sap-service-layer-expert

name	sap-service-layer-expert
description	Expert guidance for writing correct, runnable Python scripts that interact with SAP Business One Service Layer REST API. Use when creating, reading, updating, or deleting SAP B1 data via the Service Layer, producing two-file output contracts (_data.json + _run.py), writing OData query strings, understanding PATCH vs PUT semantics, or looking up endpoint schemas from the bundled OpenAPI spec.

SAP Service Layer Expert

Quick start

1. Read SESSION.md for the complete ServiceLayer context-manager class.
2. Copy assets/script_template.py as the starting point for every new script.
3. Look up endpoint schemas in assets/spec/paths/{ResourceName}.yaml for field names.
4. Check ENDPOINTS.md for available HTTP methods and action functions per resource.
5. See REFERENCE.md for base URL/auth, OData params, HTTP semantics, document chaining, and schema resolution strategies.

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Two-file output contract

Every mutation script ships exactly two files under .temp/ in the repo root:

File	Contents
.temp/<operation>_data.json	Records to create/update/delete — no credentials
.temp/<operation>_run.py	assets/script_template.py with main() filled in

Naming: snake_case, verb first — patch_business_partners, create_sales_order.

# Example main()
def main(data: list, sl: ServiceLayer) -> None:
    for record in data:
        card_code = record.pop("CardCode")
        sl.patch(f"BusinessPartners('{card_code}')", record)
        log.info("Patched %s", card_code)

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Script checklist

• Derive repo root from workspace context; create .temp/ if needed
• _data.json contains only data — no credentials, no boilerplate
• _run.py starts from assets/script_template.py
• Always use PATCH (not PUT) for partial updates
• Log out in finally (handled by ServiceLayer.__exit__)
• main(data, sl) implements the full logic
• Use sl.patch() for updates (not sl.post() or PUT)
• Log each record processed with log.info()
• Verify key field names against assets/spec/paths/{Resource}_id.yaml before patching
• After creating both files, tell the user they can review and ask you to run the script

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Running a script

When the user asks to run a script:

1. Run it directly — do not check for .env files or environment variables beforehand. The script validates its own environment on startup and will print a clear error if credentials are missing.
2. Working directory: the main repo root (derived from workspace context — the app folder that owns the current task). Scripts live under .temp/ within that root, but cd to the repo root so that python-dotenv finds the .env file there.
3. Python interpreter: use the bench virtualenv Python at /workspace/development/frappe-bench/env/bin/python.

cd <main-repo-root>
/workspace/development/frappe-bench/env/bin/python .temp/<operation>_run.py

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Related skills

Skill	When to use
sap-schema-expert	Look up DB column names, table structure, and encoded values
sap-di-api-expert	COM-based automation and read/write via DI API
sap-dtw-expert	Bulk import/export via Data Transfer Workbench TSV files

See REFERENCE.md for schema resolution strategies and ⚠️ property name / DB column mapping pitfalls.