PostgreSQL Slow Query Evidence Pack
Investigate a PostgreSQL query using plans, runtime statistics, locks, indexes, data shape, cache conditions, and controlled experiments before recommending a safe optimization.
Category
Prompts for debugging, refactoring, building features, reviewing security, improving performance, and guiding Codex through safe code changes.
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Investigate a PostgreSQL query using plans, runtime statistics, locks, indexes, data shape, cache conditions, and controlled experiments before recommending a safe optimization.
Assess a Terraform plan’s real blast radius across resources, state, dependencies, services, and data before an authorized apply decision.
Diagnose failed Kubernetes rollouts, isolate the failing layer, choose a controlled recovery path, and verify service restoration using supplied evidence.
Reproduce intermittent test failures, isolate nondeterministic causes, implement a cause-specific fix, and measure reliability without hiding product defects.
Diagnose monorepo dependency boundaries, affected-task selection, cache correctness, and CI cost using repository evidence, then design measurable, regression-safe optimization experiments.
Plan an evidence-based API deprecation with behavioural compatibility checks, verified client migration, lifecycle communications, time-bound exceptions, rollback readiness, and a controlled sunset decision.
Reproduce a web accessibility regression, trace it to the responsible code, apply a focused WCAG-informed repair, and add automated and human verification.
Find where untrusted runtime data bypasses TypeScript assumptions, add focused validation and error handling, and verify compatible behavior across affected consumers.
Audit GitHub Actions workflows, secret exposure paths, token permissions, runner trust boundaries, and repository protections, then produce a prioritized and verifiable remediation plan.
Review an AI-generated pull request for request alignment, behavioural correctness, security risks, scope drift, test quality, maintainability, rollback readiness, and human merge approval.
Diagnose Docker build failures, identify layer and dependency issues, reduce unnecessary image size, preserve runtime compatibility, and define safe verification steps.
Review GitHub pull requests for behavior changes, risky files, missing tests, security concerns, migrations, rollback needs, and merge readiness.