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 skills/codebygarv/ai-skills/sql-query-optimizernpx skills add codebygarv/Ai-skills --skill sql-query-optimizergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWhat 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 | $0.00024 | $0.00363 |
| Opus 5 | $0.00012 | $0.00181 |
| Sonnet 5 | $0.00005 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
sql-query-optimizer 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
Purpose
Analyze slow or resource-intensive SQL queries, interpret EXPLAIN / ANALYZE execution plans, and rewrite queries or recommend precise indexes to eliminate sequential scans and lock contention.
When to Use
- A query causes high CPU, memory pressure, or connection saturation on the database.
- Designing complex joins, aggregations, window functions, or subqueries.
- Diagnosing deadlocks or table lock contention under concurrent writes.
What to Analyze
- Execution Plan: Inspect cost nodes, Seq Scans, Index Scans, Bitmap Index Scans, Nested Loops vs Hash Joins.
- Predicate SARGability: Identify functions on indexed columns (e.g.
WHERE DATE(created_at) = ...) preventing index hits. - Join & Subquery Efficiency: Convert correlated subqueries to CTEs, Window Functions, or Hash Joins.
- Indexing Strategy: Single-column, composite (ordering column matches query pattern), partial, or covering indexes.
- Pagination Mechanics: Replace large offset pagination (
OFFSET 100000) with keyset/cursor pagination.
Output Format
- Diagnosis: Why the query is slow (missing index, scan type, cardinality misestimate).
- Optimized SQL: Clean rewritten query with explanation.
- DDL Changes: Exact
CREATE INDEX CONCURRENTLYstatements needed. - Before vs After Metrics: Expected scan cost reduction and latency impact.
Avoid
- Adding indexes on every column without considering write throughput degradation.
- Omitting
CONCURRENTLYon production index creation statements.
What ships with it
2 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.
- 2d ago First seen · 35 lines · 24 tokens per session scan A 9c3fa0d4d0ec
sql-query-optimizer is a skill published in the GitHub repository codebygarv/Ai-skills (24 stars, last pushed 12d ago), licensed MIT. It adds 24 tokens to every session and 363 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 2026-08-30.
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