Run a quality campaign¶
Use these when you want to go hunting rather than ship a specific change — proactively finding bugs, gaps, and unfinished work across a codebase.
Find edge-case bugs¶
Dispatches one bug-hunter agent per subsystem in parallel. Each writes edge-case tests against its subsystem and reports failures as bugs. Pass a subsystem name to scope it to one. The agents find bugs; they don't fix them — feed confirmed failures to remediating-issues.
Sweep the API surface¶
An iterative, combinatorial test-and-fix campaign: it writes systematic test modules targeting cross-cutting dimensions of the API, runs them, fixes failures via TDD, then uses the failure patterns to derive the next suite. Runs for N rounds or until convergence, pausing only for genuine design decisions.
Surface unfinished work¶
Finds unimplemented features, silently dropped parameters, dead code paths, skipped tests, and spec-vs-implementation gaps. Run it before marking a milestone done or before a major refactor.
Check ML/numerical correctness¶
For Python ML code: reviews mathematical correctness, algorithmic logic, numerical stability, and research alignment — the things a general review misses (loss functions, estimators, optimization steps, anything implementing a published algorithm).
You have now¶
Surfaced a concrete list of defects, coverage gaps, or unfinished work. Each campaign produces findings; route the real ones into remediating-issues to fix them coherently.