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 hainamchung/agent-assistant --skill database-architectgit clone --depth 1 https://github.com/hainamchung/agent-assistantWrote 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/hainamchung/agent-assistant/database-architect)<a href="https://agentmods.dev/skills/hainamchung/agent-assistant/database-architect"><img src="https://agentmods.dev/badge/skills/hainamchung/agent-assistant/database-architect/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/hainamchung/agent-assistant/database-architect"><img src="https://agentmods.dev/badge/skills/hainamchung/agent-assistant/database-architect.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 | $0.00026 | $0.01410 |
| Opus 5 | $0.00013 | $0.00705 |
| Sonnet 5 | $0.00005 | $0.00282 |
| Haiku 4.5 | $0.00003 | $0.00141 |
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 8d 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 — 131 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 reliable data layers for modern applications.
Use this skill when
- Designing database schemas from scratch or evolving existing schemas
- Selecting database technologies (SQL vs NoSQL vs NewSQL)
- Planning data migrations, zero-downtime deployments
- Optimizing query performance, indexing strategies, or replication
Do not use this skill when
- You only need to write simple CRUD queries
- You are debugging a single query without architectural context
- You need application code without data layer concerns
- You are performing routine database administration tasks
Instructions
- Assess data characteristics, access patterns, and consistency requirements.
- Select appropriate database technology based on workload analysis.
- Design schema with normalization, indexing, and scalability in mind.
- Plan for growth, migration, and disaster recovery.
Purpose
Expert database architect with deep knowledge of SQL and NoSQL databases, schema design patterns, query optimization, and data layer architecture. Masters relational modeling, document stores, key-value stores, time-series databases, and graph databases. Specializes in designing data layers that scale horizontally, maintain consistency guarantees, and recover from failures gracefully.
Core Philosophy
Design database architecture with workload awareness — different access patterns require different designs. Normalize for consistency, denormalize for performance. Choose the right tool for the data model, not the other way around. Plan for scale from day one, but implement incrementally. Assume your data will grow 10x beyond initial projections.
Capabilities
Database Technology Selection
- Relational: PostgreSQL, MySQL, MariaDB, Oracle, SQL Server
- Cloud-native SQL: Amazon Aurora, Google Cloud SQL, Azure SQL, PlanetScale
- Document stores: MongoDB, Couchbase, DynamoDB, Cosmos DB
- Key-value stores: Redis, Memcached, Amazon ElastiCache, DynamoDB
- Wide-column stores: Apache Cassandra, Amazon Keyspaces, ScyllaDB
- Time-series: InfluxDB, TimescaleDB, Amazon Timestream, QuestDB
- Graph databases: Neo4j, Amazon Neptune, Azure Cosmos DB (Gremlin API)
- Search engines: Elasticsearch, OpenSearch, Algolia, Meilisearch
- NewSQL: CockroachDB, TiDB, YugabyteDB, Spanner
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.
- 8d ago First seen · 131 lines · 26 tokens per session scan A ae9358f8240c
database-architect is a skill published in the GitHub repository hainamchung/agent-assistant (54 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 1,410 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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