Sysbench OLTP and TPC-H OLAP benchmark execution patterns, multi-instance PostgreSQL management, snapshot management, the full WorkloadOrchestrator pipeline, and performance measurement workflows. Use this skill when working on benchmark executors, evaluation pipeline, instance management, snapshot restoration, configuration application, restart policy, system metrics collection, or any code in src/benchmarks/, src/tuners/engine/, src/utils/applicator.py, or src/utils/environments/.
Complete codebase map for the PBT PostgreSQL tuning research project. Covers all source packages, file inventory with responsibilities, dependency relationships, data flow through the tuning pipeline, and navigation guide. Use this skill whenever you need to understand where code lives, how packages relate to each other, which file to modify for a given task, or when onboarding to the project. This is the first skill to consult when starting any new task in this repository.
Population-Based Training algorithm implementation patterns, evolutionary optimization conventions, and PBT-specific coding standards for the database tuning research project. Use this skill whenever working on PBT core logic, evolution, worker management, population management, scoring, normalization, convergence detection, exploit-explore mechanics, or any code in src/tuners/pbt/.
PostgreSQL configuration parameter (knob) tuning patterns, including parameter contexts (postmaster/sighup/user), knob space management, hardware-aware fractional normalization, safe bounds enforcement, and the knob tier system. Use this skill whenever working on knob configuration, parameter application, knob metadata, hardware-aware normalization, transfer learning via warm-start, or any code in src/knobs/, or src/utils/applicator.py.
Patterns for designing, executing, and analyzing reproducible scientific experiments in the database tuning domain. Covers multi-seed runs, baseline comparisons, BO comparisons, statistical reporting, and results directory structure. Use this skill when creating experiment scripts, setting up baselines, running multi-seed campaigns, comparing against Bayesian Optimization, computing improvement percentages, designing experiment protocols, or working on any experiment orchestration code.
Feature-driven scoring pipeline (scoring-v2) including the CompositeScorer, QuantileUtilityNormalizer, FeatureDrivenWeightModel, scoring policies (fixed_v1, feature_driven_v2), workload feature extraction, reliability gating, drift detection, and saturation expansion. Use this skill whenever working on score computation, metric normalization, metric weighting, scoring policies, workload features, calibration, rescoring, normalization drift, saturation detection, or any code in src/utils/scoring/, src/utils/metrics.py, or src/utils/calibration.py. Also use when debugging score values, investigating why a worker scored unexpectedly, or modifying the scoring contract.
Development workflow, CI gates, testing patterns, and contribution conventions for the PBT PostgreSQL tuning project. Covers make targets, pytest structure, ruff linting, mypy type checking, git branching, and commit conventions. Use this skill when running tests, fixing lint errors, adding new test files, setting up development environment, or preparing changes for commit.
Post-hoc comparative evaluation pipeline that compares PBT-tuned PostgreSQL configurations against defaults using Docker-isolated benchmarks and rigorous statistical analysis (Wilcoxon signed-rank, bootstrap CI, Holm correction, Cohen's d). Use this skill when working on the evaluation module, comparison reports, statistical testing, session loading, Docker evaluation containers, evaluation CLI, or any code in src/evaluation/. Also use when running `python -m src.evaluation`, interpreting comparison results, or debugging evaluation failures.