Recommend Settings

Recommend Conversion Settings

Load this skill from convert (or code-conversion-only) right before running scai code convert. It inspects the input and proposes conversion settings for the user to confirm, then returns the chosen flags to the caller.

On Entry

Tell the user:

Tuning conversion settings. I'll scan your source and suggest SnowConvert options that fit what's in your code. You'll confirm before anything runs.

Workflow

Step 1: Load the settings catalog

Run:

scai code convert --list-settings --json

This returns { "sourceLanguage": "<Dialect>", "settings": [ { "longName", "shortName", "kind", "group", "helpText", "options", "default", "optional", "allowsMultiple", "min", "max", "currentValue" }, ... ] }.

• sourceLanguage is the project's dialect — remember it as <DIALECT>.
• Treat settings[] as the ONLY valid options. Never propose a flag whose longName is not in this list.
• currentValue (when present) is what the project already has persisted — do not re-recommend a setting already at the value you'd suggest.

If the command fails, tell the user you couldn't load the catalog and skip recommendations (return no flags to the caller); do not block the conversion.

Step 2: Load dialect hints

Look for a hint file at ./reference/<DIALECT-lowercase>.md (e.g. ./reference/teradata.md). If it exists, read it — it maps source-code signals to settings with rationale. If it does not exist, proceed using each setting's helpText as your only guide (general reasoning, fewer/weaker recommendations).

Step 3: Sample the source

You do NOT need to read every file. To keep this cheap on large projects:

1. From the hint file's signal patterns (or, without hints, from setting names/help), grep source/ for each signal, e.g. grep -rIl -e 'PERIOD(' -e '.LOGON' source/.
2. Open a representative sample — cap at ~20 files or ~200 KB total. Prefer files that matched signals plus a couple of the largest files.
3. Tell the user how many files you sampled out of the total (honest coverage, not "analyzed everything").

Step 4: Build recommendations

For each catalog setting a signal supports, record:

• longName and the proposed value (for booleans: enable; for choice/text/int: the specific value).
• currentValue (from Step 1) if set.
• A one-line rationale grounded in what you found (e.g. "found PERIOD( in 3 files → split into begin/end").

Skip any setting whose currentValue already equals your proposal.

Step 5: Confirm with the user

Present the recommendations as a table (Setting | Proposed | Current | Why), then ask via ask_user_question (multiSelect = true):

"Which conversion settings should I apply? (You can select any subset, or none.)"

• one option per recommended setting, labelled --<longName> <value> — <short rationale>

If there are no recommendations, tell the user the defaults look appropriate and return no flags.

Step 6: Return flags to the caller

Turn each accepted setting into a CLI argument for scai code convert:

• Boolean settings: pass the presence flag only, e.g. --comments.
• Valued settings (choice/text/int/float/file): pass name + value, e.g. --sessionMode Ansi.

Hand the assembled flag string back to convert as <SETTINGS_FLAGS> (its Step 4 appends it to the convert command). Then return to the calling skill.

Notes

• Recommend only; never run scai code convert from this skill.
• After conversion succeeds, scai auto-persists the accepted settings to .scai/config/code-conversion-config.yaml, so next run they show up as currentValue. You don't write that file.