/education-cloud-course-catalog-migrate

name: education-cloud-course-catalog-migrate
description: "Use this skill to migrate course catalog data from external sources (CSV, PDF, website) and bulk-create Learning and LearningCourse records in Education Cloud. Triggers when a user wants to import courses, parse course catalog documents, or set up a course catalog for an academic term. Handles multi-source data ingestion, intelligent interpretation of course structures (lecture+lab linked courses via IsLinkedOnly flag, credit hours, course types), batch creation via sObject REST API, data quality validation, conflict resolution for existing courses, and gap identification. Explains what's missing (descriptions, types) and requests additional sources. Pauses for clarification on multi-component courses and conflicting data. DO NOT TRIGGER for creating individual Learning Course records manually, for creating Learning Programs (different workflow), or for enrollment/registration workflows."
metadata:
  version: "1.0"
  domains:
    - "Education"
  accessCheck:
    - type: "accessCheck"
      value: "IndustriesEducation.userHasEducationCloudAccess"
  cliTools:
    - tool: ["python3"]
      semver: ">=3.9.0"

Migrating Course Catalog to Education Cloud

Import course catalog data from multiple sources (CSV, PDF, website) and bulk-create Learning Course records with structure detection and data quality validation.

Scope

• **In scope**: Multi-source ingestion (CSV, PDF, web), Learning + LearningCourse bulk creation via sObject REST API (Composite batches up to 200), linked course detection (lecture+lab via IsLinkedOnly), credit hour parsing (Duration/DurationUnit), course type mapping, data quality validation, conflict resolution, gap identification/remediation
• **Out of scope**: Creating individual Learning Course records manually (delegate to metadata generation skills), creating Learning Programs (Learning.Type = 'LearningProgram' + separate child object), creating External Learning (Learning.Type = 'ExternalLearning'), course sections/offerings (CourseOffering object), enrollment/registration workflows (no dedicated skill yet — ask the user), SIS sync (separate integration workflow)

---

Required Inputs

Gather or infer before proceeding:

• **Target org**: Active Salesforce org with Education Cloud enabled
• **Primary data source**: CSV file, PDF document, or website URL with course catalog data
• **Business Account**: the Academic Operations Business Account to link courses to (e.g., College of Engineering)
• **Academic term** (optional): context only, not stored on Learning Course

Additional sources (gathered during workflow):

• Supplementary data sources for missing fields (descriptions, types, mappings)
• Lecture-lab mapping files (if multi-component courses detected)
• Conflict resolution decisions (if existing courses found)

Defaults unless specified:

• Course status: Active (IsActive = true on Learning)
• Batch size: 25 records per Composite API call as an agent (API supports 200; tool-call payload limits are the binding constraint — see Step 7)
• Data quality validation: Enabled
• Learning.Type: 'LearningCourse' (required for course catalog entries)

---

Execution Transport

**Resolve the API version first — do not hardcode.** `GET /services/data/` (no version segment) returns an endpoint map already resolved to the org's max version, not a `{version, label, url}` list. Pipe it into `scripts/resolve_api_version.py` to get `{"version": "69.0"}`; use as `vXX.X` everywhere below, including the transport probe.

Then probe transport health: `GET /services/data/vXX.X/limits` via headless-360 dispatch. 2xx → route all calls through it. On absent/4xx/5xx → probe other transports (other Salesforce MCP, custom MCP), use first healthy, announce it. None healthy → stop, ask user to connect one; never fabricate. (SOQL `/query` is routable on headless-360 — confirmed by live test; `/tooling/query` and `?fields=` are unconfirmed, use describe + direct record reads instead.)

Never fall back to `sf` CLI or shell out for org credentials — a slow/oversized batch is a batch-size problem (shrink it, step 7), not a transport failure, and CLI shell-out trips an unbypassable approval gate on some surfaces. A version too new for the org returns `404 NOT_FOUND` on an otherwise-healthy transport — re-read `GET /services/data/`, drop to the org's max version, retry, don't switch transport. See `references/gotchas.md` for both.

---

Workflow

All steps are sequential. Do not skip or reorder. If blocked, stop and ask for missing context.

Phase 1 — Ingest Primary Source

1. **Identify data source type** — determine if primary source is CSV, PDF, or website URL. Ask user to provide the source if not already specified.

2. **Parse primary source** — extract course data:

• For CSV: Use `Read` tool to load file, parse columns
• For PDF: Use `Read` tool (supports PDF extraction), parse text. Over 20 pages, read in sequential page-range chunks and merge parsed rows before validating completeness — see `references/large-catalog-handling.md` §1
• For website: Use `WebFetch` tool to retrieve HTML, parse course listings

Look for: course codes (identity — split into SubjectAbbreviation + CourseNumber, see step 8/9), course names (Name), credit hours (Duration + DurationUnit), descriptions (Description), course types (Lecture/Lab/Exam/Drill/Recitation), prerequisites, corequisites, linked course indicators (IsLinkedOnly), delivery modality (if the same course code repeats with different Duration/DurationUnit — see `references/large-catalog-handling.md` §2).

3. **Validate data completeness** — pipe the parsed records into the completeness helper:

   python3 scripts/validate_completeness.py --required "CourseCode,Name,Duration" --recommended "Description,CourseType" <<'JSON'
   [ { "CourseCode": "...", "Name": "...", ... }, ... ]
   JSON

The report gives present/missing counts per field. Optio