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 BagelHole/DevOps-Security-Agent-Skills --skill ai-inference-service-meshgit clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-SkillsWrote 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/bagelhole/devops-security-agent-skills/ai-inference-service-mesh)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/ai-inference-service-mesh"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-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.
<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/ai-inference-service-mesh"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/ai-inference-service-mesh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.02605 |
| Opus 5 | $0.00016 | $0.01303 |
| Sonnet 5 | $0.00007 | $0.00521 |
| Haiku 4.5 | $0.00003 | $0.00261 |
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 . 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"]
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.
- 9d ago First seen · 430 lines · 33 tokens per session scan A 7c6f1840932e
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.
Other skills, from other repositories
bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
implementing-aws-macie-for-data-classification
Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials detection.
ai-gateway-guardrails
Enforce Input/Output Guardrails at the LLM Gateway layer — PII redaction, Prompt Injection defense, Jailbreak detection, Toxicity filter, and Tool Allow-list. Integrates Bedrock Guardrails, NeMo Guardrails, Llama Guard 3, and regex/regex-ML policies on Bifrost/LiteLLM with Langfuse audit trail.
gpu-resource-management
Design GPU orchestration on EKS using Karpenter v1.2+ NodePools, KEDA scale-to-zero, and DRA 1.35 GA for multi-instance GPU (MIG) partitioning. Right-size NodePool for p5/g6e/trn2 instance mix, spot/on-demand split, consolidation, and topology-aware scheduling.
inference-gateway-routing
Configure kgateway v2.0+ as L1 and Bifrost v1.x or LiteLLM v1.60+ as L2 for a 2-Tier Inference Gateway on EKS. Apply Cascade Routing (Haiku→Sonnet→Opus fallback), Semantic Router (intent-based model pick), and HTTPRoute with OTel trace propagation to Langfuse.
vllm-serving-setup
Design, deploy, and tune vLLM v0.18.2 inference serving on EKS with PagedAttention v2, Multi-LoRA, FP8 KV Cache, Chunked Prefill, and Continuous Batching. Produces Helm values.yaml, PodMonitor, HPA, and kubectl validation steps for production agentic workloads.