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 h4vzz/awesome-ai-agent-skills --skill query-optimizationgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/query-optimization)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/query-optimization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/query-optimization/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/h4vzz/awesome-ai-agent-skills/query-optimization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/query-optimization.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 | $0.00029 | $0.02470 |
| Opus 5 | $0.00015 | $0.01235 |
| Sonnet 5 | $0.00006 | $0.00494 |
| Haiku 4.5 | $0.00003 | $0.00247 |
Grade A, and why
query-optimization 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 10d 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.
This is a copy
94% identical to query-optimization — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Query Optimization
This skill enables an AI agent to diagnose and fix slow database queries. The agent uses EXPLAIN/EXPLAIN ANALYZE to interpret query execution plans, identifies missing indexes and inefficient scan patterns, rewrites queries to eliminate performance bottlenecks, detects and resolves N+1 query problems in ORMs, and recommends monitoring tools to track query performance over time. The focus is on practical, measurable improvements with before-and-after evidence.
Workflow
-
Identify the slow query: Collect the problematic query from slow query logs, application performance monitoring (APM) tools, or user reports. Note the current execution time, the table sizes involved, and how frequently the query runs. High-frequency slow queries should be prioritized over rare ones.
-
Analyze the execution plan: Run
EXPLAIN ANALYZE(PostgreSQL) orEXPLAIN FORMAT=JSON(MySQL) on the query to obtain the actual execution plan. Look for sequential scans on large tables, nested loop joins with high row estimates, sort operations on unindexed columns, and large gaps between estimated and actual row counts. -
Identify optimization opportunities: Based on the plan, identify concrete fixes: add indexes for columns in WHERE, JOIN, and ORDER BY clauses; rewrite subqueries as JOINs; replace
SELECT *with specific columns; add LIMIT clauses where appropriate; use covering indexes to avoid table lookups; eliminate redundant or duplicate conditions. -
Apply optimizations: Create the necessary indexes, rewrite the query, or adjust ORM usage. For N+1 problems, switch from lazy loading to eager loading (e.g.,
select_related/prefetch_relatedin Django,includein Prisma,joinedloadin SQLAlchemy). Apply one change at a time to measure each improvement independently. -
Measure and validate: Re-run
EXPLAIN ANALYZEon the optimized query and compare execution time, rows scanned, and plan structure against the original. Verify that the query returns identical results. Check that new indexes do not degrade write performance beyond acceptable thresholds.
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.
- 10d ago First seen · 172 lines · 29 tokens per session scan A 3cbb4a62ac0c
query-optimization is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 2,470 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to query-optimization, differing in 2 lines, and is treated as a copy.
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