ai-inference-service-mesh

ai-inference-service-mesh is a skill for Claude Code from sickn33/agentic-awesome-skills. It costs 34 tokens per session (2,882 once invoked), scanned A, original, MIT.

A guide to using a service mesh, a layer of networking software between services, for AI model-serving traffic. It covers encrypted service connections, traffic rules, staged releases, and routing across clusters.

In plain words
What is it for?
Use it with Istio or Linkerd to enforce mTLS encryption, route by model version or tenant, run canary releases, observe request latency, and protect GPU-backed services.
Why use it?
It helps protect and control requests between gateways, retrievers, rerankers, and model services without changing each application. It can also reduce failures caused by overloaded or unavailable model services.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agentic-awesome-skills plugin — 247 skills shipped together

Good fit Use it with Istio or Linkerd to enforce mTLS encryption, route by model version or tenant, run canary releases, observe request latency, and protect GPU-backed services.

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Install with agentmods
npx agentmods add skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh
About the project

AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.

sickn33/agentic-awesome-skills · 47,729 stars · on GitHub · aaskills.tech

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 sickn33/agentic-awesome-skills --skill ai-inference-service-mesh
Clone the repo
git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 247 skills.

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.

agentmods badge for ai-inference-service-mesh

README.md
[![agentmods](https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh/github.svg)](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh)
Your own site
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh/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.

agentmods 80×15 button for ai-inference-service-mesh

Your own site · 80×15
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-inference-service-mesh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,882 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.
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 $0.00034 $0.02882
Opus 5.5 $0.00014 $0.01153
Sonnet 5.5 $0.00007 $0.00576
Haiku 4.5 $0.00003 $0.00288

Measured 2d ago against content hash db080977f334, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 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.

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 .
Origin

Copies of this mod

2 near-identical copies found in the catalogue:

plugins/agentic-awesome-skills-claude/skills/ai-inference-service-mesh/SKILL.md · 425 lines

How it starts

The opening of the file, as written. The whole thing — 425 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

Confirm the exact cluster/context, mesh version, CRD schemas, owned namespace and authorized mutation scope. Examples are alternative fragments, not a manifest to apply wholesale: multiple DestinationRules for the same host must be consolidated under the installed version's merge rules. Preview and validate in a disposable environment; protect production routes and rollback evidence.

# Illustrative mutating install; default is not a validated production configuration
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

Read the full file on GitHub · 425 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. 2d ago Changed · -25 lines · +1 tokens per session db080977f334
  2. 26d ago First seen · 450 lines · 33 tokens per session scan A 5a831ccc32cc

Subscribe to this mod's changes

ai-inference-service-mesh is a skill published in the GitHub repository sickn33/agentic-awesome-skills (47,729 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 2,882 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-21.