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SKILL verified Apache-2.0 Self-run

Db Query Optimization

skill-kentoshimizu-sw-agent-skills-db-query-optimization · by KentoShimizu

Query optimization workflow for reducing latency and resource cost through plan-aware rewrites and access-path improvements. Use when hot-path query behavior is the bottleneck; do not use for conceptual schema redesign without workload evidence.

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Install

$ agentstack add skill-kentoshimizu-sw-agent-skills-db-query-optimization

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

DB Query Optimization

Overview

Use this skill to improve query performance based on execution evidence, not intuition.

Scope Boundaries

  • Hot-path latency or database CPU/IO usage is query-bound.
  • Query plans are unstable across parameter distributions.
  • Workload changes expose previously acceptable query anti-patterns.

Core Judgments

  • Dominant bottleneck: scan cost, join explosion, sort spill, lock wait, network round trips.
  • Rewrite scope: query shape, index changes, schema adjustment, or materialization.
  • Plan stability and parameter-sensitivity risk.
  • Correctness risk from aggressive rewrite or approximation.

Practitioner Heuristics

  • Start from actual execution plans and runtime metrics.
  • Optimize the highest-impact query families, not one-off outliers.
  • Sargability and predicate selectivity usually dominate early wins.
  • Keep optimization readable; opaque SQL hacks create long-term maintenance debt.

Workflow

  1. Identify high-impact queries by frequency and user/business impact.
  2. Capture plan/runtime evidence under representative parameters.
  3. Propose rewrites and access-path changes with expected effects.
  4. Compare candidates for latency gain versus complexity and risk.
  5. Roll selected change and monitor plan stability and resource usage.
  6. Record conditions that should trigger re-optimization.

Common Failure Modes

  • Tuning for small dev datasets misleads production behavior.
  • Index-only fixes mask poor query shape.
  • Query changes improve p50 while degrading tail latency.

Failure Conditions

  • Stop when no representative workload evidence is available.
  • Stop when optimization changes correctness semantics.
  • Escalate when required performance target is unattainable without model changes.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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