By Terryc21
Run a six-skill audit on iOS/Swift codebases to detect data-model bugs, UI-path issues, time bombs, and visual quality problems, with automated bug classification and A-F grading to support ship/no-ship decisions.
Unified A-F grading and ship/no-ship decisions for the 6-skill radar family (5 companions + capstone). Aggregates handoffs from data-model, ui-path, roundtrip, time-bomb, and ui-enhancer; owns 5 grep-reliable domains; tracks velocity; celebrates improvements. Triggers: "capstone radar", "can I ship", "grade codebase", "/capstone-radar".
Audits SwiftData/Core Data model layer for field completeness, serialization gaps, relationship integrity, semantic ambiguity, dead fields, and migration safety. Finds model-layer bugs that manifest as workflow bugs. Triggers: "audit models", "model radar", "/data-model-radar".
Shared axis classification framework for all radar-suite skills. Every finding must be classified as axis_1 (bug), axis_2 (scatter), or axis_3 (dead/smelly) before emission, with mandatory coaching fields and file:line citations to existing patterns in the audited codebase. Triggers: invoked by every radar before emitting findings.
Unified entry point for the 6-skill radar family (5 companions + capstone). Routes to individual skills, runs targeted (Tier 2) or full (Tier 3) audit sequences, owns the unified ledger, and provides cross-skill operations (status, verify, ledger, link, deferred, fresh/no-fresh). Triggers: "radar suite", "full audit", "run all radars", "/radar-suite".
Per-journey code audit tracing data through complete user flows for bugs, data safety, performance, and round-trip completeness. Discovers workflows, audits each end-to-end, rolls up cross-cutting issues, and supports natural-language flow tracing. Triggers: "roundtrip audit", "trace user journey", "/roundtrip-radar".
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Audit skills for Claude Code that find a different class of iOS/macOS bugs from what linters and pattern checkers catch — by tracing how data flows through your app across files, not by checking individual files against a rule list. Every finding cites a real file:line in your code and is rated on a 9-column severity table. A capstone skill aggregates findings into a ship-or-don't-ship grade.
Use Radar Suite alongside your existing linter / SwiftLint / pattern-based audits — they find different things. A thorough pre-release audit runs both. See What Radar Suite is for vs what linters are for.
Built while shipping Stuffolio (Universal iOS/iPadOS/macOS app, currently build 33). Free, open source, Apache 2.0.
/plugin commands in Claude Code; then /radar-suite is available in any project./radar-suite ui-enhancer --scope <small directory> — ~5 min, one report to look at.Radar Suite and linters are complementary, not competitive. They look at code through different lenses and find different classes of bugs. A thorough pre-release audit runs both — neither alone is sufficient.
Pattern-based tools (SwiftLint, custom linters, single-file audit skills) check individual files against a rule catalog: force unwraps, missing @MainActor, try? swallowing errors, deprecated APIs, naming conventions, style. They're fast, precise, run on every save, and catch a real and important class of bugs cheaply. They will find issues Radar Suite won't — anything that lives inside one file's text and can be expressed as a grep pattern.
Radar Suite traces behavior across files. It starts from what the user sees (a screen, a flow, a backup round-trip) and follows the data through views, view models, managers, and persistence to verify the loop actually closes. It catches bugs that live in the handoff between files: data flow gaps, navigation dead ends, round-trip integrity loss. It will find issues linters won't — anything that requires reading multiple files together to spot.
Concrete example of the difference: a SwiftData @Model with a non-optional inverse relationship is correctly declared. SwiftLint and any pattern audit will say it's fine, because the declaration is fine. A backup→restore→edit→save cycle that loses one of those relationships in the round trip is a bug, but no single file is wrong — each file's view of the data is locally correct. Radar Suite catches the silent loss by reading the round-trip path; the linter has no reason to flag it because there's nothing in any individual file to flag.
Useful framing: pattern-based tools are the building inspector confirming each bolt is torqued to spec. Radar Suite is the home inspector who turns on the shower and checks where the water actually goes. The inspection isn't complete without both.
| What linters do better | What Radar Suite does better |
|---|---|
| Run on every save (cheap, fast) | Run before release (deeper, slower) |
| Catch style and pattern violations | Catch behavior and data-flow bugs |
| Single-file context | Cross-file traces |
| Hundreds of well-understood rules | Domain-specific behavior verification |
| Mature ecosystem | New approach, narrower scope |
If your project already uses SwiftLint or another pattern-based audit, keep it. Radar Suite layers on top.
Eight skills total: six domain auditors, a router that orchestrates them, and a foundation skill that runs implicitly before any finding is emitted.
npx claudepluginhub terryc21/radar-suite --plugin radar-suiteSwiftUI workflow audit — find dead ends, broken promises, and UX friction. Bundled with a phased plan generator.
Use when reviewing a Claude Code skill, auditing skill quality before publish, or asking "is this skill any good?". Produces structured reports with file:line citations, severity-rated findings cards, and an optional second-opinion pass. Lens variants: full (default), safety, discoverability, architecture, parseability, tests, quick.
Mine for hidden bugs that pattern-based auditors miss — logic errors, broken assumptions, state machine gaps, and semantic fragility
After you fix a bug, find and rate other instances of the same pattern in the codebase. Two modes: inferred from your recent fix (self-validates the constructed pattern against the pre-fix file before scanning) or user-described. Each match is read in context and classified individually as BUG / WATCH / OK / REVIEW with severity, fix effort, and blast-radius columns. Pattern matching after a real fix is dramatically more accurate than pattern matching from a theoretical catalog.
SwiftUI workflow audit — find dead ends, broken promises, and UX friction. Bundled with a phased plan generator.
AI-powered visual testing and accessibility setup for iOS/SwiftUI apps. Build, launch in Simulator, test with computer use, detect crashes, analyze memory leaks, and add accessibility identifiers.
Swift, SwiftUI, SwiftData, iOS animation design/implementation/review, and framework code review (HealthKit, CloudKit, WidgetKit, watchOS, App Intents). Pairs with beagle-core for full workflow.
16 specialized Swift agents for Claude Code and VS Code Copilot. Covers concurrency, SwiftUI, Core ML, Foundation Models, MLX Swift, on-device AI, accessibility, security, testing, App Store review, Meta glasses SDK, SwiftData, visionOS, StoreKit 2, and performance.
Reviews SwiftUI code for best practices on modern APIs, maintainability, and performance.
Expert Swift 6.2 + SwiftUI for iOS 26, macOS 26, iPadOS 26, watchOS 26, visionOS 26, tvOS 26 with Liquid Glass, @Observable, actors, SwiftData inheritance