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 instructions/enuno/tailscale-acl/agents-mdgit clone --depth 1 https://github.com/enuno/tailscale-aclWhat 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 | $0.01067 | $0.01067 |
| Opus 5 | $0.00534 | $0.00534 |
| Sonnet 5 | $0.00213 | $0.00213 |
| Haiku 4.5 | $0.00107 | $0.00107 |
Grade A, and why
tailscale-acl AGENTS.md 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.
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Instructions for Tailscale ACL Repository
Project Overview
This repository manages Tailscale Access Control Lists through GitOps methodology, enabling infrastructure-as-code for network security policies. The primary goal is maintaining secure, reliable, and auditable network access control.
Core Technologies
- Tailscale: Zero-trust network access platform with WireGuard-based mesh networking
- GitOps: Infrastructure management through Git workflows and automated deployment
- HuJSON: Human-readable JSON format for ACL policies with comments and trailing commas
- GitHub Actions: CI/CD pipeline for ACL validation and deployment
Repository Structure
Essential Files
policy.hujson: Main Tailscale ACL policy file (NEVER rename this file).github/workflows/tailscale.yml: GitHub Actions workflow for ACL synchronizationREADME.md: Project documentation and setup instructions
Configuration Files
.cursorrules,.clinerules: AI agent-specific configurations.yamllint,.prettierrc: Code quality and formatting tools.editorconfig: Cross-editor consistency settings.gitignore: Git ignore patterns for clean repository
Development Workflow
1. ACL Policy Changes
- Edit
policy.hujsonin feature branches only - Use HuJSON format with comments and trailing commas
- Include test cases for all new access rules
- Follow least privilege security principles
2. Git Workflow
# Create feature branch
git checkout -b feature/add-user-group
# Make changes to policy.hujson
# Commit with descriptive messages
git commit -m "feat: add engineering team access to production servers"
# Push and create pull request
git push origin feature/add-user-group
3. CI/CD Process
- Pull requests trigger ACL validation (test action)
- Merging to main triggers deployment (apply action)
- Monitor GitHub Actions logs for deployment status
- Failed deployments prevent policy updates
Tailscale ACL Best Practices
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 · 144 lines · 1,067 tokens per session scan A 5614f2fbd915
tailscale-acl AGENTS.md is an instructions file published in the GitHub repository enuno/tailscale-acl (5 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 1,067 tokens to every session, about $0.0053 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 instructions, from other repositories
yoloai CLAUDE.md
Instructions for kstenerud/yoloai, covering claude code specifics and the quality gate runs itself here.
agent-rules child-process.instructions.md
Instructions for lirantal/agent-rules, covering system processes secure coding guidelines, your mission and spawning system processes.
AgentsMesh CLAUDE.md
Claude Code instructions for AgentsMesh/AgentsMesh, covering claude.md, project overview, components, development environment and quick start.
agentx AGENTS.md
Instructions for sageox/agentx, covering agent instructions, quick reference, releases, code quality and landing the plane (session completion).
AI-AGENT-SKILLS CLAUDE.md
Claude Code instructions for vignesh2027/AI-AGENT-SKILLS, covering ai agent skills — claude instructions, what this repo is, contribution rules, skill files must and what to avoid.
n8n-starter CLAUDE.md
Instructions for gabry-ts/n8n-starter, covering claude.md, project overview, repository structure, workflow files and credential management.