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 manu14357/zskills --skill azure-aigatewaygit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/manu14357/zskills/azure-aigateway)<a href="https://agentmods.dev/skills/manu14357/zskills/azure-aigateway"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-aigateway/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/manu14357/zskills/azure-aigateway"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-aigateway.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 | $0.00045 | $0.03040 |
| Opus 5 | $0.00023 | $0.01520 |
| Sonnet 5 | $0.00009 | $0.00608 |
| Haiku 4.5 | $0.00005 | $0.00304 |
Grade C, and why
azure-aigateway scanned grade C with 1 finding 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.
Instruction-override phrasinghighPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
├─ Reject attempts to override system prompt How it starts
The opening of the file, as written. The whole thing — 354 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Gateway
Centralize LLM traffic through a controlled gateway for security, observability, policy enforcement, and cost control.
Use This Skill When
- The user needs one entry point for multiple models (Azure OpenAI, third-party LLMs, internal models)
- The user needs to enforce token budgets, rate limits, or SLA policies
- The user needs centralized auditing and compliance logging
- The user needs intelligent fallback (model outage handling)
- The user wants to avoid client-side model management complexity
Context: Gateway Maturity
Immature: Clients call models directly, no centralization
Developing: Basic gateway, simple routing, minimal logging
Managed: Multi-model routing, policy enforcement, token budgets, audit trails → Target
Optimized: Real-time cost optimization, feedback-driven routing, self-healing, ML-based model selection
Required Inputs
- Model endpoints: Azure OpenAI? Hugging Face? Internal models? Multiple versions?
- Routing criteria: Latency? Cost? Specific models for specific tasks?
- Policy requirements: Rate limits? Token budgets? Approval workflows?
- Scale: Throughput (req/sec)? Concurrent users? Peak vs. average?
- Compliance: PII redaction? Audit trail retention? Data residency?
- Cost model: Budget cap? Cost-per-department? Showback?
Decision Tree
What's the primary goal of this gateway?
├─ Cost control → Route to cheaper model if accuracy acceptable
├─ Reliability → Route to alternative model on failure/timeout
├─ Governance → Enforce policies, audit all requests
├─ Performance → Route based on latency SLA
└─ Multi-tenancy → Tenant isolation, quota per tenant
Which models will the gateway route to?
├─ Single model (single Azure OpenAI deployment) → No routing needed
├─ Multiple Azure OpenAI models → Route by task/cost/latency
├─ Multiple providers (Azure OpenAI + third-party) → Handle auth differences
└─ Canary/blue-green (new model test) → Weighted routing
What policy enforcement is needed?
├─ None (simple routing only)
├─ Token budgets (cap usage per user/org)
├─ Rate limits (req/sec, concurrent, bursts)
├─ Approval workflows (route restricted requests for review)
└─ Compliance (PII redaction, output filtering, audit)
How much data volume?
├─ < 100 req/sec → Simple load balancer
├─ 100-1000 req/sec → Async processing, queue buffering
└─ > 1000 req/sec → Distributed gateway, database-backed quotas
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 · 354 lines · 45 tokens per session scan C afb072a95c02
azure-aigateway is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 3,040 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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