Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.
This skill covers implementing network segmentation in Operational Technology environments using VLANs, industrial firewalls, data diodes, and software-defined networking. It addresses the Purdue Model-based segmentation strategy, migration from flat networks to segmented architectures without disrupting operations, configuring OT-aware firewalls with industrial protocol deep packet inspection, and validating segmentation effectiveness through traffic analysis.
Design and implement network segmentation using firewall security zones, VLANs, ACLs, and microsegmentation policies to restrict lateral movement and enforce least-privilege network access.
Develop and implement OT-specific incident response playbooks aligned with SANS PICERL framework, IEC 62443, and NIST SP 800-82 that address unique ICS challenges including safety-critical systems, limited downtime tolerance, and coordination between IT SOC, OT engineering, and plant operations teams.
Implement network segmentation based on the Purdue Enterprise Reference Architecture (PERA) model to separate industrial control system networks into hierarchical security zones from Level 0 physical process through Level 5 enterprise, enforcing strict traffic control between OT and IT domains.
Implements security chaos engineering experiments that deliberately disable or degrade security controls to verify detection and response capabilities. Tests WAF bypass, firewall rule removal, log pipeline disruption, and EDR disablement scenarios using boto3 and subprocess. Use when validating SOC detection coverage and resilience.
Implements SIEM detection use cases by designing correlation rules, threshold alerts, and behavioral analytics mapped to MITRE ATT&CK techniques across Splunk, Elastic, and Sentinel. Use when SOC teams need to expand detection coverage, formalize use case lifecycle management, or build a detection library aligned to organizational threat profile.
Builds and manages structures in Structs. Handles construction, activation, deactivation, movement, defense positioning, stealth, and generator infusion. Use when building a struct, activating or deactivating structs, moving structs between slots, setting defense assignments, enabling stealth, or infusing generators. Build times range from ~17 min (Command Ship) to ~6.4 hours (World Engine).
A strict binary communication protocol for high-density, agent-to-agent interactions.
A comprehensive AI agent skill for managing, designing, and troubleshooting technical infrastructure. Covers cloud architecture, server configuration, networking, security hardening, cost optimization, incident response, and infrastructure as code. Helps engineers, DevOps professionals, and technical founders build systems that are reliable, secure, scalable, and cost-efficient — and fix them fast when they are not.
Agent safety that learns from incidents. Reflex arc blocks repeat threats without LLM calls.
Draft IACUC protocol applications with focus on the 3Rs principles justification
Design gene circuits for synthetic biology applications
Structured reasoning through sequential thinking — break complex problems into steps, solve each independently, verify consistency, synthesize conclusions with confidence scoring. Use for complex analysis, debugging, and multi-step reasoning.
The agent data layer. Perkoon moves files between agents and the physical world — agent to human, agent to agent, agent to pipeline. P2P over WebRTC. Free. Encrypted. Unlimited. No accounts. Files never touch our servers. CLI, MCP server, A2A protocol, browser automation — pick the interface that fits your runtime.
Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞
Agent Designer - Multi-Agent System Architecture
Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.
