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 Robotti-io/copilot-security-instructions --skill access-control-reviewgit clone --depth 1 https://github.com/Robotti-io/copilot-security-instructionsWrote 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/robotti-io/copilot-security-instructions/access-control-review)<a href="https://agentmods.dev/skills/robotti-io/copilot-security-instructions/access-control-review"><img src="https://agentmods.dev/badge/skills/robotti-io/copilot-security-instructions/access-control-review/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/robotti-io/copilot-security-instructions/access-control-review"><img src="https://agentmods.dev/badge/skills/robotti-io/copilot-security-instructions/access-control-review.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 | $0.00027 | $0.04452 |
| Opus 5 | $0.00014 | $0.02226 |
| Sonnet 5 | $0.00005 | $0.00890 |
| Haiku 4.5 | $0.00003 | $0.00445 |
Grade A, and why
access-control-review 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 9d 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 — 616 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Access Control Review
Purpose
Provide a repeatable, repository-grounded workflow for reviewing identity, access control, and authorization architecture, then producing durable Markdown output with validated Mermaid diagrams using local Node.js helper scripts instead of VS Code extension-based Mermaid tools.
When to use
Use this skill when you need to:
- analyze how identity, access control, and authorization are implemented across a repository or PR diff
- map roles, groups, permissions, claims, scopes, and policies to concrete enforcement points
- build an endpoint-to-access-control view grounded in repository evidence
- produce a Markdown review plus Mermaid diagrams that can be validated from a CLI-friendly workflow
Inputs to collect
- repository or PR diff in scope
- authentication source and identity provider details, if known
- principal types that matter for the review, such as end users, administrators, support users, service identities, and background jobs
- known sources of truth for groups, roles, permissions, claims, scopes, or policy assignments
- environment or tenant-specific variance, if any
- repository evidence from code, config, IaC, manifests, docs, route declarations, tests, and policy definitions
How to use
- Review the current repository and PR diff, if available, before relying on operator answers.
- Persist the output as a root-level Markdown file named
Access Control Architecture Review - YYYY-MM-DD.mdwhen the environment supports writing files. - Use evidence first and cite file paths and, when possible, line ranges.
- Mark anything that cannot be confirmed from repository evidence as
ASSUMPTIONorUNKNOWN. - Perform the mandatory intake before finalizing the report, choosing only the questions that materially change the interpretation of identity source, authorization model, source of truth, enforcement location, or environment behavior.
- Use the local Mermaid helper scripts in this skill instead of VS Code extension-based Mermaid tools.
- Validate every Mermaid diagram before finalizing the report.
- Do not generate code changes unless explicitly requested.
What ships with it
3 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.
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.
- 9d ago First seen · 616 lines · 27 tokens per session scan A 050e2562aa00
access-control-review is a skill published in the GitHub repository Robotti-io/copilot-security-instructions (42 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 4,452 once invoked, about $0.0001 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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