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 aiappsgbb/awesome-gbb --skill azure-resource-diagnosticsgit clone --depth 1 https://github.com/aiappsgbb/awesome-gbbWrote 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/aiappsgbb/awesome-gbb/azure-resource-diagnostics)<a href="https://agentmods.dev/skills/aiappsgbb/awesome-gbb/azure-resource-diagnostics"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/azure-resource-diagnostics/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/aiappsgbb/awesome-gbb/azure-resource-diagnostics"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/azure-resource-diagnostics.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 | $0.00210 | $0.01172 |
| Opus 5 | $0.00105 | $0.00586 |
| Sonnet 5 | $0.00042 | $0.00234 |
| Haiku 4.5 | $0.00021 | $0.00117 |
Grade A, and why
azure-resource-diagnostics 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
azure-resource-diagnostics
Audits Azure diagnostic-settings coverage at a resource group scope.
When to use
- threadlight v0.5.x needs to flip OBS-106 from
kind: manualtokind: sibling-skill— this skill'sprobe()is the sibling. - Pre-pilot review: confirm a candidate Foundry RG routes its resource logs somewhere (Log Analytics / Event Hubs / Storage) before a customer pilot.
- Spoke landing-zone check: detect resources that have no diagnostic settings configured at all.
Probing an RG
from azure_resource_diagnostics.probe import probe
result = probe(
subscription_id="<sub-id>",
resource_group="<rg>",
# target_resource_types=["storage_account", "key_vault"], # optional OBS-106 filter
)
# result["resources"] → list of {id, name, type, configured, destinations, setting_count}
# result["summary"]["total_resources"] → int (after type filter)
# result["summary"]["configured_count"] → int (≥1 destination set)
# result["summary"]["unconfigured_count"] → int (no destination)
# result["summary"]["target_resource_types_filter"] → list[str] | None (echo of applied filter)
# result["summary"]["confidence"] → 0.0..1.0
# result["summary"]["probe_error"] → str | None
# result["findings"] → list of no-diagnostic-settings findings
# result["manifest_path"] → path to JSON manifest on disk
target_resource_types (the OBS-106 sibling-contract input) is an
optional list of resource-type tokens. Matching is robust: each
token is normalized (lowercased, non-alphanumerics stripped) and
matched as a substring of the normalized ARM type, so both raw ARM
types (Microsoft.Storage/storageAccounts) and snake_case logical
kinds (storage_account) select the same resources. When omitted
(default), every resource in the RG is probed. The applied filter is
echoed back in summary.target_resource_types_filter.
The probe never raises. If the RG resource listing is denied
(RBAC missing), the probe still returns a shape with probe_error
populated and confidence: 0.0. Resource types that don't support
diagnostic settings (Monitor returns 404) are treated as having no
destinations, not as a denial.
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.
- 9d ago First seen · 116 lines · 210 tokens per session scan A 52ed0b109264
azure-resource-diagnostics is a skill published in the GitHub repository aiappsgbb/awesome-gbb (5 stars, last pushed 2d ago), licensed MIT. It adds 210 tokens to every session and 1,172 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 2026-08-31.
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