ai-inference-service-mesh

ai-inference-service-mesh is a skill for Claude Code, Codex from BagelHole/DevOps-Security-Agent-Skills. It costs 33 tokens per session (2,605 once invoked), scanned A, original, MIT.

A guide to applying a service mesh to AI inference systems, where services retrieve data, rerank results, and generate model responses. It uses Istio or Linkerd to manage their communication.

In plain words
What is it for?
Use it to enforce mutual TLS, route requests by model version or priority, run canary releases, apply service policies, and observe latency across retrieval and generation steps.
Why use it?
It helps secure and control traffic between inference components and protects costly GPU-backed services from failures spreading through the system.

Skill for Claude CodeCodex

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

Good fit Use it to enforce mutual TLS, route requests by model version or priority, run canary releases, apply service policies, and observe latency across retrieval and generation steps.

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Install with agentmods
npx agentmods add skills/bagelhole/devops-security-agent-skills/ai-inference-service-mesh
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 BagelHole/DevOps-Security-Agent-Skills --skill ai-inference-service-mesh
Clone the repo
git clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-Skills

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Your own site · 80×15
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Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,605 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00033 $0.02605
Opus 5 $0.00016 $0.01303
Sonnet 5 $0.00007 $0.00521
Haiku 4.5 $0.00003 $0.00261

Measured 9d ago against content hash 7c6f1840932e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ai-inference-service-mesh scanned grade A with 1 finding 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 9d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s http://localhost:20001/kiali/api/namespaces/ai-inference/health | jq .
infrastructure/networking/ai-inference-service-mesh/SKILL.md · 430 lines

How it starts

The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI Inference Service Mesh

Apply Istio/Linkerd mesh controls to secure and optimize east-west AI traffic across inference microservices.

Why Mesh for AI

  • Enforce mTLS between gateway, retriever, reranker, and model services
  • Apply fine-grained traffic policies without app code changes
  • Run progressive delivery for model-serving backends
  • Observe latency hops for retrieval + generation chains
  • Route inference requests by model version, tenant, or priority tier
  • Protect expensive GPU-backed services from cascading failures

Prerequisites

# Install Istio with production profile
istioctl install --set profile=default \
  --set meshConfig.accessLogFile=/dev/stdout \
  --set meshConfig.defaultConfig.holdApplicationUntilProxyStarts=true

# Label inference namespace for sidecar injection
kubectl create namespace ai-inference
kubectl label namespace ai-inference istio-injection=enabled

# Verify installation
istioctl verify-install
istioctl analyze -n ai-inference

Core Patterns

mTLS Strict Mode Cluster-Wide

apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
  name: default
  namespace: istio-system
spec:
  mtls:
    mode: STRICT
---
# Namespace-level override if needed for gradual rollout
apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
  name: ai-inference-mtls
  namespace: ai-inference
spec:
  mtls:
    mode: STRICT
  portLevelMtls:
    # gRPC inference port
    8081:
      mode: STRICT
    # Prometheus metrics port - allow plaintext scraping
    9090:
      mode: PERMISSIVE

AuthorizationPolicy Per Service Account

apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: model-server-access
  namespace: ai-inference
spec:
  selector:
    matchLabels:
      app: model-server
  action: ALLOW
  rules:
  - from:
    - source:
        principals:
        - "cluster.local/ns/ai-inference/sa/api-gateway"
        - "cluster.local/ns/ai-inference/sa/orchestrator"
    to:
    - operation:
        methods: ["POST"]
        paths: ["/v1/predict", "/v1/embeddings", "/v2/models/*/infer"]
---
apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: deny-external-to-retriever
  namespace: ai-inference
spec:
  selector:
    matchLabels:
      app: vector-retriever
  action: DENY
  rules:
  - from:
    - source:
        notNamespaces: ["ai-inference"]

Read the full file on GitHub · 430 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. 9d ago First seen · 430 lines · 33 tokens per session scan A 7c6f1840932e

Subscribe to this mod's changes

ai-inference-service-mesh is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,084 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 2,605 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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