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 agentmods add skills/unitoneai/securityskills/segmentationnpx skills add UnitOneAI/SecuritySkills --skill segmentationgit clone --depth 1 https://github.com/UnitOneAI/SecuritySkillsWhat 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 | $0.00078 | $0.04105 |
| Opus 5 | $0.00039 | $0.02053 |
| Sonnet 5 | $0.00016 | $0.00821 |
| Haiku 4.5 | $0.00008 | $0.00411 |
Grade A, and why
segmentation 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 2d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Network Segmentation Review
A structured, repeatable process for evaluating network segmentation architecture against NIST SP 800-207 (Zero Trust Architecture) and CIS Controls v8 Control 12 (Network Infrastructure Management). This skill produces a segmentation maturity assessment with zone mapping, trust boundary analysis, east-west traffic control evaluation, and prioritized remediation guidance.
When to Use
If a target is provided via arguments, focus the review on: $ARGUMENTS
- Architecture reviews for new or modified network designs.
- Zero Trust readiness assessments.
- PCI DSS scoping exercises requiring CDE segmentation validation (PCI DSS v4.0 Requirement 1.3).
- Post-incident reviews where lateral movement was observed or suspected.
- Cloud migration planning requiring workload isolation design.
- Merger/acquisition network integration planning.
Context
Network segmentation is the foundational control that limits blast radius. NIST SP 800-207 Section 2 defines Zero Trust Architecture as requiring "no implicit trust granted to assets or user accounts based solely on their physical or network location." CIS Controls v8 Control 12 requires enterprises to "establish, implement, and actively manage network devices, in order to prevent attackers from exploiting vulnerable network services and access points." Effective segmentation moves beyond flat VLANs to enforce policy at the workload level, restricting east-west traffic between systems that have no legitimate communication requirement.
Process
Step 1: Discovery -- Locate Network Architecture Artifacts
Use Glob and Grep to locate network configuration files, diagrams-as-code, and infrastructure definitions.
Patterns to search:
# Infrastructure-as-Code
**/*.tf # Terraform (VPCs, subnets, route tables, security groups)
**/vpc*
**/subnet*
**/network*
# Kubernetes network policies
**/NetworkPolicy*
**/network-policy*
**/calico*
**/cilium*
# Cloud-native
**/firewall-rule*
**/security-group*
**/nsg*
**/route-table*
# Traditional
**/vlan*
**/*.acl
**/interfaces*
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.
- 2d ago First seen · 386 lines · 78 tokens per session scan A ce782885e86c
segmentation is a skill published in the GitHub repository UnitOneAI/SecuritySkills (58 stars, last pushed 2mo ago), licensed MIT. It adds 78 tokens to every session and 4,105 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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