Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add andycungkrinx91/konoha --skill configuring-microsegmentation-for-zero-trustgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andycungkrinx91/konoha/configuring-microsegmentation-for-zero-trust)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/configuring-microsegmentation-for-zero-trust"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/configuring-microsegmentation-for-zero-trust/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/configuring-microsegmentation-for-zero-trust"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/configuring-microsegmentation-for-zero-trust.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.01663 |
| Opus 5.5 | $0.00020 | $0.00665 |
| Sonnet 5.5 | $0.00010 | $0.00333 |
| Haiku 4.5 | $0.00005 | $0.00166 |
Grade A, and why
configuring-microsegmentation-for-zero-trust scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to configuring-microsegmentation-for-zero-trust — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configuring Microsegmentation for Zero Trust
Prerequisites
- Understanding of zero trust principles (NIST SP 800-207)
- Knowledge of network segmentation concepts
- Familiarity with firewall and SDN technologies
- Experience with VMware NSX, Illumio, Guardicore, or Cisco ACI
Overview
Microsegmentation divides a network into granular security zones, enforcing least-privilege access between workloads at the application layer rather than relying on traditional VLAN-based segmentation. In a zero trust architecture, microsegmentation eliminates implicit trust between workloads within the same network segment, preventing lateral movement even after an attacker gains initial access.
This skill covers designing microsegmentation policies using workload identity, implementing host-based and network-based enforcement, and validating segmentation effectiveness with tools like Illumio Core and VMware NSX.
When to Use
- When deploying or configuring configuring microsegmentation for zero trust capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Familiarity with zero trust architecture concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities
Architecture
Microsegmentation Models
- Network-Based (VMware NSX, Cisco ACI): Distributed firewall rules enforced at the hypervisor or network fabric level
- Host-Based (Illumio, Guardicore): Agent-based enforcement at the OS level using iptables/WFP rules
- Container-Based (Calico, Cilium): Network policies enforced at the pod/container level in Kubernetes
- Application-Based (Zscaler Workload Segmentation): Identity-based segmentation based on software identity rather than IP addresses
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 179 lines · 50 tokens per session scan A 3f60a7fda2de
configuring-microsegmentation-for-zero-trust is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 50 tokens to every session and 1,663 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to configuring-microsegmentation-for-zero-trust, differing in 0 lines, and is treated as a copy.
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