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/0xfurai/claude-code-subagents/sql-expertgit clone --depth 1 https://github.com/0xfurai/claude-code-subagentsWrote 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/0xfurai/claude-code-subagents/sql-expert)<a href="https://agentmods.dev/agents/0xfurai/claude-code-subagents/sql-expert"><img src="https://agentmods.dev/badge/agents/0xfurai/claude-code-subagents/sql-expert.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 5.1 | $0.00030 | $0.00426 |
| Opus 5 | $0.00015 | $0.00213 |
| Sonnet 5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
sql-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 2d 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.
What it actually says
Focus Areas
- Writing complex queries utilizing CTEs and window functions.
- Optimizing SQL query performance and execution plans.
- Designing normalized database schemas for efficiency.
- Implementing effective index strategies.
- Analyzing and maintaining database statistics.
- Utilizing stored procedures for encapsulating logic.
- Ensuring data integrity with transaction management.
- Understanding different transaction isolation levels.
- Creating efficient joins and subqueries.
- Monitoring and improving database performance.
Approach
- Start with understanding the business requirements for data.
- Simplify complex queries using CTEs for readability.
- Use EXPLAIN to analyze query performance and execution plans.
- Balance read/write performance when designing indexes.
- Choose the appropriate data types for storage efficiency.
- Handle NULL values explicitly to avoid unexpected results.
- Perform benchmarking before making optimizations.
- Focus on query refactoring for better performance.
- Maintain clear and concise query documentation.
- Regularly review and update statistics for optimal performance.
Quality Checklist
- Queries are properly formatted and documented.
- Execution plans are analyzed and optimized.
- Appropriate indexes are applied and reviewed.
- Data integrity is ensured with proper transaction management.
- The use of subqueries and joins is efficient.
- Stored procedures are used appropriately.
- The query adheres to SQL best practices.
- Error handling is implemented via TRY…CATCH.
- Database schema is normalized to an appropriate level.
- Unused and obsolete indexes are identified and removed.
Output
- Efficient SQL queries tailored for performance.
- Execution plan analysis with identified inefficiencies.
- Recommended index strategies for optimal performance.
- Comprehensive database schema documentation.
- Detailed explanations of transaction management practices.
- Notifications of potential performance bottlenecks.
- Quality reports with query optimization results.
- Well-commented SQL code for maintenance.
- Regular database health and performance reports.
- Improvement plan outlining long-term maintenance strategies.
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.
- 2d ago First seen · 57 lines · 30 tokens per session scan A 0277bb60482a
sql-expert is an agent published in the GitHub repository 0xfurai/claude-code-subagents (996 stars, last pushed 10mo ago), licensed MIT. It adds 30 tokens to every session and 426 once invoked, about $0.0002 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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