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 j4flmao/agent-skills --skill graph-databasegit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/graph-database)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/graph-database"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/graph-database/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/j4flmao/agent-skills/graph-database"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/graph-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00130 | $0.05386 |
| Opus 5 | $0.00065 | $0.02693 |
| Sonnet 5 | $0.00026 | $0.01077 |
| Haiku 4.5 | $0.00013 | $0.00539 |
Grade A, and why
data-graph-database 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 — 501 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Graph Database
Purpose
Design graph data models for connected domains (social networks, recommendation engines, knowledge graphs, fraud detection) with optimal traversal patterns, platform selection, and scaling strategy.
Agent Protocol
Trigger
Exact user phrases: "Neo4j", "Amazon Neptune", "JanusGraph", "graph database", "graph model", "Cypher", "Gremlin", "RDF", "SPARQL", "graph traversal", "property graph", "knowledge graph", "graph schema", "node label", "relationship", "graph query".
Input Context
Before activating, verify:
- Domain data (nodes, relationships, properties, cardinality)
- Query patterns (depth of traversal, path enumeration, aggregations)
- Transaction volume (reads/sec, writes/sec, complexity)
- Consistency requirements (ACID vs CQRS)
- Existing graph platform or greenfield
- Integration points (existing databases, streaming, ML pipelines)
- Expected graph size (millions, billions of nodes/edges)
Output Artifact
Graph data model with node labels, relationship types, traversal patterns, and platform-specific deployment config.
Response Format
// Neo4j schema + constraints + traversal queries
// Gremlin traversals for Neptune/JanusGraph
# JanusGraph storage backend config
# Neptune cluster config
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Graph data model designed (property graph or RDF)
- Node labels and relationship types defined with constraints
- Indexes created for high-traffic property lookups
- Traversal patterns optimized for depth and cardinality
- Platform selected (Neo4j, Neptune, JanusGraph) with rationale
- Scaling strategy defined (sharding, replication, caching)
- Query performance verified with PROFILE/explain
Max Response Length
300 lines of schema and queries.
Workflow
Step 1: Graph Data Modeling
Property graph: nodes (entities) with labels, relationships (edges) with types, properties on both. RDF: subject-predicate-object triples with URIs. Design around traversal patterns: ask "what queries will traverse from this node through which relationships?" Favor nodes for entities, relationships for connections, properties for attributes.
What ships with it
8 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/graph-algorithms.md 948 B
- references/graph-data-modeling.md 13 KB
- references/graph-modeling.md 4.9 KB
- references/graph-performance.md 884 B
- references/graph-platforms.md 3.5 KB
- references/graph-query-performance.md 13 KB
- references/graph-use-cases.md 9.2 KB
- references/query-patterns.md 8.6 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.
- 9d ago First seen · 501 lines · 130 tokens per session scan A 453947cf255a
data-graph-database is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 6d ago), licensed MIT. It adds 130 tokens to every session and 5,386 once invoked, about $0.0006 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 2026-09-03.
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