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/engineerwithai/engineerwith-agents/sql-optimization-patternsnpx skills add EngineerWithAI/engineerwith-agents --skill sql-optimization-patternsgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/engineerwithai/engineerwith-agents/sql-optimization-patterns)<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/sql-optimization-patterns"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/sql-optimization-patterns.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 | $0.00044 | $0.03400 |
| Opus 5 | $0.00022 | $0.01700 |
| Sonnet 5 | $0.00009 | $0.00680 |
| Haiku 4.5 | $0.00004 | $0.00340 |
Grade A, and why
sql-optimization-patterns 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.
This is a copy
100% identical to sql-optimization-patterns — 0 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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Optimization Patterns
Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.
When to Use This Skill
- Debugging slow-running queries
- Designing performant database schemas
- Optimizing application response times
- Reducing database load and costs
- Improving scalability for growing datasets
- Analyzing EXPLAIN query plans
- Implementing efficient indexes
- Resolving N+1 query problems
Core Concepts
1. Query Execution Plans (EXPLAIN)
Understanding EXPLAIN output is fundamental to optimization.
PostgreSQL EXPLAIN:
-- Basic explain
EXPLAIN SELECT * FROM users WHERE email = '[email protected]';
-- With actual execution stats
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = '[email protected]';
-- Verbose output with more details
EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT u.*, o.order_total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '30 days';
Key Metrics to Watch:
- Seq Scan: Full table scan (usually slow for large tables)
- Index Scan: Using index (good)
- Index Only Scan: Using index without touching table (best)
- Nested Loop: Join method (okay for small datasets)
- Hash Join: Join method (good for larger datasets)
- Merge Join: Join method (good for sorted data)
- Cost: Estimated query cost (lower is better)
- Rows: Estimated rows returned
- Actual Time: Real execution time
2. Index Strategies
Indexes are the most powerful optimization tool.
Index Types:
- B-Tree: Default, good for equality and range queries
- Hash: Only for equality (=) comparisons
- GIN: Full-text search, array queries, JSONB
- GiST: Geometric data, full-text search
- BRIN: Block Range INdex for very large tables with correlation
-- Standard B-Tree index
CREATE INDEX idx_users_email ON users(email);
-- Composite index (order matters!)
CREATE INDEX idx_orders_user_status ON orders(user_id, status);
-- Partial index (index subset of rows)
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';
-- Expression index
CREATE INDEX idx_users_lower_email ON users(LOWER(email));
-- Covering index (include additional columns)
CREATE INDEX idx_users_email_covering ON users(email)
INCLUDE (name, created_at);
-- Full-text search index
CREATE INDEX idx_posts_search ON posts
USING GIN(to_tsvector('english', title || ' ' || body));
-- JSONB index
CREATE INDEX idx_metadata ON events USING GIN(metadata);
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 · 494 lines · 44 tokens per session scan A bfd74fb05aa7
sql-optimization-patterns is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 44 tokens to every session and 3,400 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to sql-optimization-patterns, differing in 0 lines, and is treated as a copy.
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