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 vinayaklatthe/microsoft-security-skills --skill azure-confidential-computinggit clone --depth 1 https://github.com/vinayaklatthe/microsoft-security-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/vinayaklatthe/microsoft-security-skills/azure-confidential-computing)<a href="https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/azure-confidential-computing"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/azure-confidential-computing/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/vinayaklatthe/microsoft-security-skills/azure-confidential-computing"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/azure-confidential-computing.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 | $0.00221 | $0.02197 |
| Opus 5 | $0.00111 | $0.01099 |
| Sonnet 5 | $0.00044 | $0.00439 |
| Haiku 4.5 | $0.00022 | $0.00220 |
Grade A, and why
azure-confidential-computing 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 11d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Confidential Computing
Confidential computing is the third pillar of data protection — data in use — complementing encryption at rest and in transit. Azure Confidential Computing uses hardware-based Trusted Execution Environments (TEEs) so workloads run on encrypted memory the host (and Microsoft) cannot inspect, with cryptographic attestation to prove the TEE state to a remote relying party before secrets are released.
When to use
- Multi-party data collaboration where parties must compute on each other's data without seeing it (clean rooms, fraud consortia, joint analytics).
- Regulated workloads requiring isolation from cloud operator access (defense, certain financial / healthcare scenarios).
- Sensitive AI training/inference where model weights or training data are crown jewels.
- Cryptographic operations that must be hardware-rooted with attestation evidence.
Do not use this skill for ordinary data-at-rest CMK (azure-key-vault), generic app
encryption, or non-Azure TEE / Intel SGX-only designs.
Form factors
| Form factor | Hardware | Best for |
|---|---|---|
| Confidential VMs (DCasv5/ECasv5) | AMD SEV-SNP | Lift-and-shift Linux/Windows workloads with whole-VM TEE |
| Confidential VMs (DCesv5/ECesv5) | Intel TDX | Trust-domain isolation for VMs |
| Confidential containers on AKS | Kata Containers + AMD SEV-SNP | Container workloads, per-pod TEE |
| Confidential GPU VMs (NCC H100 v5) | NVIDIA H100 + Intel TDX | Confidential AI training/inference |
| App-enclave (Intel SGX, DCsv2/3) | Intel SGX | Targeted enclaves; legacy pattern, less common in new builds |
| Azure Confidential Ledger | Hardware-backed append-only ledger | Tamper-evident audit logs |
Approach
- Validate the threat model. Confidential computing protects against:
- Cloud operator (host admin) memory inspection.
- Co-tenant side-channel reads (within hardware mitigation limits).
- Snapshot/disk-image exfiltration combined with memory dump. It does not protect against application bugs, supply-chain compromise of your own code, or insider access at the customer.
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.
- 11d ago First seen · 153 lines · 221 tokens per session scan A 3bd284761de9
azure-confidential-computing is a skill published in the GitHub repository vinayaklatthe/microsoft-security-skills (173 stars, last pushed 2mo ago), licensed MIT. It adds 221 tokens to every session and 2,197 once invoked, about $0.0011 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 2026-08-30.
Other skills, from other repositories
azure-prepare
WORKFLOW SKILL — Prepare Azure apps for deployment (Bicep/Terraform, azure.yaml, Dockerfiles). WHEN: "create app", "build web app", "create API", "deploy to Azure", "generate Bicep", "generate Terraform", "function app", "add authentication", "managed identity". DO NOT USE FOR: cross-cloud migration…
staging-deploy
Build, push, migrate, and deploy to staging environment with health check verification. Use when deploying a completed phase to staging.
maintenance-check-aws-eks
Check everything that can be upgraded for maintenance across an AWS EKS Kubernetes cluster and output a decision file with Jira-formatted tickets for any required upgrades.
kubernetes-operations
Production-grade Kubernetes operations for AI agents: manifest generation, security hardening, Helm charts, GitOps workflows, and multi-cloud deployment patterns. Prevents K8s hallucinations with failure-mode diagnosis, compliance validation, and structured output contracts.
kubernetes-architect
Kubernetes architecture principles. Cluster design, Helm charts, GitOps, scaling strategies, and production operations. Use when deploying containerized applications at scale.
hosted-agents-v2-py
Build hosted agents using Azure AI Projects SDK with ImageBasedHostedAgentDefinition. Use when creating container-based agents in Azure AI Foundry.