Use when reviewing Go, Python, or TypeScript code for over-engineering, reducing complexity after prototyping, enforcing reuse over addition, simplifying before a refactor, or auditing a codebase after a library or framework migration. Covers duplication, reinvented primitives, avoidable abstractions, dead logic…
Use when reviewing Go, Python, or TypeScript code for classic code smells, preparing a refactor, auditing code after rapid feature development, or hunting for misplaced responsibilities and undermodeled domain concepts. Covers feature envy, data clumps, shotgun surgery, temporal coupling, comments as deodorant…
Act as a senior Go engineer performing a critical design and testing review. Evaluates architecture, patterns, idiomaticity, over-engineering, testing strategy, and proposes concrete improvements — all grounded in the codebase. Use when: reviewing a Go codebase for design soundness, testing robustness, or overall code…
Grilling session that challenges your requirements against the existing codebase. Reports omit empty sections — no placeholder headings, empty tables, or negative statements like "no issues found".
Clarify the user’s intent for vague, incomplete, or ambiguous clauses, statements, and requirements before modifying the code. Reports omit empty sections — no placeholder headings, empty tables, or negative statements like "no issues found".
Audit Go packages for boundary violations — leaked internals via exports, coupling through shared types, import cycles, missing internal/ packages, over-exported APIs, and dependency direction violations. Use when: reviewing package structure, shrinking public API surface, enforcing encapsulation, preparing packages…
Audit Go code for missing documentation where the "why" is not obvious — obscure calculations, non-trivial business rules, surprising behavior, implicit constraints, workarounds, and missing godoc on exported symbols. Finds where a comment would save the next reader significant time. Use when: reviewing Go code for…
Run all 10 Go code quality hunters in parallel as subagents and write each report to a timestamped reports folder. Covers boundary, doc, invariant, security, simplicity, slop, smell, solid, test, and type hunters. Use when: running a full Go codebase audit, scanning all quality dimensions at once, preparing for a code…
Audit Go code for AI-generated noise — redundant comments, verbose documentation, style drift from project conventions and gofmt, trivially dead code, and unnecessary error wrapping. Surface-level hygiene pass; defaults to branch diff but supports any scope. Use when: reviewing AI-assisted Go code before merge…
Audit Go code for classic code smells — feature envy, data clumps, shotgun surgery, primitive obsession, temporal coupling, comments as deodorant, temporary fields, init() abuse, package-level mutable state, and stuttering names. Use when: reviewing Go code for structural design problems, preparing for a refactor…
Audit Go code for design principle violations — god packages, rigid extension points, broken interface contracts, fat interfaces, and concrete dependency chains. Adapted from SOLID for Go's composition-over-inheritance model. Use when: reviewing package structure, preparing for extension with new variants, reducing…
Audit Go test code for quality gaps — missing coverage on critical paths, brittle tests coupled to implementation, over-mocking, assertion-free tests, missing edge cases, table-driven test misuse, and race condition blindness. Focuses on test effectiveness. Use when: reviewing Go test suites for reliability, reducing…
Audit Go type definitions for design debt — duplicated struct shapes, misused generics, under-constrained type parameters, embedding antipatterns, poor enum patterns, and disorganized type architecture. Type structure and maintainability. Use when: reviewing type definitions for maintainability, reducing type…
Audit Python packages and modules for black-box boundary violations — leaked internals via exports, coupling through shared types, Law of Demeter chains, missing abstraction layers around externals, and over-exported APIs. Use when: reviewing package structure, shrinking public API surface, enforcing encapsulation…
Audit Python code for missing or misleading inline documentation where the "why" is not obvious — obscure calculations, non-trivial business rules, surprising behavior, implicit constraints, workarounds, and stale comments that contradict the current code. Finds where a comment would save the next reader significant…
Audit Python code for weak invariants — unnecessary casts, loose optionality, defensive None-checks masking missing guarantees, leaky tagged unions, error suppression, and runtime checks that the type system or construction boundaries should enforce. Use when: tightening post-construction guarantees, reducing type…
Run all 12 Python code quality hunters in parallel as subagents and write each report to a timestamped reports folder. Covers boundary, doc, error, invariant, perf, security, simplicity, slop, smell, solid, test, and type hunters. Use when: running a full Python codebase audit, scanning all quality dimensions at once…
Audit Python code for security vulnerabilities — hardcoded secrets, injection risks, missing input validation at trust boundaries, insecure defaults, auth gaps, sensitive data exposure, and unsafe patterns like eval, pickle, or shell injection. Use when: reviewing Python code before deployment, auditing trust…
★not rated 5 26d agoB103 tokens
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