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 | $0.00042 | $0.01042 |
| Opus 5.5 | $0.00017 | $0.00417 |
| Sonnet 5.5 | $0.00008 | $0.00208 |
| Haiku 4.5 | $0.00004 | $0.00104 |
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 25d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Multi-Model Data Architectures: Unifying document (JSON), graph, and key-value models into a single query engine with ACID transactions.
- Complex Graph Traversals: Executing high-performance depth-first and breadth-first traversals with edge attributes in AQL.
- Unified Knowledge Graphs: Storing entity metadata as rich documents while querying interconnected relationships as graphs.
- Search & Full-Text Integration: Leveraging ArangoSearch for integrated full-text indexing, BM25 scoring, and geo-spatial queries.
Quick Start
// 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 Document & Edge Collections
Documents store unstructured JSON entities; Edge collections define directed connections between documents using _from and _to attributes:
// Vertex Document (users/alice)
{ "_key": "alice", "name": "Alice Mercer", "role": "Lead Architect" }
// Edge Document (knows/edge_1)
{ "_from": "users/alice", "_to": "users/bob", "relationship": "mentor", "since": 2024 }
ArangoDB Query Language (AQL) Graph Traversal
Traverses interconnected relationship graphs up to N hops:
// 1 to 3 hops outward traversal from Alice
FOR v, e, p IN 1..3 OUTBOUND 'users/alice' GRAPH 'socialGraph'
FILTER e.relationship == 'mentor'
RETURN {
colleague: v.name,
distance: LENGTH(p.edges),
path: p.vertices[*].name
}
Distributed Cluster Sharding (OneShard & SmartGraphs)
Ensures interconnected graph vertices and edges are colocated on identical physical DB servers to eliminate network hop penalties:
// Creates a SmartGraph partitioned by customer tenant ID
const graph = db._createSmartGraph(
"tenantSocialGraph",
[{ collection: "members", from: ["users"], to: ["users"] }],
{ smartGraphAttribute: "tenantId" },
);
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.
- 25d ago Changed 17d4c048a296
- 1mo ago Changed · +59 lines · +24 tokens per session 46a89f06e29f
- 1mo ago First seen · 55 lines · 18 tokens per session scan A 71fb2c85090a
arangodb is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 26d ago), licensed MIT. It adds 42 tokens to every session and 1,042 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.
Other skills, from other repositories
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
awesome-database-audit
Read-only audit of a database layer: schema anti-patterns, query and index fit, integrity, concurrency, migrations, tenancy, with a verdict. Use when asked to review a schema, migrations or data model.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.