microsegmentation-patterns

microsegmentation-patterns is a skill for Claude Code, Codex from HermeticOrmus/LibreSecOps-Claude-Code. It costs 0 tokens per session (2,595 once invoked), scanned A, original, MIT.

A knowledge base describing microsegmentation, a security method that limits which services, applications, or workloads may communicate. It covers network, application, and identity-based approaches across local and cloud systems.

In plain words
What is it for?
Use it when designing firewall rules, service-mesh policies, API access controls, workload identity, or Kubernetes network policies.
Why use it?
It helps teams choose how finely to separate systems and decide between allowing traffic by default or blocking it unless explicitly approved.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when designing firewall rules, service-mesh policies, API access controls, workload identity, or Kubernetes network policies.

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Install with agentmods
npx agentmods add skills/hermeticormus/libresecops-claude-code/microsegmentation-patterns
Install

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.

Any agent
npx skills add HermeticOrmus/LibreSecOps-Claude-Code --skill microsegmentation-patterns
Clone the repo
git clone --depth 1 https://github.com/HermeticOrmus/LibreSecOps-Claude-Code

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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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.

agentmods 80×15 button for microsegmentation-patterns

Your own site · 80×15
<a href="https://agentmods.dev/skills/hermeticormus/libresecops-claude-code/microsegmentation-patterns"><img src="https://agentmods.dev/badge/skills/hermeticormus/libresecops-claude-code/microsegmentation-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,595 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.02595
Opus 5.5 $0.00000 $0.01038
Sonnet 5.5 $0.00000 $0.00519
Haiku 4.5 $0.00000 $0.00260

Measured 1mo ago against content hash cd454dd5d572, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

microsegmentation-patterns 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 1mo 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.

plugins/zero-trust-architecture/skills/microsegmentation-patterns/SKILL.md · 360 lines

How it starts

The opening of the file, as written. The whole thing — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Microsegmentation Patterns

Implementation approaches for network, application, and identity-based microsegmentation across on-premises and cloud environments.

Knowledge Base

Segmentation Levels

Microsegmentation operates at multiple layers, each providing different granularity and capabilities:

Layer 3/4 (Network): IP addresses, ports, protocols. The most basic form. Implemented via firewalls, security groups, VLANs, and Kubernetes NetworkPolicy. Limitation: IP addresses are not stable identities in dynamic environments (containers, autoscaling).

Layer 7 (Application): HTTP methods, paths, headers, gRPC services. Implemented via service mesh (Istio, Linkerd), application-aware firewalls, and API gateways. Provides much finer control but requires deeper infrastructure integration.

Identity-based: Cryptographic workload identity (SPIFFE/SPIRE, mTLS certificates). Communication is authorized based on verified identity, not network location. The most robust form -- works across clusters, clouds, and hybrid environments.

Default Deny vs. Default Allow

Default deny (target state): All traffic is blocked unless explicitly allowed. This is the zero trust ideal. In practice, it requires a complete communication matrix before enforcement.

Default allow with logging (starting state): All traffic is allowed but logged. Use this phase to discover communication patterns, build the allowlist, and then transition to default deny.

The transition process:

  1. Deploy in logging/audit mode (default allow, log everything)
  2. Analyze traffic patterns for 2-4 weeks
  3. Build communication matrix from observed traffic
  4. Create explicit allow policies for legitimate traffic
  5. Switch to default deny with allow policies
  6. Monitor for breakage, adjust policies
  7. Iterate per segment/namespace/tier

Patterns

Pattern 1: Kubernetes NetworkPolicy (L3/L4)

# Default deny all ingress and egress in a namespace
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
  namespace: production
spec:
  podSelector: {}  # Applies to all pods in namespace
  policyTypes:
    - Ingress
    - Egress

---
# Allow frontend to talk to backend API on port 8080
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-frontend-to-backend
  namespace: production
spec:
  podSelector:
    matchLabels:
      app: backend-api
  policyTypes:
    - Ingress
  ingress:
    - from:
        - podSelector:
            matchLabels:
              app: frontend
      ports:
        - protocol: TCP
          port: 8080

---
# Allow backend API to reach database on port 5432
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-backend-to-database
  namespace: production
spec:
  podSelector:
    matchLabels:
      app: database
  policyTypes:
    - Ingress
  ingress:
    - from:
        - podSelector:
            matchLabels:
              app: backend-api
      ports:
        - protocol: TCP
          port: 5432

---
# Allow all pods to reach DNS (required for service discovery)
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-dns
  namespace: production
spec:
  podSelector: {}
  policyTypes:
    - Egress
  egress:
    - to:
        - namespaceSelector: {}
          podSelector:
            matchLabels:
              k8s-app: kube-dns
      ports:
        - protocol: UDP
          port: 53
        - protocol: TCP
          port: 53

Read the full file on GitHub · 360 lines

Changes

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.

  1. 1mo ago First seen · 360 lines · 0 tokens per session scan A cd454dd5d572

Subscribe to this mod's changes

microsegmentation-patterns is a skill published in the GitHub repository HermeticOrmus/LibreSecOps-Claude-Code (4 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,595 tokens. 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 2026-09-03.

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