Data SQL Optimization

What it does

When your agent uses it

• The user needs to speed up slow queries
• Fix N+1 problems
• Optimize database performance — even if they say this query is slow
• Optimize our database

Example prompts

• “this query is slow”
• “optimize our database”
• “which indexes do we need”
• “/data-sql-optimization”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Identify slow queries: Database slow query log (pg_stat_statements, MySQL slow log)
2. 2Run EXPLAIN ANALYZE on the slowest
3. 3Find the bottleneck: Seq Scan on large table? Missing index? Expensive sort?
4. 4Apply fix: Add index, rewrite query, or restructure schema
5. 5Verify: Run EXPLAIN ANALYZE again — confirm improvement
6. 6Monitor: Check that fix didn't degrade other queries

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data SQL Optimization loads about 1.3k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 421 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~99

When it runs · the whole SKILL.md, loaded when a task matches

~1.3k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~7.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 421 words, ~1,341 tokens.

name

data-sql-optimization

description

Optimize SQL query performance using EXPLAIN analysis, indexing strategies, and common anti-pattern fixes. Use this skill when the user needs to speed up slow queries, design indexes, fix N+1 problems, or optimize database performance — even if they say 'this query is slow', 'optimize our database', 'which indexes do we need', or 'our dashboard takes 30 seconds to load'.

metadata.category

WP-04 數據分析

metadata.tags

data-analysis, sql, database, performance

IRON LAW: Measure Before Optimizing

NEVER guess which query is slow or why. Use EXPLAIN (EXPLAIN ANALYZE in
PostgreSQL) to see the actual execution plan. The database's plan often
differs from what you expect — a query you think is efficient may do
a full table scan, and a complex-looking query may use an index perfectly.

Measure → identify bottleneck → fix → measure again.

# Query Optimization: {Context}

## Slow Query
```sql
{the original slow query}

## Gotchas

- **Indexes have write cost**: Every INSERT/UPDATE must update all indexes. Over-indexing slows writes. Index what you query, not everything.
- **Statistics can be stale**: If EXPLAIN estimates are way off from actuals, run `ANALYZE` (PostgreSQL) or `ANALYZE TABLE` (MySQL) to update statistics.
- **Query cache hides problems**: A query may appear fast because it's cached. Test with cache cleared or cold start.
- **ORM-generated queries**: ORMs (Django, SQLAlchemy, ActiveRecord) generate SQL that may not be optimal. Always inspect the actual SQL for performance-critical paths.
- **Connection pooling**: Sometimes the bottleneck isn't the query but connection overhead. Use connection pooling (PgBouncer, ProxySQL) for high-concurrency applications.

## References

- For PostgreSQL-specific optimization, see `references/pg-optimization.md`
- For CTE vs temp table performance comparison, see `references/cte-vs-temp.md`

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in data-sql-optimization of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/cte-vs-temp.md
• references/pg-optimization.md

Open the folder on GitHubat commit 4e7f4f8