Add selftest tui commands for manual prompt verification#5208
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Add selftest tui commands for manual prompt verification#5208
selftest tui commands for manual prompt verification#5208Conversation
Each scenario is a one-shot subcommand under `databricks selftest tui` that exercises a single cmdio helper with the simplest meaningful inputs; flags layer in customization. Used for manual visual confirmation across terminals and to eyeball the visual diff when prompt rendering changes. Coverage: ask, ask-yes-no, ask-select, prompt (--default, --mask, --validate), secret, select (--n), select-ordered (--n, --long, --filter), run-select (--rich, --conditional), spinner (--elapsed), colors. Fixture data is drawn from the public Databricks docs so the demo looks like content a user would actually encounter. Co-authored-by: Isaac
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@simonfaltum Good spot to include the pager as well, later perhaps. |
simonfaltum
reviewed
May 7, 2026
simonfaltum
reviewed
May 7, 2026
simonfaltum
reviewed
May 7, 2026
- Validate --n > 0 in cobra PreRunE (both `select` and `select-ordered`). Previously a negative --n flowed into make([]Tuple, 0, n) and panicked. - Mirror the production cluster picker more faithfully in --rich: expose State/Access/Runtime as methods on clusterItem so the templates exercise text/template's method-resolution path, and have State() return a pre-colored string via cmdio.Green/Red/Blue (matching the renderedState cache on libs/databrickscfg/cfgpickers/clusters.go). Co-authored-by: Isaac
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Summary
Adds a
databricks selftest tuigroup (hidden, like the rest ofselftest) with one subcommand per cmdio prompt entry point:prompt,secret,ask,ask-yes-no,ask-select,select,select-ordered,run-select,spinner,colors. Each runs a single helper with the simplest meaningful inputs; flags layer in customization (e.g.prompt --mask --validate,select-ordered --filter,run-select --rich,spinner --elapsed).The motivation is twofold: I want to (1) sanity-check prompts on different terminals (iTerm2, Terminal.app, Windows console, VS Code, tmux) without having to construct a real workspace flow, and (2) eyeball the visual diff when a prompt's rendering is modified — both to catch regressions and to demonstrate intentional changes side-by-side.
Fixture data is drawn from the public Databricks docs so the demo looks like content a user would actually encounter.
This pull request and its description were written by Isaac.