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 ivegamsft/basecoat --skill azure-identitygit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/ivegamsft/basecoat/azure-identity)<a href="https://agentmods.dev/skills/ivegamsft/basecoat/azure-identity"><img src="https://agentmods.dev/badge/skills/ivegamsft/basecoat/azure-identity.svg" alt="Measured on agentmods" 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 | $0.00085 | $0.00355 |
| Opus 5 | $0.00043 | $0.00178 |
| Sonnet 5 | $0.00017 | $0.00071 |
| Haiku 4.5 | $0.00009 | $0.00036 |
Grade A, and why
azure-identity 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 4d 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.
What it actually says
Azure Identity & Entra ID Skill
Design and implement Azure identity and access management — RBAC hierarchies, managed identities, Entra ID app registrations, conditional access policies, and workload identity federation.
Templates in This Skill
| Template | Purpose |
|---|---|
rbac-role-assignment-template.md |
RBAC role assignment matrix — principal-to-role-to-scope mappings |
managed-identity-mapping-template.md |
Managed identity catalogue — system-assigned and user-assigned per workload |
app-registration-checklist.md |
Entra ID app registration — API permissions, credentials, token configuration |
workload-identity-federation-template.md |
GitHub Actions OIDC and external identity provider federation |
conditional-access-policy-template.md |
Zero trust conditional access — users, devices, applications |
Agent Pairing
Use with identity-architect agent. For IaC provisioning pair with devops-engineer; for app auth pair with backend-dev or frontend-dev; for threat modeling pair with security-analyst.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 30 lines · 85 tokens per session scan A 529065f02e46
azure-identity is a skill published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 355 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
skypilot-multi-cloud-orchestration
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
lambda-gpu-cloud
Safely inspect and operate Lambda Cloud GPU instances through the documented Cloud API and SSH, with explicit approval before billable or destructive actions.
runpod-gpu-cloud
Safely inspect and operate RunPod resources with runpodctl, live product data, and explicit approval before paid or destructive actions.
tensorpool-gpu-cloud
Safely inspect and operate TensorPool GPU clusters and jobs using the current tp CLI, with explicit approval before any billable or destructive action.
vast-ai-gpu-cloud
Safely inspect and operate Vast.ai marketplace instances with the vastai CLI, live offer data, and explicit approval before paid or destructive actions.
modal-serverless-gpu
Run approved CPU or GPU work through OpenScience computejob on the user's configured Modal account. Use for isolated scientific scripts, dependency provisioning, durable outputs, logs, status, cancellation, and recovery. Never invoke the Modal SDK or CLI directly.