Automating Tasks with PIMShell: Scripts and ExamplesPIMShell is a command-line interface and automation toolkit designed to simplify product information management (PIM) workflows. Whether you’re syncing catalogs, transforming product attributes, or integrating with other systems, PIMShell provides a set of commands and scripting capabilities that let you automate repetitive tasks, enforce data quality, and scale operations across large catalogs. This article walks through practical automation patterns, example scripts, best practices, and troubleshooting tips to help you get the most from PIMShell.
What automation with PIMShell looks like
Automation typically involves:
- Scheduling repeated tasks (imports, exports, feeds).
- Applying bulk transformations to product attributes (normalizing names, fixing categories).
- Validating and reporting data quality issues automatically.
- Integrating PIM operations with CI/CD pipelines and external services (ERP, e-commerce platforms, DAM).
- Orchestrating multi-step workflows (import → transform → validate → export).
Core concepts and commands
PIMShell exposes several core commands (names here are illustrative — adapt to your PIMShell version):
- pim import — ingest product files (CSV, JSON, XML).
- pim export — export products or catalogs to specified formats.
- pim transform — apply transformations or mappings to attributes.
- pim validate — run validation rules and generate reports.
- pim sync — synchronize with external systems (APIs, FTP, S3).
- pim script — execute user-defined scripts or pipelines.
Key concepts:
- Profiles: predefined sets of options for imports/exports.
- Pipelines: chained operations that run sequentially.
- Hooks: scripts triggered before/after commands.
- Templates: reusable transformation or mapping definitions.
Scripting languages and environments
PIMShell typically supports:
- Shell scripting (bash, zsh) — for OS-level orchestration and scheduled jobs.
- Node.js/JavaScript — when using programmatic SDK bindings or JSON-heavy transformations.
- Python — for complex data manipulation, integrations, or when leveraging data libraries (pandas).
- Embedded DSL — some PIMShell builds include a small domain-specific language for mappings.
Choose the language you and your team are most comfortable with and which has the libraries you need for parsing, HTTP requests, or data processing.
Example 1 — Basic import → validate → export pipeline (bash)
This example shows a simple bash script that imports a CSV, runs validation, and exports a cleansed JSON file.
#!/usr/bin/env bash set -euo pipefail SRC_FILE="products_incoming.csv" IMPORT_PROFILE="csv_default" EXPORT_PROFILE="json_cleansed" REPORT_DIR="./reports" TIMESTAMP=$(date +"%Y%m%d_%H%M%S") mkdir -p "$REPORT_DIR" # 1. Import pim import --file "$SRC_FILE" --profile "$IMPORT_PROFILE" --log "$REPORT_DIR/import_$TIMESTAMP.log" # 2. Validate pim validate --profile "standard_rules" --output "$REPORT_DIR/validation_$TIMESTAMP.json" # Fail if critical validation errors CRITICAL_COUNT=$(jq '.errors | length' "$REPORT_DIR/validation_$TIMESTAMP.json") if [ "$CRITICAL_COUNT" -gt 0 ]; then echo "Critical validation errors found: $CRITICAL_COUNT" exit 1 fi # 3. Export cleansed product data pim export --profile "$EXPORT_PROFILE" --output "products_cleansed_$TIMESTAMP.json" echo "Pipeline completed successfully. Export: products_cleansed_$TIMESTAMP.json" Example 2 — Transform attributes with Node.js
Use Node.js for JSON transformations, mapping incoming attribute names to your PIM schema and normalizing values.
#!/usr/bin/env node const fs = require('fs'); const input = JSON.parse(fs.readFileSync('products_raw.json', 'utf8')); const output = input.map(prod => { // Map attributes const mapped = { id: prod.sku || prod.id, title: prod.name && prod.name.trim(), price: parseFloat(prod.price) || null, categories: (prod.categories || '').split('|').map(c => c.trim()).filter(Boolean), in_stock: Boolean(prod.stock && prod.stock > 0), }; // Normalize title capitalization if (mapped.title) { mapped.title = mapped.title.split(' ').map(w => w[0].toUpperCase() + w.slice(1)).join(' '); } return mapped; }); fs.writeFileSync('products_transformed.json', JSON.stringify(output, null, 2)); console.log('Transformation complete: products_transformed.json'); Call this within a PIMShell pipeline:
pim import --file products_incoming.json --profile json_raw node transform_products.js pim import --file products_transformed.json --profile json_mapped --mode merge Example 3 — Scheduled sync with external API (Python)
Automate daily syncs from an external supplier API into your PIM using Python and requests.
#!/usr/bin/env python3 import requests, json, subprocess, os from datetime import datetime API_URL = "https://supplier.example.com/api/products" API_KEY = os.environ.get("SUPPLIER_API_KEY") OUT_FILE = f"daily_supplier_{datetime.utcnow().strftime('%Y%m%d')}.json" resp = requests.get(API_URL, headers={"Authorization": f"Bearer {API_KEY}"}, timeout=30) resp.raise_for_status() data = resp.json() # Simple filter: only active products filtered = [p for p in data if p.get('status') == 'active'] with open(OUT_FILE, 'w', encoding='utf-8') as f: json.dump(filtered, f, ensure_ascii=False, indent=2) # Import into PIM subprocess.run(["pim", "import", "--file", OUT_FILE, "--profile", "supplier_default"], check=True) print("Sync complete:", OUT_FILE) Schedule with cron or systemd timers.
Example 4 — Using hooks for pre-processing
Hooks let you run scripts automatically before or after PIMShell commands. Example: a pre-import hook that validates CSV encoding and converts Excel to CSV.
pre_import_hook.sh:
#!/usr/bin/env bash FILE="$1" # Convert XLSX to CSV if needed if file "$FILE" | grep -q 'Microsoft Excel'; then in2csv "$FILE" > "${FILE%.*}.csv" echo "${FILE%.*}.csv" else echo "$FILE" fi Configure your import profile to run this hook and consume the returned file path.
Example 5 — CI/CD integration (GitHub Actions)
Automate running PIMShell validation whenever product data changes in a repo.
.github/workflows/pim-validate.yml:
name: PIM Validate on: push: paths: - 'data/products/**' jobs: validate: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: '3.11' - name: Install tools run: | pip install some-deps curl -sSL https://get.pimshell.example/install.sh | bash - name: Run PIM validate run: pim validate --profile standard_rules --files data/products/*.json Error handling and retries
- Use exit codes to fail pipelines on critical errors.
- Implement exponential backoff for flaky network calls.
- Produce machine-readable logs (JSON) for downstream parsing.
- Capture and surface partial successes (e.g., imported ⁄1000 records).
Best practices
- Keep transformations idempotent — running them twice should not corrupt data.
- Use profiles and templates to avoid repeating CLI flags.
- Store transformations and scripts in version control.
- Test scripts on a staging subset before running on production catalogs.
- Monitor task durations and set alerts for unusually long runs.
Troubleshooting tips
- When imports fail, check file encoding and delimiter mismatches.
- Use dry-run or –preview modes before destructive operations.
- Inspect logs for stack traces and attach timestamps when asking for support.
- Validate API credentials and rate limits when syncing external systems.
Security and credentials
- Store API keys in environment variables or secret stores (Vault, GitHub Secrets).
- Avoid embedding credentials in scripts checked into VCS.
- Limit service accounts to the least privileges needed (read/import but not delete if unnecessary).
Conclusion
Automating with PIMShell increases reliability and throughput for product data operations. By combining simple shell scripts, higher-level language transforms, hooks, and CI/CD integration, you can build repeatable, auditable pipelines that keep your catalogs clean and synchronized. Start small with a single import-validate-export flow, then expand to scheduled syncs and event-driven workflows as your needs grow.
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