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gclmgclm/super-stack/skills/planning/query-optimization/SKILL.md

query-optimization

Skill0 starsChanged 7 months agoMITView on GitHub

Analyze slow or expensive queries and recommend indexing, shape, and access-path improvements before performance issues harden into the design.

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 6 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when the problem is query latency, scan cost, N+1 access, pagination inefficiency, or uncertainty about the right index or …

    line 8
  2. Distinctive:

    avoid local optimizations that hurt write cost or correctness

    line 22
  3. Distinctive:

    Check for obvious waste:

    line 33

“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Query Optimization

Use this skill when the problem is query latency, scan cost, N+1 access, pagination inefficiency, or uncertainty about the right index or access path.

Read First

  • query code and ORM usage
  • explain plans, logs, or observed latency evidence
  • table sizes and expected growth
  • harness/state.md if it exists
  • harness/history.md if it exists

Goals

  • optimize based on evidence instead of folklore
  • match indexes to real filters, joins, and sort paths
  • separate query-shape fixes from schema fixes
  • avoid local optimizations that hurt write cost or correctness

Steps

  1. Restate the slow path and its evidence.
  2. Identify the exact query shape:
    • filters
    • joins
    • ordering
    • pagination
    • selected columns
  3. Check for obvious waste:
    • N+1 access
    • wide selects
    • missing limits
    • unbounded scans
  4. Compare improvement options:
    • query rewrite
    • index change
    • denormalization or cache
  5. Estimate the write, storage, and maintenance cost of the optimization.
  6. Recommend the smallest evidence-backed improvement first.

Output

Report:

  • slow path summary
  • query shape
  • evidence used
  • recommended optimization
  • cost or trade-off
  • verification plan

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