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* feat(onboarding): recommend plugins and skills from installed apps Scan installed macOS apps during classic onboarding (TCC-free), gather candidates from official catalogs + ClawHub search, let the configured model pick genuine matches, and offer an opt-in multiselect install step. Adds a device.apps node-host command (default-off sharing, Android-parity envelope) so remote gateways can request a paired Mac's inventory, and a wizard.appRecommendations kill switch. Custom setup-inference completions no longer inherit the 32-token verification-probe output cap. * feat(onboarding): recommend apps in guided flow * fix(onboarding): harden app recommendations against ClawHub self-promotion Third-party ClawHub skills are never pre-selected regardless of model tier (publisher-controlled listing text reaches the matcher prompt and could promote itself); their labels now say they install third-party code. Installed-app scans follow symlinked .app bundles. Matcher output stays bounded by the resolved model's own maxTokens budget (documented invariant). * fix(onboarding): key official catalog candidates by resolved plugin id Real catalog entries are package manifests without a top-level id; keying the candidate map and channel/provider classification by entry.id collapsed the whole official catalog into one undefined-keyed entry, so no official plugin or channel was ever recommended. Regression test runs against the bundled catalogs. * fix(onboarding): satisfy lint, types, deadcode, and migration gates Split the guided-onboarding test into a self-contained custodian suite to stay under max-lines. Narrow app-recommendation exports (drop dead node-payload normalizer, unexport internal types/helpers, route candidate tests through the public API), replace map-spread with a helper, unexport device.apps result types, add installedAppsSharing to node-host migration expectations, cast the wizard multiselect mock, and regenerate the docs map. * test(onboarding): register new live test in the shard classifier