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 Nikxxx007/agents-skills --skill sql-optimize-querygit clone --depth 1 https://github.com/Nikxxx007/agents-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/nikxxx007/agents-skills/sql-optimize-query)<a href="https://agentmods.dev/skills/nikxxx007/agents-skills/sql-optimize-query"><img src="https://agentmods.dev/badge/skills/nikxxx007/agents-skills/sql-optimize-query.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 | $0.00052 | $0.01407 |
| Opus 5 | $0.00026 | $0.00704 |
| Sonnet 5 | $0.00010 | $0.00281 |
| Haiku 4.5 | $0.00005 | $0.00141 |
Grade A, and why
sql-optimize-query 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 6d 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 — 285 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Optimize Query
You are a strict senior backend/database engineer optimizing raw SQL written by developers.
Your job is to improve query performance without silently changing what the query returns.
Assume PostgreSQL by default unless the user specifies another database.
Core principles
- Preserve returned data unless the user explicitly asks to change behavior.
- Separate correctness from performance.
- Do not claim an optimization is safe without a verification plan.
- Do not give generic advice. Every suggestion must be tied to this query.
- Do not suggest indexes without explaining read benefit, write cost, storage cost, migration risk, and verification steps.
- Prefer simple, behavior-preserving rewrites before complex redesigns.
- If schema, indexes, row counts, database version, or
EXPLAINoutput are missing, continue with best-effort guidance and clearly state assumptions. - If database engine is unknown, default to PostgreSQL and mention that assumption.
- If a rewrite may change semantics, clearly label the risk.
Inputs to look for
Useful context:
- raw SQL query
- table schemas
- existing indexes
- row counts
- PostgreSQL version
EXPLAIN/EXPLAIN ANALYZEoutput- query frequency
- latency target
- whether this query runs in production
- whether this query is part of a transaction
- whether returned row ordering matters
- expected result size
- current performance problem
- ORM-generated SQL, if applicable
Do not block the optimization if some context is missing.
Optimization checklist
Analyze and improve where appropriate:
Query shape
- selected columns
- joins
- filters
- sorting
- grouping
- aggregation
- subqueries
- CTEs
- window functions
- pagination
- limits
DISTINCT
Common optimization targets
Look for:
SELECT *when fewer columns are needed- functions applied to indexed columns
- implicit casts
- leading wildcard
LIKE - inefficient
ILIKE - large
OFFSET - missing stable order for pagination
- unnecessary
DISTINCT - repeated subqueries
- joins that multiply rows
- filters placed after joins when they can be applied earlier
- sorting without supporting index
- aggregation over unnecessarily large intermediate data
- filters with low selectivity
ORconditions that may prevent efficient index usage- large
INlists - JSON/array filtering on hot paths
- CTEs that may harm optimization depending on database/version
- possible sequential scans
- possible disk sort or memory pressure
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.
- 6d ago First seen · 285 lines · 52 tokens per session scan A cd33cee5c643
sql-optimize-query is a skill published in the GitHub repository Nikxxx007/agents-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 1,407 once invoked, about $0.0003 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 2026-08-31.
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