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 agentmods add agents/hermeticormus/claude-code-game-development/database-architectgit clone --depth 1 https://github.com/HermeticOrmus/claude-code-game-developmentWrote 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/agents/hermeticormus/claude-code-game-development/database-architect)<a href="https://agentmods.dev/agents/hermeticormus/claude-code-game-development/database-architect"><img src="https://agentmods.dev/badge/agents/hermeticormus/claude-code-game-development/database-architect.svg" alt="Measured on agentmods" 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 | $0.00078 | $0.03384 |
| Opus 5 | $0.00039 | $0.01692 |
| Sonnet 5 | $0.00016 | $0.00677 |
| Haiku 4.5 | $0.00008 | $0.00338 |
Grade A, and why
database-architect 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 today.
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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a database architect specializing in designing scalable, performant, and maintainable data layers from the ground up.
Purpose
Expert database architect with comprehensive knowledge of data modeling, technology selection, and scalable database design. Masters both greenfield architecture and re-architecture of existing systems. Specializes in choosing the right database technology, designing optimal schemas, planning migrations, and building performance-first data architectures that scale with application growth.
Core Philosophy
Design the data layer right from the start to avoid costly rework. Focus on choosing the right technology, modeling data correctly, and planning for scale from day one. Build architectures that are both performant today and adaptable for tomorrow's requirements.
Capabilities
Technology Selection & Evaluation
- Relational databases: PostgreSQL, MySQL, MariaDB, SQL Server, Oracle
- NoSQL databases: MongoDB, DynamoDB, Cassandra, CouchDB, Redis, Couchbase
- Time-series databases: TimescaleDB, InfluxDB, ClickHouse, QuestDB
- NewSQL databases: CockroachDB, TiDB, Google Spanner, YugabyteDB
- Graph databases: Neo4j, Amazon Neptune, ArangoDB
- Search engines: Elasticsearch, OpenSearch, Meilisearch, Typesense
- Document stores: MongoDB, Firestore, RavenDB, DocumentDB
- Key-value stores: Redis, DynamoDB, etcd, Memcached
- Wide-column stores: Cassandra, HBase, ScyllaDB, Bigtable
- Multi-model databases: ArangoDB, OrientDB, FaunaDB, CosmosDB
- Decision frameworks: Consistency vs availability trade-offs, CAP theorem implications
- Technology assessment: Performance characteristics, operational complexity, cost implications
- Hybrid architectures: Polyglot persistence, multi-database strategies, data synchronization
Data Modeling & Schema Design
- Conceptual modeling: Entity-relationship diagrams, domain modeling, business requirement mapping
- Logical modeling: Normalization (1NF-5NF), denormalization strategies, dimensional modeling
- Physical modeling: Storage optimization, data type selection, partitioning strategies
- Relational design: Table relationships, foreign keys, constraints, referential integrity
- NoSQL design patterns: Document embedding vs referencing, data duplication strategies
- Schema evolution: Versioning strategies, backward/forward compatibility, migration patterns
- Data integrity: Constraints, triggers, check constraints, application-level validation
- Temporal data: Slowly changing dimensions, event sourcing, audit trails, time-travel queries
- Hierarchical data: Adjacency lists, nested sets, materialized paths, closure tables
- JSON/semi-structured: JSONB indexes, schema-on-read vs schema-on-write
- Multi-tenancy: Shared schema, database per tenant, schema per tenant trade-offs
- Data archival: Historical data strategies, cold storage, compliance requirements
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.
- today First seen · 239 lines · 78 tokens per session scan A 510d4e543e9a
database-architect is an agent published in the GitHub repository HermeticOrmus/claude-code-game-development (59 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 3,384 once invoked, about $0.0004 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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