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/ancoleman/ai-design-components/optimizing-sqlnpx skills add ancoleman/ai-design-components --skill optimizing-sqlgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWhat 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 | $0.00047 | $0.02862 |
| Opus 5 | $0.00023 | $0.01431 |
| Sonnet 5 | $0.00009 | $0.00572 |
| Haiku 4.5 | $0.00005 | $0.00286 |
Grade A, and why
optimizing-sql 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.
How it starts
The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Optimization
Provide tactical guidance for optimizing SQL query performance across PostgreSQL, MySQL, and SQL Server through execution plan analysis, strategic indexing, and query rewriting.
When to Use This Skill
Trigger this skill when encountering:
- Slow query performance or database timeouts
- Analyzing EXPLAIN plans or execution plans
- Determining index requirements
- Rewriting inefficient queries
- Identifying query anti-patterns (N+1, SELECT *, correlated subqueries)
- Database-specific optimization needs (PostgreSQL, MySQL, SQL Server)
Core Optimization Workflow
Step 1: Analyze Query Performance
Run execution plan analysis to identify bottlenecks:
PostgreSQL:
EXPLAIN ANALYZE SELECT * FROM users WHERE email = '[email protected]';
MySQL:
EXPLAIN FORMAT=JSON SELECT * FROM products WHERE category_id = 5;
SQL Server: Use SQL Server Management Studio: Display Estimated Execution Plan (Ctrl+L)
Key Metrics to Monitor:
- Cost: Estimated resource consumption
- Rows: Number of rows processed (estimated vs actual)
- Scan Type: Sequential scan vs index scan
- Execution Time: Actual time spent on operation
For detailed execution plan interpretation, see references/explain-guide.md.
Step 2: Identify Optimization Opportunities
Common Red Flags:
| Indicator | Problem | Solution |
|---|---|---|
| Seq Scan / Table Scan | Full table scan on large table | Add index on filter columns |
| High row count | Processing excessive rows | Add WHERE filter or index |
| Nested Loop with large outer table | Inefficient join algorithm | Index join columns |
| Correlated subquery | Subquery executes per row | Rewrite as JOIN or EXISTS |
| Sort operation on large result set | Expensive sorting | Add index matching ORDER BY |
For scan type interpretation, see references/scan-types.md.
Step 3: Apply Indexing Strategies
Index Decision Framework:
Is column used in WHERE, JOIN, ORDER BY, or GROUP BY?
├─ YES → Is column selective (many unique values)?
│ ├─ YES → Is table frequently queried?
│ │ ├─ YES → ADD INDEX
│ │ └─ NO → Consider based on query frequency
│ └─ NO (low selectivity) → Skip index
└─ NO → Skip index
What ships with it
13 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.
- examples/explain-analysis-examples.sql 9.9 KB
- examples/query-rewriting-examples.sql 12 KB
- outputs.yaml 5.8 KB
- references/anti-patterns.md 17 KB
- references/composite-indexes.md 7.6 KB
- references/efficient-patterns.md 13 KB
- references/explain-guide.md 12 KB
- references/index-types.md 5.2 KB
- references/indexing-decisions.md 15 KB
- references/mysql.md 12 KB
- references/postgresql.md 12 KB
- references/scan-types.md 13 KB
- references/sqlserver.md 12 KB
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 · 393 lines · 47 tokens per session scan A 7667d0a60d2b
optimizing-sql is a skill published in the GitHub repository ancoleman/ai-design-components (516 stars, last pushed 8mo ago), licensed MIT. It adds 47 tokens to every session and 2,862 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 2026-08-30.
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