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-monitor-alert-baselinegit 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-monitor-alert-baseline)<a href="https://agentmods.dev/skills/aiappsgbb/awesome-gbb/azure-monitor-alert-baseline"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/azure-monitor-alert-baseline/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-monitor-alert-baseline"><img src="https://agentmods.dev/badge/skills/aiappsgbb/awesome-gbb/azure-monitor-alert-baseline.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 | $0.00204 | $0.01916 |
| Opus 5 | $0.00102 | $0.00958 |
| Sonnet 5 | $0.00041 | $0.00383 |
| Haiku 4.5 | $0.00020 | $0.00192 |
Grade A, and why
azure-monitor-alert-baseline 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 12d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
azure-monitor-alert-baseline
Peer skill that probes Azure Monitor metric alert rules at a resource-group
scope against one of three published baselines, returning a structured
SRE-104 finding. It wraps MonitorManagementClient via
DefaultAzureCredential and never raises — errors are captured in the returned
dict. Threadlight v0.5.3+ consumes this as the SRE-104 sibling-skill check in
its threadlight-production-ready OBS-203 gate.
When to use
- Threadlight OBS-203 sibling-skill flip — threadlight's apply-plan
reasoner calls
probe()directly to satisfy the OBS-203 → SRE-104 check (kind: sibling-skill), advancing a pilot handover from manual to automated. - Pre-pilot observability review of a Foundry-adjacent resource group — confirms the correct alert rules are configured and thresholds are within the baseline's prescribed maximums before spoke onboarding.
- Scheduled CI drift check — detects alert rule removal or threshold relaxation after a deployment; runnable as a CI step with no interactive auth.
When NOT to use
- Creating or modifying alert rules — use
az monitor metrics alert create/az monitor metrics alert updatedirectly. - App Insights traces, logs, or availability tests — use the
foundry-observabilityskill for that surface. - Azure Service Health alerts or activity log alerts — those use a
different ARM API (
Microsoft.Insights/activityLogAlerts) and are not covered by this probe.
Probe contract
The probe returns a dict matching the design spec §4.3.1 sibling-skill
contract. Signature and shape are stable across 1.x releases:
| Field | Type | Notes |
|---|---|---|
finding_id |
str | Always literal "SRE-104" |
scope |
dict (sub_id, rg, alert_baseline_kind) | Nested; not a string |
result |
enum ok / needs_attention / errored |
Never anything else |
observations |
list[dict] | Empty when result == "ok" |
remediation_hints |
list[str] | Empty when observations empty |
confidence |
0.0 / 0.5 / 1.0 | See Confidence heuristic below |
probed_at |
ISO-8601 UTC with Z |
tz-aware |
error |
str | None | None on success; "<Type>: <msg>" on errored |
What ships with it
8 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.
- README.md 1.8 KB
- references/baselines/foundry_pilot.yaml 619 B
- references/baselines/production.yaml 684 B
- references/baselines/spoke_minimum.yaml 482 B
- references/python/__main__.py 1.7 KB runs code
- references/python/probe.py 5.0 KB runs code
- references/upstream-pin.md 6.7 KB
- test-fixture/consumer_prompt.md 6.1 KB
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.
- 12d ago First seen · 160 lines · 204 tokens per session scan A 3ad575356adb
azure-monitor-alert-baseline is a skill published in the GitHub repository aiappsgbb/awesome-gbb (5 stars, last pushed yesterday), licensed MIT. It adds 204 tokens to every session and 1,916 once invoked, about $0.0010 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.
Other skills, from other repositories
gke-compute-classes
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
cloud-architect
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost…