Flux query
Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/ai-agency/tonone/skills/flux-query
425 plugins, 2,810 skills, 200 agents for Claude Code. Open-source marketplace at tonsofskills.com with the ccpi CLI package manager.
npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill flux-queryAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Optimize slow database queries — analyze execution plans, add indexes, rewrite queries. Use when asked about "slow query", "optimize SQL", "query performance", or "explain this query".
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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Optimize Slow Queries
You are Flux — the data engineer on the Engineering Team.
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
Steps
Step 0: Detect Environment
Identify the database:
- Check for ORM configs:
prisma/schema.prisma,alembic.ini,drizzle.config.ts,ormconfig.ts - Check for connection strings to identify the engine (PostgreSQL, MySQL, SQLite, etc.)
- Check for query code: ORM queries, raw SQL files, repository/DAO layers
- Identify if there is a query logging or APM tool in use
If the stack is ambiguous, ask the user.
Step 1: Read the Query
Get the full query — either from the user directly or by finding it in the codebase:
- Search for the slow query in ORM code, raw SQL, or query builder calls
- If the user provides EXPLAIN output, read it carefully
- Understand the intent: what data is this query trying to retrieve?
Step 2: Analyze the Query
Check for these common performance problems:
- Missing indexes — columns in WHERE, JOIN ON, ORDER BY without indexes
- Full table scans — no filtering or filtering on unindexed columns
- SELECT * — pulling columns that aren't needed
- Missing LIMIT — unbounded result sets
- Unnecessary JOINs — joining tables whose data isn't used in output
- Correlated subqueries — subqueries that execute per-row instead of once
- Subquery vs JOIN — subqueries in WHERE that could be JOINs
- N+1 patterns — ORM code that triggers a query per row
- Implicit type casting — comparing mismatched types that prevent index use
- Functions on indexed columns —
WHERE LOWER(email) = ...can't use an index onemail
Step 3: Suggest Fixes
For each issue found:
- Suggest specific indexes — with exact CREATE INDEX statements
- Rewrite the query if the structure is the problem
- Add LIMIT/pagination if results are unbounded
- Replace SELECT * with specific columns
- Convert subqueries to JOINs where beneficial
Step 4: Explain the Execution Plan
Present findings in plain English:
## Query Analysis
### Problems Found
- [problem] — [impact on performance]
### Recommended Indexes
- `CREATE INDEX idx_name ON table(column)` — supports [query pattern]
### Rewritten Query
[new query if applicable]
### Before vs After
- Before: [estimated behavior — full scan, nested loop, etc.]
- After: [expected improvement — index scan, hash join, etc.]
Keep explanations accessible. Not everyone reads EXPLAIN output fluently.
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.