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 Arvo-AI/aurora --skill teamsgit clone --depth 1 https://github.com/Arvo-AI/auroraWrote 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/arvo-ai/aurora/teams)<a href="https://agentmods.dev/skills/arvo-ai/aurora/teams"><img src="https://agentmods.dev/badge/skills/arvo-ai/aurora/teams/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/arvo-ai/aurora/teams"><img src="https://agentmods.dev/badge/skills/arvo-ai/aurora/teams.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 | $0.00014 | $0.00333 |
| Opus 5.5 | $0.00006 | $0.00133 |
| Sonnet 5.5 | $0.00003 | $0.00067 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
teams 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 today.
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.
What it actually says
Microsoft Teams
Aurora acts as a teammate in connected Microsoft Teams channels. In a channel, users
@mention the Aurora Teams app (same as @mentioning the Slack bot). Aurora replies
as that app, not as the person who connected OAuth. Use Teams tools to list channels,
read history, and post conclusions when the org's Microsoft Teams memory
(context/Microsoft Teams) says to speak.
Tools
get_connected_teams_channels— channels Aurora may post to (with descriptions)list_teams_channels— live channel list (names/descriptions, no routing text)get_teams_channel_history— recent messages in a channel (requiresteam_id,channel_id)get_teams_thread_replies— replies under a messagepost_teams_message— post or thread a reply (reply_to_idfor follow-ups)
RCA workflow (read-only)
Search Teams channel history for human discussion around the incident window. Do not post during RCA unless explicitly in Agent mode and the user asks.
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.
- today First seen · 39 lines · 14 tokens per session scan A b923a00df8f2
teams is a skill published in the GitHub repository Arvo-AI/aurora (434 stars, last pushed today), licensed Apache-2.0. It adds 14 tokens to every session and 333 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 2026-10-17.
Other skills, from other repositories
terraform-infrastructure-as-code
Comprehensive Terraform Infrastructure as Code skill covering resources, modules, state management, workspaces, providers, and advanced patterns for cloud-agnostic infrastructure deployment.
isaac-automator
Deploy and operate a cloud Isaac Workstation with Isaac Automator: provision a GPU VM running Isaac Sim, Isaac Lab, and/or Isaac Lab Arena on AWS, GCP, Azure, or Alibaba Cloud, connect to it, move data in and out, control cost with stop/start, repair, import existing deployments, and destroy. Use when the user wants a…
cloud-iam-deep
GCP/AWS/Azure cloud exploitation -- Cloud Functions, Firestore, Cloud Run, S3, MinIO, Blob Storage, SA keys.
check-ai-budget
Use before starting a long or expensive task (a large refactor, a change across many files, a long agent loop, a big test or eval run, a batch of LLM calls), and when asked how much AI budget is left.
performing-cloud-forensics-investigation
Conduct forensic investigations in cloud environments by collecting and analyzing logs, snapshots, and metadata from AWS, Azure, and GCP services.
databricks-platform-provisioning
Provision and test Databricks workspaces. Use when the user asks to create a workspace, set up a new environment, provision infrastructure, bootstrap Databricks, test a workspace, verify a deployment, or run validation checks against a Databricks workspace. Covers Azure, AWS, and GCP.