Review existing tests and CI, the automated checks run during development, for redundant coverage, misplaced tests, and opportunities to reduce runtime or cost. Receive file-specific recommendations based on documented improvement patterns, with uncertain findings separated.
Audit the existing test tree and CI configuration for improvements, using a catalog of patterns previously applied in this repo. Use when asked to audit tests or CI, shrink CI time or cost, find redundant or misplaced tests, clean up a test group, or review whether a CI change follows established practice.
Review the instructions the agent will receive before you install.
Audits what is already in test/, .github/ and scripts/ci/. For writing a new test see
write-sglang-test; for how the pipeline dispatches and
gates work see ci-workflow-guide.
Read action-items.md first. It is a catalog of patterns that have been applied to this repo, each with a way to spot it and example PRs. The catalog is the substance of this skill; everything below is only scaffolding.
The user says what to audit -- a test group, a stage, a workflow file, or a complaint like "this suite is slow" -- and how thoroughly. Read that scope and decide which patterns apply.
An audit is only useful if it is specific. "This suite could be trimmed" is not a
finding; "TestFooLargePage is a strict subset of TestFooRetractLargePage, drop it"
is. Every finding needs the file, the pattern it matches, and what to do.
Group findings by confidence in the claim, not by pattern id. Lead with the ones where the evidence is in the file you just read; keep the speculative ones separate and say what you would need to check. Skip anything you are not reasonably sure about -- a long list of maybes costs more to triage than it saves.
Propose, do not apply. Deleting a test, moving a registration to another stage, and changing a threshold are all decisions for the user. Land them as separate changes, since a trim and a threshold change fail for different reasons.
Example PRs age. When a pattern shows up in newer work, or a new pattern recurs, update
action-items.md: the pattern text should stay general, the examples are
replaceable evidence.
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Develop lightweight CUDA operations for SGLang using JIT, which compiles code on first use. A tensor-scaling example explains implementation conventions, input validation, memory access helpers, and when to choose this approach.
Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.
Monitor selected GitHub Actions checks on an SGLang pull request until they pass on its latest commit. Diagnose failures, push small validated fixes, and retry only failed jobs within defined limits.
Understand and modify SGLang's CI pipelines, including test stage order, start conditions, early stopping after failures, and workload splitting. Use the guide to add jobs and investigate failed, skipped, or cancelled checks.
Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.
Calculate how to divide serving memory between recurrent state and KV cache, two memory pools used by hybrid models. Use startup logs and workload settings to recommend a ratio, estimate concurrency, and identify the limiting pool.