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 ancoleman/ai-design-components --skill using-graph-databasesgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/using-graph-databases)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/using-graph-databases"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-graph-databases/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/ancoleman/ai-design-components/using-graph-databases"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-graph-databases.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 | $0.00056 | $0.03289 |
| Opus 5.5 | $0.00022 | $0.01316 |
| Sonnet 5.5 | $0.00011 | $0.00658 |
| Haiku 4.5 | $0.00006 | $0.00329 |
Grade A, and why
using-graph-databases 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 1mo 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 — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Graph Databases
Purpose
This skill guides selection and implementation of graph databases for applications where relationships between entities are first-class citizens. Unlike relational databases that model relationships through foreign keys and joins, graph databases natively represent connections as properties, enabling efficient traversal-heavy queries.
When to Use This Skill
Use graph databases when:
- Deep relationship traversals (4+ hops): "Friends of friends of friends"
- Variable/evolving relationships: Schema changes don't break existing queries
- Path finding: Shortest route, network analysis, dependency chains
- Pattern matching: Fraud detection, recommendation engines, access control
Do NOT use graph databases when:
- Fixed schema with shallow joins (2-3 tables) → Use PostgreSQL
- Primarily aggregations/analytics → Use columnar databases
- Key-value lookups only → Use Redis/DynamoDB
Quick Decision Framework
DATA CHARACTERISTICS?
├── Fixed schema, shallow joins (≤3 hops)
│ └─ PostgreSQL (relational)
│
├── Already on PostgreSQL + simple graphs
│ └─ Apache AGE (PostgreSQL extension)
│
├── Deep traversals (4+ hops) + general purpose
│ └─ Neo4j (battle-tested, largest ecosystem)
│
├── Multi-model (documents + graph)
│ └─ ArangoDB
│
├── AWS-native, serverless
│ └─ Amazon Neptune
│
└── Real-time streaming, in-memory
└─ Memgraph
Core Concepts
Property Graph Model
Graph databases store data as:
- Nodes (vertices): Entities with labels and properties
- Relationships (edges): Typed connections with properties
- Properties: Key-value pairs on nodes and relationships
(Person {name: "Alice", age: 28})-[:FRIEND {since: "2020-01-15"}]->(Person {name: "Bob"})
Query Languages
| Language | Databases | Readability | Best For |
|---|---|---|---|
| Cypher | Neo4j, Memgraph, AGE | ⭐⭐⭐⭐⭐ SQL-like | General purpose |
| Gremlin | Neptune, JanusGraph | ⭐⭐⭐ Functional | Cross-database |
| AQL | ArangoDB | ⭐⭐⭐⭐ SQL-like | Multi-model |
| SPARQL | Neptune, RDF stores | ⭐⭐⭐ W3C standard | Semantic web |
What ships with it
10 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.
- examples/knowledge-graph/hybrid_vector_graph.py 14 KB runs code
- examples/social-graph/python-neo4j/main.py 12 KB runs code
- examples/social-graph/schema.cypher 9.5 KB
- examples/social-graph/typescript-neo4j/index.ts 12 KB runs code
- outputs.yaml 3.6 KB
- references/arangodb.md 15 KB
- references/cypher-patterns.md 16 KB
- references/graph-modeling.md 17 KB
- references/neo4j.md 12 KB
- scripts/validate_graph_schema.py 16 KB runs code
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.
- 1mo ago First seen · 474 lines · 56 tokens per session scan A 625fb2bb670c
using-graph-databases is a skill published in the GitHub repository ancoleman/ai-design-components (525 stars, last pushed 10mo ago), licensed MIT. It adds 56 tokens to every session and 3,289 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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