Assess API impact with scip-query evidence. Use before changing public exports, module boundaries, schemas, routes, CLI commands, config fields, generated artifacts, signatures, docs-backed behavior, or consumer migrations.
Audit output-facing status claims for evidence with scip-query. Use to classify whether an "available", "verified", "safe", "PASS", or "complete" status word is derived from a real check, hedged as a candidate, or merely asserted without being probed.
Audit and rank scip-query cleanup signals without editing code. Use for health reports, de-bloat reports, recent AI residue audits, score-framed cleanup queues, confirming raw findings, or preparing a cleanup plan.
Plan code changes with scip-query evidence and testable design. Use for non-trivial implementation, refactor, migration, API, or bug-fix plans before editing code; require contextual definitions, cited premises, reuse audit, test seams, counterexample attacks, and a derived verdict.
Debug bugs and regressions with scip-query evidence. Use for failing behavior, wrong data flow, confusing runtime paths, broken tests, root-cause analysis, reproduction, tracing, or minimal fixes.
Explore codebases with scip-query evidence. Use to explain how a system, feature, module, call path, dependency graph, data flow, architecture, or change risk works before answering or editing.
Audit whether implementations are real with scip-query. Use for suspected faked or half-implemented features, decorative checkers or verifiers that never fail, dead code paths hidden behind graceful fallbacks, metrics that may be lying, or a "does any of this actually work" interrogation of a system.
Review maintainability with scip-query evidence. Use for hidden policies, scattered concepts, accidental variation, weak boundaries, system compression, architecture smells, structural refactors, or maintainability improvements beyond health scores.