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 rules/authensor/authensor/cursorrulesgit clone --depth 1 https://github.com/AUTHENSOR/AUTHENSORWhat 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 | $0.00237 | $0.00237 |
| Opus 5 | $0.00118 | $0.00118 |
| Sonnet 5 | $0.00047 | $0.00047 |
| Haiku 4.5 | $0.00024 | $0.00024 |
Grade A, and why
cursorrules 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 2d 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
Authensor Safety Stack
When working with AI agents in this project, use Authensor for safety:
Key packages
- @authensor/engine: Pure policy evaluation (synchronous, zero dependencies)
- @authensor/aegis: Content safety scanning (prompt injection, PII, credentials)
- @authensor/sentinel: Real-time behavioral monitoring
- @authensor/sdk: TypeScript client SDK
- @authensor/mcp-server: MCP tool authorization gateway
Integration pattern
Every agent tool call should be wrapped with Authensor:
- Create an ActionEnvelope with action type, resource, and principal
- Evaluate against policy: engine.evaluate(envelope, [policy])
- Check decision.outcome: 'allow' | 'deny' | 'require_approval'
- Create a receipt for the audit trail
Policy format
Policies are YAML with rules containing id, name, effect (allow/deny/require_approval), and conditions. Default effect should always be 'deny' (fail-closed).
Quick start
npx @authensor/create-authensor my-agent
Docs
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.
- 2d ago First seen · 28 lines · 237 tokens per session scan A 5eb32a62ed07
cursorrules is a cursor rule published in the GitHub repository AUTHENSOR/AUTHENSOR (2 stars, last pushed 1mo ago), licensed MIT. It adds 237 tokens to every session, about $0.0012 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
selectools-testing
Testing conventions and patterns for selectools.
selectools-agent-core
Rules for working with the agent core loop in selectools.
selectools-development
Selectools development conventions, testing requirements, and release process.
selectools-docs
Documentation standards for selectools docs and MkDocs site.
selectools-providers
Rules for working with LLM provider adapters in selectools.
agentguard
AgentGuard local-first safety.