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 G1Joshi/Agent-Skills --skill arangodbgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/arangodb)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/arangodb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/arangodb/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/g1joshi/agent-skills/arangodb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/arangodb.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 | $0.00018 | $0.00429 |
| Opus 5 | $0.00009 | $0.00215 |
| Sonnet 5 | $0.00004 | $0.00086 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
arangodb 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 6d 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.
What it actually says
ArangoDB
ArangoDB is a native multi-model database. It allows you to store data as Key/Values, JSON Documents, and Graphs, and query them all with a single language (AQL).
When to Use
- Polyglot Persistence: When you need Documents AND Graph traversals but don't want to manage two databases (Mongo + Neo4j).
- GraphRAG (2025): ArangoDB 3.12+ has native Vector Search combined with Graph capabilities for AI.
- Microservices: Reduces "Database Sprawl" by serving multiple data access patterns from one cluster.
Quick Start (AQL)
// AQL (ArangoDB Query Language) - SQL-like
FOR u IN users
FILTER u.active == true
FOR order IN OUTBOUND u orders
RETURN { user: u.name, order: order.product }
Core Concepts
Multi-Model Core
One engine, multiple APIs. Storing a specific "Edge" collection turns your Documents into a Graph automatically.
SmartGraphs
Sharding feature. Keeps related graph data (e.g., Users and their Orders) on the same server to avoid network hops during traversal (Enterprise).
Foxx
A microservices framework running inside the DB (V8 engine). Write endpoints in JS that run close to data.
Best Practices (2025)
Do:
- Use AQL: It is powerful and standardizes Graph and Document queries.
- Use Edge Collections: Explicitly define edges to enable graph features.
- Use Analyzer for Search: ArangoSearch (integrated) offers full-text search capabilities like Elastic.
Don't:
- Don't ignore sharding: Graph traversals across network shards are slow. Plan your shard keys (SmartGraphs) carefully.
References
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.
- 6d ago First seen · 55 lines · 18 tokens per session scan A 71fb2c85090a
arangodb is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 429 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 2026-09-03.
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