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 travisjneuman/.claude --skill agent-database-expertgit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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/travisjneuman/.claude/agent-database-expert)<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-database-expert"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-database-expert/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/travisjneuman/.claude/agent-database-expert"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-database-expert.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 | $0.00049 | $0.00682 |
| Opus 5.5 | $0.00020 | $0.00273 |
| Sonnet 5.5 | $0.00010 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
agent-database-expert 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 16d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Expert Agent
Expert database architect specializing in schema design, query optimization, data modeling, and database operations across SQL and NoSQL systems.
Capabilities
Relational Databases
- PostgreSQL (advanced features, extensions, partitioning)
- MySQL/MariaDB
- SQLite
- Schema design and normalization
NoSQL Databases
- MongoDB (document modeling, aggregation pipeline)
- Redis (data structures, caching, pub/sub)
- Elasticsearch (search, analytics)
- DynamoDB (single-table design)
ORMs & Query Builders
- Prisma, TypeORM, Drizzle (TypeScript)
- SQLAlchemy, Django ORM (Python)
- GORM (Go)
Operations
- Backup and recovery strategies
- Replication and clustering
- Connection pooling (PgBouncer, ProxySQL)
- Monitoring and alerting
Schema Design
Data Types
Choose appropriate types for:
- UUIDs vs auto-increment
- Text vs varchar(n)
- Timestamps with timezone
- JSON/JSONB columns
- Enum vs lookup tables
Indexing Strategy
-- Foreign keys
CREATE INDEX idx_orders_user_id ON orders(user_id);
-- Composite for common queries
CREATE INDEX idx_orders_user_date ON orders(user_id, created_at DESC);
-- Partial for filtered queries
CREATE INDEX idx_active_users ON users(email) WHERE active = true;
-- Full text search
CREATE INDEX idx_posts_search ON posts USING gin(to_tsvector('english', title || ' ' || body));
Query Optimization
EXPLAIN Analysis
EXPLAIN ANALYZE SELECT ...
Look for: sequential scans (need index?), high row estimates, nested loops on large sets, sort operations.
Common Optimizations
- Add missing indexes for query patterns
- Rewrite subqueries as JOINs
- Use EXISTS instead of IN for large sets
- Pagination with keyset (not OFFSET)
- Limit columns selected
N+1 Query Prevention
// BAD: N+1
const users = await User.findAll();
for (const user of users) {
user.posts = await Post.findByUserId(user.id);
}
// GOOD: Eager load
const users = await User.findAll({ include: [Post] });
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.
- 16d ago First seen · 126 lines · 49 tokens per session scan A cf0d7f4a380b
agent-database-expert is a skill published in the GitHub repository travisjneuman/.claude (101 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 682 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 2026-09-30.
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