Register Sas Source Units

Register SAS Source Units into the Code Unit Registry

© Snowflake Inc. This skill and its contents are the proprietary intellectual property of Snowflake Inc.

Purpose: Bridge the isolated SAS conversion track to the existing scai test regression harness by writing Code Unit Registry (CUR) entries directly as JSON from the SAS source files. This is the first of two registration steps; the second (register-sas-converted-units) attaches the converted .sql.

Boundary (do not cross): This writes registry/<id>.json files only. It does not register a SnowConvert source dialect, touch the .NET CodeUnitRegistry engine, add SAS to MCP state machines, or join AIM claims/waves. See ../INTEGRATION.md. The contract for the JSON it writes is ../references/cur-schema.md.

When to use

• After (or alongside) a SAS conversion, when the user wants to run scai test (seed/validate) against the converted output.
• Automatically invoked by convert-sas-to-snowflake post-conversion (see that skill's wiring), or run standalone against an existing .sas corpus.

Inputs

1. SAS source — a .sas file or a directory of them.
2. Project root — the SAS conversion <output_dir>. Gains a .scai/ marker plus sibling registry/, source/, snowflake/, artifacts/ dirs so scai test recognizes it as a project.
3. Target schema (optional) — DB.SCHEMA the converted objects will live in.

Workflow

1. Resolve the project root. Default to the conversion <output_dir>. If an existing .scai/ project is present elsewhere, confirm with the user before scaffolding a new one (do not collide with an AIM project).

2. Run the emitter (deterministic, stdlib-only Python — no Snowflake needed):

   cd ../assess-sas-migration/tool
   python3 emit_cur.py source \
     --sas <dir_or_file> \
     --project-root <output_dir> \
     --source-root <dir> \
     --target-schema DB.SCHEMA
   

   For each SAS file it writes one source-side unit: source.platform = "sas", objectType inferred from the parsed blocks (macro/DATA-step logic -> procedure; pure table-building -> table), file-level dependency edges from the cross-file graph, and a signature from any %MACRO parameters. The .sas file is copied under source/ and md5-checksummed.

3. Report the counts by objectType and note that only procedure / function / macro units will produce scai test cases (tables register but are not proc-tested). Tier-3 (PySpark) files are not SQL objects and are left for the converted pass to skip.

4. Next step: run register-sas-converted-units once conversion output exists to attach files.converted and confirm objectType from the generated SQL.

Notes

• Idempotent. Unit ids are UUIDv5 of SAS.<NAME>, so re-running overwrites the same files rather than duplicating.
• objectType is provisional here. The authoritative objectType comes from the converted SQL in register-sas-converted-units; this pass uses a block heuristic so units are registerable before conversion completes.
• Verify entries with scai code where (queryable fields) and scai test seed --where "source.platform = 'sas'".