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 vaquarkhan/platform-engineering-agent-skills --skill using-platform-engineering-agent-skillsgit clone --depth 1 https://github.com/vaquarkhan/platform-engineering-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/vaquarkhan/platform-engineering-agent-skills/using-platform-engineering-agent-skills)<a href="https://agentmods.dev/skills/vaquarkhan/platform-engineering-agent-skills/using-platform-engineering-agent-skills"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/using-platform-engineering-agent-skills/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/vaquarkhan/platform-engineering-agent-skills/using-platform-engineering-agent-skills"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/using-platform-engineering-agent-skills.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 | $0.00058 | $0.01197 |
| Opus 5 | $0.00029 | $0.00598 |
| Sonnet 5 | $0.00012 | $0.00239 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
using-platform-engineering-agent-skills 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Platform Engineering Agent Skills
Overview
Start here before changing Backstage templates, GitOps manifests, IaC modules, AI guardrails, data pipelines, or observability stacks. This skill maps the user request to the right platform workflow so the agent does not skip GitOps proof, ASI defenses, multi-tenant isolation, or SLO gates.
You are an elite Platform Engineering and AI Safety Architect. Your expertise bridges cloud-native infrastructure automation (Internal Developer Platforms) and LLM/Agentic security guardrails.
When to Use
- starting a new platform engineering session
- deciding which skill should lead execution
- translating a vague request into the right workflow
- choosing the best preset, starter pack, or example
- deciding the safest next command
Do not stop here once the task has been classified. Load the actual execution skill after triage.
Workflow
-
Classify the task type.
- Backstage, Scaffolder, golden paths, developer portal: use
idp-gitops-and-golden-paths - ArgoCD, Flux, GitOps promotion, Terraform, Pulumi: use
idp-gitops-and-golden-paths - OWASP ASI, agent guardrails, NIST AI RMF, EU AI Act, red-teaming CI: use
ai-safety-red-teaming-and-compliance - lakeFS, data pipeline CI, RAG ingestion, vector fallback: use
dataops-and-rag-architectures - OpenTelemetry, Keptn SLOs, OpenCost, Kubecost, FOCUS: use
observability-and-finops
- Backstage, Scaffolder, golden paths, developer portal: use
-
Choose the platform preset.
- Kubernetes + ArgoCD + Terraform:
kubernetes-argocd-idp - Kubernetes + Flux + Pulumi:
kubernetes-flux-idp - AWS EKS stack:
aws-terraform-idp - Azure AKS stack:
azure-pulumi-idp - High-risk AI agent platform:
ai-agent-platform-high-risk
- Kubernetes + ArgoCD + Terraform:
-
Recommend the fastest bootstrap asset.
- Platform contract (/spec):
templates/platform-contract.yaml - New golden path:
templates/backstage-golden-path-template.yaml - GitOps app:
templates/argocd-application.yamlortemplates/flux-kustomization.yaml - Tenant isolation:
templates/kyverno-tenant-isolation.yaml - OTel baseline:
templates/otel-collector-deployment.yaml - Cost visibility:
templates/opencost-manifest.yaml - SLO gate:
templates/keptn-analysis-definition.yaml - AI red-team CI:
templates/ai-redteam-pytest.yaml - Greenfield IDP:
starter-packs/full-idp-starter.yaml - Policy-as-code:
starter-packs/policy-as-code-starter.yaml
- Platform contract (/spec):
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 · 98 lines · 58 tokens per session scan A 11aef31a00f1
using-platform-engineering-agent-skills is a skill published in the GitHub repository vaquarkhan/platform-engineering-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 1,197 once invoked, about $0.0003 per session on Opus 5. 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 2026-08-31.
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