Borrowing it
Nothing to install: this file belongs to tan-yong-sheng/ai-vision-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tan-yong-sheng/ai-vision-mcp/main/.claude/skills/access-control-policy-design/SKILL.mdgit clone --depth 1 https://github.com/tan-yong-sheng/ai-vision-mcpWrote 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/tan-yong-sheng/ai-vision-mcp/access-control-policy-design)<a href="https://agentmods.dev/skills/tan-yong-sheng/ai-vision-mcp/access-control-policy-design"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/access-control-policy-design/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/tan-yong-sheng/ai-vision-mcp/access-control-policy-design"><img src="https://agentmods.dev/badge/skills/tan-yong-sheng/ai-vision-mcp/access-control-policy-design.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 | $0.00207 | $0.01870 |
| Opus 5 | $0.00103 | $0.00935 |
| Sonnet 5 | $0.00041 | $0.00374 |
| Haiku 4.5 | $0.00021 | $0.00187 |
Grade A, and why
access-control-policy-design 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 12d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Access Control Policy Design — Skill Navigator
What this skill does
Acts as a senior IAM/authorization architect. Covers all major access control paradigms, hybrid composition patterns, policy engines, compliance mapping, implementation code, and decision frameworks for modern apps (SaaS, multi-tenant, microservices, cloud-native).
Core Mental Model (always apply this first)
Every access control system answers one runtime question:
Should subject S perform action A on resource R right now?
Models differ in how that decision is made. They are layers, not competitors:
| Layer | Model | Answers |
|---|---|---|
| Structure | RBAC | Who are you organizationally? |
| Context | ABAC | What conditions apply right now? |
| Governance | PBAC | Who controls the rules and how? |
| Relationships | ReBAC | How do entities connect to resources? |
| Precision | ACL | What's explicitly allowed on this object? |
| Delegation | DAC | What has the owner chosen to share? |
Key principle: Most mature systems use 3–4 of these together, with PBAC as the governance shell wrapping the others. Start simple (RBAC), add layers as complexity demands.
Quick Decision Matrix
| Scenario | Recommended Model(s) | Reference File |
|---|---|---|
| Internal tool, stable job roles | RBAC | 01-rbac.md |
| Multi-tenant SaaS | RBAC + ABAC | 01-rbac.md, 02-abac.md |
| Healthcare / Finance data | ABAC + PBAC | 02-abac.md, 03-pbac-opa.md |
| Collaborative hierarchical content | ReBAC | 04-rebac-zanzibar.md |
| Object-level sharing exceptions | ACL on top of RBAC | 05-acl-dac.md |
| Consumer app with owner sharing | DAC + guardrails | 05-acl-dac.md |
| Many microservices, many teams | PBAC (OPA/Cedar) | 03-pbac-opa.md |
| Zero Trust architecture | RBAC + ABAC + PBAC | 06-hybrid-patterns.md |
| SOC2 / HIPAA / GDPR compliance | PBAC + audit trail | 08-compliance.md |
| Early-stage startup (<50 users) | RBAC only | 01-rbac.md |
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/01-rbac.md 12 KB
- references/02-abac.md 16 KB
- references/03-pbac-opa.md 15 KB
- references/04-rebac-zanzibar.md 15 KB
- references/05-acl-dac.md 13 KB
- references/06-hybrid-patterns.md 13 KB
- references/07-policy-engines.md 13 KB
- references/08-compliance.md 19 KB
- references/09-code-examples.md 24 KB
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.
- 12d ago First seen · 149 lines · 207 tokens per session scan A e6ab349cc89f
access-control-policy-design is a skill published in the GitHub repository tan-yong-sheng/ai-vision-mcp (78 stars, last pushed 5mo ago), licensed MIT. It adds 207 tokens to every session and 1,870 once invoked, about $0.0010 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 2026-08-30.
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