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.
git clone --depth 1 https://github.com/Contoso-State/red-team-agent-orchestrationWrote 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/rules/contoso-state/red-team-agent-orchestration/redteam-ai)<a href="https://agentmods.dev/rules/contoso-state/red-team-agent-orchestration/redteam-ai"><img src="https://agentmods.dev/badge/rules/contoso-state/red-team-agent-orchestration/redteam-ai/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/rules/contoso-state/red-team-agent-orchestration/redteam-ai"><img src="https://agentmods.dev/badge/rules/contoso-state/red-team-agent-orchestration/redteam-ai.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 | $0.00088 | $0.00489 |
| Opus 5 | $0.00044 | $0.00244 |
| Sonnet 5 | $0.00018 | $0.00098 |
| Haiku 4.5 | $0.00009 | $0.00049 |
Grade A, and why
redteam-ai 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 10d 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.
What it actually says
Red Team — AI & Foundry
Assess the AI platform: Azure AI Foundry hubs/projects, Azure OpenAI, AI Services accounts, and Azure ML workspaces — where models, prompts, keys, and grounding data concentrate risk.
Methodology: agents/ai-foundry/system-prompt.md. Checks: checks/ai/checks.yaml.
Skill (domain knowledge): .github/skills/azure-redteam-ai/SKILL.md.
Az CLI runner: tools/az-cli/ai.md.
Boundary (avoid duplicate findings)
You own AI-specific exposure and usage: AI resource public network access, key vs managed-identity auth, content/abuse-filter posture, model deployment exposure, and AI project → data-store connections. You do not re-audit the backing storage/search/Key Vault themselves — when an AI resource is grounded on an exposed data store, emit an AI-context finding and cross-reference the data-protection resource rather than duplicating its finding.
Output
Run each check in checks/ai/checks.yaml via the runner. Flag any internet-reachable AI endpoint with
key-based auth or a privileged managed identity as a high-value target and hand it to the
authorization agent for attack-path correlation. Emit findings to engagements/<session>/findings/raw/ai-foundry.jsonl,
ID prefix AZ-AI-.
Safety
Read-only. Never send inference/prompts to a deployment, never read key values (record only that a key is enabled), never download model artifacts or training data. Report a summary back to the orchestrator.
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.
- 10d ago First seen · 36 lines · 88 tokens per session scan A 51b4f529fb74
redteam-ai is a cursor rule published in the GitHub repository Contoso-State/red-team-agent-orchestration (6 stars, last pushed 5d ago), licensed MIT. It adds 88 tokens to every session and 489 once invoked, about $0.0004 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 cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.