Skill UI

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

RecallMax dramatically enhances AI agent memory capabilities by cleanly injecting massive external contexts (500K-1M tokens) without hallucination drift. It automatically summarizes lengthy conversations, preserving tone, intent, and key facts. Furthermore, it compresses multi-turn interaction histories into high-density token sequences, ensuring agents maintain perfect recall across complex and long-running sessions.

An advanced reliability wrapper for AI agents that monitors, manages, and recovers from tool call failures. It automatically handles common issues such as truncated JSON responses, API timeouts, rate limits, and mid-chain process breakdowns. By implementing sophisticated failure classification, checkpointing, and learning mechanisms, Guardian ensures complex, multi-step agent workflows remain stable and resilient.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

This guide details comprehensive methods for auditing Terraform infrastructure-as-code (IaC) against security misconfigurations. By leveraging industry-standard tools like Checkov, tfsec, Terrascan, and Open Policy Agent (OPA), users can detect critical vulnerabilities—such as overly permissive IAM policies, public resource exposure, and missing encryption—before cloud deployment. It is essential for integrating robust security gates into CI/CD pipelines and enforcing compliance standards.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

A comprehensive guide for implementing enterprise-grade Security Access Service Edge (SASE) architectures using Palo Alto Networks Prisma Access. This skill covers configuring ZTNA connectors, GlobalProtect agents, security policies, and device posture checks (HIP) to provide unified, cloud-delivered security, replacing traditional VPNs and maintaining zero trust principles across remote and branch offices.

This tool automates the extraction of embedded configuration details from Agent Tesla Remote Access Trojan (RAT) samples. It is designed to recover crucial exfiltration credentials (SMTP, FTP, Telegram), keylogger settings, and Command and Control (C2) endpoints by analyzing and deobfuscating the .NET assembly structure. Essential for threat intelligence, incident response, and malware reverse engineering.

This guide details implementing Policy-as-Code using Open Policy Agent (OPA) and Gatekeeper. It covers writing complex Rego policies, deploying OPA Gatekeeper as a Kubernetes admission controller, and integrating policy testing into CI/CD pipelines to enforce security standards across clusters. Ideal for DevSecOps practitioners requiring robust governance.

This guide details the implementation of comprehensive security monitoring using Datadog Cloud SIEM, Cloud Security Management (CSM), and Workload Protection. It covers deploying the Datadog Agent across hybrid cloud infrastructure (AWS, Azure, GCP), ingesting critical log sources (CloudTrail, VPC Flow Logs, etc.), configuring Workload Protection, and creating custom detection rules to detect threats, enforce compliance, and provide centralized SOC visibility.

This skill covers performing comprehensive vulnerability assessments across diverse environments, including on-premises infrastructure, cloud VMs, and container registries. It leverages standard network protocols like SSH (Linux) and WMI (Windows), coupled with advanced cloud provider APIs for analyzing disk snapshots without requiring local endpoint agents. Ideal for security auditing and compliance checks.