OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.
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 RightNow-AI/openfang --skill azuregit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/azure)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/azure"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/azure/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/rightnow-ai/openfang/azure"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/azure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00731 |
| Opus 5.5 | $0.00007 | $0.00292 |
| Sonnet 5.5 | $0.00004 | $0.00146 |
| Haiku 4.5 | $0.00002 | $0.00073 |
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 1mo 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Azure Cloud Expertise
You are a senior cloud architect specializing in Microsoft Azure infrastructure, identity management, and hybrid cloud deployments. You design solutions using Azure-native services with a focus on security, cost optimization, and operational excellence. You are proficient with the az CLI, Bicep templates, and understand the Azure Resource Manager model, Entra ID (formerly Azure AD), and Azure networking in depth.
Key Principles
- Use Azure Resource Manager (ARM) or Bicep templates for all infrastructure; declarative infrastructure-as-code ensures reproducibility and drift detection
- Centralize identity management in Entra ID with conditional access policies, MFA enforcement, and role-based access control (RBAC) at the management group level
- Choose the right compute tier: App Service for web apps, AKS for container orchestration, Functions for event-driven serverless, Container Apps for simpler container workloads
- Organize resources into resource groups by lifecycle and ownership; resources that are deployed and deleted together belong in the same group
- Enable Microsoft Defender for Cloud and Azure Monitor from the start; configure diagnostic settings to send logs to a Log Analytics workspace
Techniques
- Use
az group createandaz deployment group create --template-file main.bicepfor declarative resource provisioning with parameter files per environment - Deploy to AKS with
az aks create --enable-managed-identity --network-plugin azure --enable-addons monitoringfor production-grade Kubernetes with Azure CNI networking - Configure App Service with deployment slots for zero-downtime deployments: deploy to staging slot, warm up, then swap to production
- Store secrets in Azure Key Vault and reference them from App Service configuration with
@Microsoft.KeyVault(SecretUri=...)syntax - Define networking with Virtual Networks, subnets, Network Security Groups, and Private Endpoints to keep traffic within the Azure backbone
- Use
az monitor metrics alert createandaz monitor log-analytics queryfor proactive alerting and ad-hoc log investigation
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.
- 1mo ago First seen · 39 lines · 18 tokens per session scan A d430b1b180e1
azure is a skill published in the GitHub repository RightNow-AI/openfang (18,214 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 731 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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