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 AnthonyAlcaraz/agentic-graph-rag-skills --skill subgraph-access-controlgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote 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/anthonyalcaraz/agentic-graph-rag-skills/subgraph-access-control)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/subgraph-access-control"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/subgraph-access-control/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/anthonyalcaraz/agentic-graph-rag-skills/subgraph-access-control"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/subgraph-access-control.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 | $0.00152 | $0.02253 |
| Opus 5 | $0.00076 | $0.01126 |
| Sonnet 5 | $0.00030 | $0.00451 |
| Haiku 4.5 | $0.00015 | $0.00225 |
Grade A, and why
subgraph-access-control 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subgraph Access Control
Overview
An agent with unrestricted access to your knowledge graph is a liability. The dense relationships that make agentic reasoning powerful also mean a single unscoped query can traverse from a public product catalog to internal cost data to employee records. In a graph, everything is reachable from everything else.
Graph databases have mature access-control primitives. Neo4j Enterprise supports privileges at the node-label, relationship-type, and property level: you GRANT traverse rights on specific labels, DENY read access to specific properties, and combine them into role-based policies. A critical design feature is security transparency — when a role lacks permission to see a node, the node is invisible, not access-denied. The agent cannot distinguish data that does not exist from data it is not allowed to see, which blocks an autonomous agent from probing access boundaries as part of its reasoning.
This skill covers the three governance concerns from the chapter: subgraph-level access control (a role per persona, bound at connection time), PII and retention (Rehmer's Privacy-by-Architecture UUID separation with GDPR soft/hard erasure), and the execution graph as a compliance artifact (KG.GOV governance metadata that answers auditor questions as a graph query).
When to Use
- A graph-backed agent serves multiple personas (SRE, finance) who need different views of the same graph.
- You must prove which role accessed which data for a compliance review.
- The graph holds PII and you need GDPR Article 17 erasure that does not cascade across densely connected relationships.
- You are testing that an access policy does not accidentally starve the agent's reasoning (the "governance blocks the agent" pitfall).
Phrases that should invoke this skill: "who can see what in the graph", "subgraph access control", "role-based graph permissions", "GDPR delete from the graph", "PII in the knowledge graph", "compliance audit of agent decisions".
What ships with it
2 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.
- 10d ago First seen · 172 lines · 152 tokens per session scan A 3a546daed5e8
subgraph-access-control is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 152 tokens to every session and 2,253 once invoked, about $0.0008 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.
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