.

This guide outlines best practices for designing and executing Computer Aided Verification (CAV) empirical evaluations. It emphasizes ensuring evidence matches claims by using standard, pinned benchmark sets, selecting fair baselines, respecting equal resource limits, and employing rigorous reporting methods like cactus and scatter plots to establish technical soundness and performance.

This guide provides detailed protocols for achieving high levels of reproducibility in Computer Aided Verification (CAV) research. It emphasizes the necessity of pinning benchmark sets, recording exact tool versions, documenting resource limits (time/memory), and providing checkable proof witnesses to ensure that evaluation claims can be independently validated by competitors or reviewers.

Provides strict guidelines for assembling supplementary material for Computer Aided Verification (CAV) papers. It details how to separate decision-critical content (main theorems, proof ideas) into the primary body, while moving voluminous details (full proofs, massive benchmarks) to the optional appendix or artifact, ensuring the paper remains judgeable from the main text alone.

Use this comprehensive guide to manage the entire project lifecycle for a CAV (Computer Aided Verification) paper submission. It provides a backward-planning timeline, covering topics from initial concept fitting and evidence locking to the two-stage review process, optional artifact evaluation, and final camera-ready submission. Essential for managing technical academic deadlines and mitigating common failure modes.