Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jnPiyush/AgentXnpx agentmods add skills/jnpiyush/agentx/databaseWrote 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/jnpiyush/agentx/database)<a href="https://agentmods.dev/skills/jnpiyush/agentx/database"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/database/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/jnpiyush/agentx/database"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.01359 |
| Opus 5.5 | $0.00018 | $0.00544 |
| Sonnet 5.5 | $0.00009 | $0.00272 |
| Haiku 4.5 | $0.00004 | $0.00136 |
Grade A, and why
database 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 1mo 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database
Purpose: Efficient, reliable database operations with migrations, indexes, and transactions. Focus: ORM patterns, query optimization, data integrity. Note: For database-specific details, see PostgreSQL or SQL Server.
When to Use This Skill
- Creating database schemas or migrations
- Optimizing slow database queries
- Configuring connection pooling
- Choosing between ORM frameworks
- Implementing data integrity constraints
Prerequisites
- SQL fundamentals
- Access to a relational database
Decision Tree
Database operation?
+- Schema change?
| +- New table/column? -> Migration (up + down)
| +- Rename/drop? -> Migration + verify no dependents
| - Index needed? -> CREATE INDEX CONCURRENTLY (avoid locks)
+- Query performance?
| +- Slow query? -> EXPLAIN ANALYZE -> add index or rewrite
| +- N+1 problem? -> Use eager loading / JOIN
| - Large result set? -> Pagination (cursor-based preferred)
+- Data integrity?
| +- Multiple writes? -> Use transaction
| - Concurrent access? -> Optimistic concurrency (version column)
- Connection management?
- Always use connection pooling, never open/close per query
Performance Best Practices
Database Optimization Checklist
- Add indexes on foreign keys
- Add indexes on columns in WHERE/JOIN/ORDER BY
- Use composite indexes for multi-column queries
- Analyze query execution plans
- Fix N+1 queries with eager loading
- Use connection pooling
- Cache frequently accessed data
- Denormalize for read-heavy workloads
- Partition large tables
- Archive old data
- Monitor slow query logs
Query Analysis
Analyze Query Performance:
-- PostgreSQL
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = '[email protected]';
-- MySQL
EXPLAIN
SELECT * FROM users WHERE email = '[email protected]';
Look for:
- Seq Scan - Table scan (bad for large tables)
- Index Scan - Uses index (good)
- Nested Loop - Join method (can be slow)
- Hash Join - Better for large datasets
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed fe7f37b9047e
- 1mo ago First seen · 161 lines · 44 tokens per session scan A bf4ec9204c28
database is a skill published in the GitHub repository jnPiyush/AgentX (16 stars, last pushed 3d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,359 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.
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