scylla-cluster-tests

Use when asked to generate an implementation plan, draft a plan, save a plan, or design a feature rollout for the SCT repository. Supports two formats: full 7-section plans for multi-phase work (1K+ LOC, tracked in MASTER.md) and lightweight mini-plans for single-PR changes (under 1K LOC, stored in docs/plans/mini-plans/). Routes automatically based on PR plans label, user input, or task size estimate.

Guides writing remote package installation commands (apt-get, yum, dnf, zypper) in SCT code. Use when adding apt-get install/update, yum install, dnf install, or zypper install calls via remoter.run/remoter.sudo. Ensures timeouts, retries, and non-interactive flags are always present to prevent hangs in CI.

Generate Gmail-compatible HTML performance weekly status reports from Argus CLI data. Use when asked to produce a weekly perf summary, create a performance status email, aggregate latency and throughput results across enterprise perf tests, or generate an HTML report for stakeholders. Covers predefined-throughput-steps, latency-650gb-with-nemesis, rolling-upgrade, and microbenchmark tests using argus run list and argus run results commands.

Migrate SCT test configs and pipeline jobs from literal instance_type params to constraint-based sizing_db/sizing_loader/sizing_monitor. Use when converting instance_type_db, gce_instance_type_db, azure_instance_type_db, instance_type_loader, instance_type_monitor to sizing constraints. Covers identifying instance specs, choosing vcpu/memory/arch/disk constraints, running sizing preview, and handling multi-DC or special params like nemesis_grow_shrink_instance_type.

Guides AI-assisted code review of SCT pull requests. Use when reviewing a PR, checking a diff for correctness, evaluating method signature changes across class hierarchies, verifying override compatibility, checking import conventions, error handling patterns, backend impact, test coverage, or provision label requirements. Covers inheritance safety, polymorphic method audits, and SCT-specific review criteria.

Generate weekly commit summary reports for SCT repository. Use when asked to create a commit summary, weekly report, changelog, or "last week in SCT" issue. Applies to summarizing git commits from scylla-cluster-tests master branch for developer audiences. Covers running sct_commits_summary.py, filtering commits by importance, and writing prose summaries with embedded GitHub links.

Guides writing and debugging unit tests for the SCT framework using pytest conventions. Use when creating new test files in unit_tests/, adding test cases, mocking external services, setting up fixtures, or reviewing test coverage. Covers network-blocking patterns, FakeRemoter, moto for AWS mocking, monkeypatch, and common pitfalls.

Guides writing and debugging integration tests for the SCT framework that interact with real external services. Use when creating tests requiring Docker, AWS, GCE, Azure, OCI, or Kubernetes backends. Covers service labeling, credential skip patterns, Docker Scylla fixtures, resource cleanup, and common pitfalls.

Fix inline merge conflict markers in backport PRs by resolving conflicts and recommitting cleanly with original metadata preserved. Use when a backport PR has unresolved conflict markers, a cherry-pick produced merge conflicts, or a PR has the 'conflicts' label and needs to be made ready for review. Supports bulk mode for multiple PRs.

Guides writing new nemesis (chaos engineering disruptions) for the SCT framework. Use when creating a new NemesisBaseClass subclass, adding disruption logic, setting nemesis flags, or configuring target node pools. Covers the sdcm/nemesis/ package structure, auto-discovery, flag filtering, CI configuration, and unit testing patterns.

Guides the design and structuring of AI agent skills for the SCT repository with multi-step phases, progressive disclosure, and dual-platform compatibility (GitHub Copilot and Claude Code). Use when creating new skills, reviewing existing skills, restructuring AI guidance into modular skill directories, editing SKILL.md files, or improving agent instructions.

Profile Python code in SCT to find CPU, memory, and concurrency bottlenecks using cProfile, scalene, memray, and py-spy. Use when a test or framework operation is unexpectedly slow, memory usage grows unbounded, you need to find which functions dominate CPU time, or you want to verify that an optimization actually improved performance. Covers profiling unit tests and full SCT test runs.