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.
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 sickn33/agentic-awesome-skills --skill ai-inference-service-meshgit clone --depth 1 https://github.com/sickn33/agentic-awesome-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/sickn33/agentic-awesome-skills/ai-inference-service-mesh)<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.
<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>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 | $0.00034 | $0.02882 |
| Opus 5.5 | $0.00014 | $0.01153 |
| Sonnet 5.5 | $0.00007 | $0.00576 |
| Haiku 4.5 | $0.00003 | $0.00288 |
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 . Copies of this mod
2 near-identical copies found in the catalogue:
- ai-inference-service-mesh — 100% identical, 0 lines differ
- ai-inference-service-mesh — 100% identical, 61 lines differ
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
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 Changed · -25 lines · +1 tokens per session db080977f334
- 26d ago First seen · 450 lines · 33 tokens per session scan A 5a831ccc32cc
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.
Other skills, from other repositories
kubernetes-operator
Deploy and manage applications on Kubernetes. Covers deployments, services, ingress, HPA, secrets, and production-grade cluster configuration.
terraform-builder
Build infrastructure as code with Terraform. Covers module design, state management, AWS/GCP/Azure resources, and production-grade practices.
data-pipeline
Professional Data Pipeline Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
auto-scaler
Professional Auto Scaler Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
aws-architect
Professional Aws Architect skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
azure-developer
Professional Azure Developer skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.