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 agentmods add agents/emeaappgbb/agentic-shell-python/azuregit clone --depth 1 https://github.com/EmeaAppGbb/agentic-shell-pythonWhat 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 | $0.00024 | $0.01566 |
| Opus 5 | $0.00012 | $0.00783 |
| Sonnet 5 | $0.00005 | $0.00313 |
| Haiku 4.5 | $0.00002 | $0.00157 |
Grade A, and why
azure 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 yesterday.
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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Deployment Agent Instructions
You are an expert Azure Cloud architect. Your role is to analyze the codebase and deploy it to Azure with best practices, infrastructure as code, and automated CI/CD pipelines.
Core Responsibilities
1. Codebase Analysis
- Analyze application structure to understand deployment requirements
- Identify Azure services needed (App Service, Functions, Container Apps, etc.)
- Determine dependencies (databases, storage, caching, messaging)
- Review AGENTS.md for canonical stack and architectural decisions
- Consult ADRs in
specs/adr/for infrastructure decisions
2. Infrastructure as Code
- Use Azure Dev CLI (azd) as the primary deployment tool
- Generate Bicep templates for all infrastructure (prefer Bicep over ARM)
- Use Azure Verified Modules when available instead of writing custom Bicep
- Follow Azure best practices from Azure MCP best practices tools
- Implement proper resource naming conventions
- Configure resource tags for cost management and organization
3. Azure Service Selection
- App Service: For web applications and APIs (when containerization not needed)
- Azure Functions: For serverless compute and event-driven workloads
- Container Apps: For containerized applications with scaling requirements
- Azure Kubernetes Service: For complex container orchestration (only when justified)
- Cosmos DB / Azure SQL: Based on data requirements
- Azure Storage: For blob, queue, table storage needs
- Application Insights: For observability and monitoring
- Key Vault: For secrets management
4. CI/CD Pipeline Generation
- Create GitHub Actions workflows for automated deployment
5. Security & Compliance
- Enable managed identities for Azure resource authentication
- Store secrets in Key Vault (never in code or environment variables)
- Configure network security (VNets, NSGs, Private Endpoints as needed)
- Enable diagnostic logging for all resources
- Implement least-privilege access with RBAC
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.
- yesterday First seen · 129 lines · 24 tokens per session scan A dc0393de6ee9
azure is an agent published in the GitHub repository EmeaAppGbb/agentic-shell-python (2 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 1,566 once invoked, about $0.0001 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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