Engineering a fully automated workflow for a Shopify store Maintaining a successful e-commerce store comes with its fair share of challenges. It demands constant attention to ever-changing details across inventory, customer experience, and platform updates. With so many moving parts, manual oversight can quickly become overwhelming, error-prone, and time-consuming. That’s where automation steps in — not just as a convenience but as a necessity to keep your store running efficiently and at scale. While Shopify offers a rich ecosystem of apps and drag-and-drop interfaces, it often requires you to trade transparency and control for convenience. Taking Back Control Let the robots worry about the boring stuff! Sooner or later, you will hit the limits with off-the-shelf apps and manual workflows and start looking for alternatives. One such alternative is to shift away from GUI-centric tools toward programmable pipelines that offer complete flexibility and control. What you want is: Full ownership of your data Enhancements tailored to your brand and products Shareable Workflows: multiple stores could use the same workflow with little to no tweaks Confidence in every step of the process Now, let’s explore how we can build an automated CI pipeline to help mitigate the issues mentioned above. As a proof-of-concept, we’ll create a pipeline to streamline our product-content workflow. The pipeline will use LLM to review the latest products on our store, optimize the title, add SEO title and description, and generate a summary for the team to review. The Stack Here’s what powers the workflow: Shopify — where our products live GitHub Actions — for orchestration and automation ShopCTL — A command line utility for Shopify store management OpenAI API — to revise product titles, generate SEO content, and provide suggestions Python and some Bash scripts — for the enrichment logic and updates First Things First — Setting Up the Stack Let’s start by setting up a GitHub Actions workflow. We’ll store pipeline configs in the .github/workflows/ directory. Create a file named enrich-products.yml inside the workflows directory. This file will define jobs for our product-content workflow. # .github/workflows/enrich-products.yml name: Shopify Product Enrichment on: workflow_dispatch: The workflow_dispatch event in GitHub Actions allows you to manually trigger a workflow from the GitHub interface or via the API, or you can schedule it to run automatically at a specific time. API Keys We’d need a few API keys to complete our configuration: OPENAI_API_KEY for AI operations and SHOPIFY_ACCESS_TOKEN to communicate with our store. Get the OpenAI API key from your OpenAI account, and set it as a secret in GitHub. Getting a Shopify access token is tricky since you need to create a dummy app to do so. Follow this official guide to get one. ShopCTL We’ll use a command-line tool to export and update our products. Let’s create a custom action that we can reuse to reference in our pipeline. Create a file called setup-shopctl.yml inside the actions directory and add the following config. # .github/workflows/actions/setup-shopctl.yml name: Setup ShopCTL description: Installs Go and ShopCTL CLI runs: using: "composite" steps: - name: Set up Go uses: actions/setup-go@v5 with: go-version: "1.24" - name: Install ShopCTL shell: bash run: | sudo apt-get update sudo apt-get install -y libx11-dev go install github.com/ankitpokhrel/shopctl/cmd/shopctl@main echo "$HOME/go/bin" >> "$GITHUB_PATH" Apart from custom actions, we need to add a configuration for the store we’re operating. Create a folder called shopctl on the repo’s root and add the following config in a file called .shopconfig.yml. Replace all occurrences of store1 with your store alias. # shopctl/.shopcofig.yml ver: v0 contexts: - alias: store1 store: store1.myshopify.com currentContext: store1 Finalizing the Pipeline Full source for the pipeline can be found here. Our pipeline has four stages, viz: Export -> Enrich -> Update -> Notify Stage 1: Export Products The first step in our pipeline is to export the latest products from our store. Add a job called export-products in the enrich-products.yml file we created earlier. jobs: export-products: runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} # The secret we set earlier SHOPIFY_CONFIG_HOME: ${{ github.workspace }} # This will tell shopctl to use current dir to look for .shopconfig outputs: has-data: ${{ steps.check.outputs.has_data }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Export products run: | mkdir -p data # Export latest data (last 7 days) using the shopctl tool as latest_products.tar.gz shopctl export -r product="created_at:>=$(date -v -7d +%Y-%m-%d)" -o data/ -n latest_products -vvv - name: Check if export has data id: check run: | if [ -s data/latest_products.tar.gz ]; then echo "has_data=true" >> "$GITHUB_OUTPUT" else echo "has_data=false" >> "$GITHUB_OUTPUT" echo "No products found to process" fi - name: Upload exported products if: steps.check.outputs.has_data == 'true' uses: actions/upload-artifact@v4 with: name: exported-products path: data/latest_products.tar.gz The job above will set up ShopCTL using the custom action we created earlier. It will export all products created in the last 7 days and upload them as artifacts if any new products exist. Stage 2a: Review Catalog The next thing we want to do is to review our catalog. We’ll use the OpenAI API to review product data samples and identify the following: Issues or inconsistencies in tags, product types, or variants Missing or inconsistent inventory information Gaps in product configuration or variant structure Duplicate or overly similar products General recommendations to improve catalog quality and its completeness review-catalog: needs: export-products runs-on: ubuntu-latest env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Download product export uses: actions/download-artifact@v4 with: name: exported-products path: data/ - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.13" - name: Install dependencies run: pip install openai - name: Run catalog review script run: | # Assuming your script is saved in scripts/review_catalog.py python scripts/review_catalog.py \ data/latest_products.tar.gz \ data/review_summary.md - name: Upload catalog summary uses: actions/upload-artifact@v4 with: name: catalog-review-summary path: data/review_summary.md - name: Final summary run: echo "✅ Shopify product catalog review completed!" Notice the needs section. We want to run it after products are exported and made available as artifacts. We also need to set up Python, as our review script is written in Python. You can use any language of your choice here. The script generates review_summary.md, which is uploaded as an artifact in the next step (example output below). ## Identified Issues ### 1. Missing or Inconsistent Information: - Some products have missing or inconsistent `productType` (e.g. `"gid://shopify/Product/8790718087392"`, `"gid://shopify/Product/879071795632 The sample script and the prompt can be found here. Stage 2b: Enrich Products Similar to the review-catalog job, add an enrich-products job that will run the script to review the product title and generate an SEO title and description for the product using OpenAI. This job runs in parallel with the review catalog job and generates a CSV with details on metadata to update. The sample script and the prompt can befound here. Stage 3: Update Products Once the metadata is generated in stage 2b, we can update products using ShopCTL. We’ll use a bash script instead of Python at this stage. Add a job called update-products, as shown below. update-products: needs: enrich-products runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} SHOPIFY_CONFIG_HOME: ${{ github.workspace }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Download enriched products uses: actions/download-artifact@v4 with: name: enriched-products path: data/ - name: Apply updates using shopctl run: | mkdir -p logs touch logs/audit.txt while IFS=, read -r pid new_title seo_title seo_desc; do # Strip leading/trailing quotes seo_desc="${seo_desc%\"}" seo_desc="${seo_desc#\"}" # Use shopctl to update product details if output=$(shopctl product update "$pid" \ --title "$new_title" \ --seo-title "$seo_title" \ --seo-desc "$seo_desc" 2>&1); then echo "$pid,success" >> logs/audit.txt else sanitized_error=$(echo "$output" | tr '\n' ' ' | sed 's/,/ /g') echo "$pid,failure,$sanitized_error" >> logs/audit.txt fi done < <(tail -n +2 data/enriched_products.csv) - name: Upload audit log uses: actions/upload-artifact@v4 with: name: product-audit-log path: logs/audit.txt - name: Final summary run: echo "✅ Shopify product enrichment and updates completed!" The job is relatively simple; it uses a bash script to read from the CSV file generated in the previous step, update the product using ShopCTL, and create a log file. Stage 4: Notify Now, the only thing remaining is to notify interested parties that the job has been completed (or failed) and what has changed. You can either send a Slack notification or email the details. We will simply fetch and print the logs for the tutorial’s sake. notify: needs: [review-catalog, update-products] runs-on: ubuntu-latest steps: - name: Download audit log uses: actions/download-artifact@v4 with: name: product-audit-log path: logs/ - name: Download catalog review uses: actions/download-artifact@v4 with: name: catalog-review-summary path: data/ - name: Print audit summary run: | ls -lah logs/ ls -lah data/ echo "🧾 Shopify Product Update Audit" echo "-------------------------------" total=$(wc -l < logs/audit.txt) updated=$(grep -c ',success' logs/audit.txt || true) failed=$(grep -c ',failure' logs/audit.txt || true) echo "✅ Success: $updated" echo "❌ Failed: $failed" echo "📦 Total Processed: $total" echo "" echo "📋 Detailed Audit:" cat logs/audit.txt - name: Print catalog review summary run: | echo "" echo "🧠 Catalog Review Summary" echo "-------------------------" cat data/review_summary.md Putting It All Together The example above showcases how you can leverage available tools to create something unique and powerful, tailored to your use case, without handing over sensitive store data to external apps. While our proof-of-concept skips over a few production-grade essentials — like using a staging store for manual approvals and proper error handling — it gives you a general idea of how to get started. Takeaway This level of flexibility and control opens up limitless possibilities — from automated A/B testing on product copies, multi-language enrichment workflows, dynamic pricing experiments, and automated inventory cleanup to personalized recommendations and beyond. With every step in your control, you can experiment with new ideas, adapt quickly to market shifts, and scale operations effortlessly as your business grows. Engineering a fully automated workflow for a Shopify store Engineering a fully automated workflow for a Shopify store Maintaining a successful e-commerce store comes with its fair share of challenges. It demands constant attention to ever-changing details across inventory, customer experience, and platform updates. With so many moving parts, manual oversight can quickly become overwhelming, error-prone, and time-consuming. That’s where automation steps in — not just as a convenience but as a necessity to keep your store running efficiently and at scale. While Shopify offers a rich ecosystem of apps and drag-and-drop interfaces, it often requires you to trade transparency and control for convenience. Taking Back Control Let the robots worry about the boring stuff! Let the robots worry about the boring stuff! Sooner or later, you will hit the limits with off-the-shelf apps and manual workflows and start looking for alternatives. One such alternative is to shift away from GUI-centric tools toward programmable pipelines that offer complete flexibility and control. What you want is: Full ownership of your data Enhancements tailored to your brand and products Shareable Workflows: multiple stores could use the same workflow with little to no tweaks Confidence in every step of the process Full ownership of your data Enhancements tailored to your brand and products Shareable Workflows: multiple stores could use the same workflow with little to no tweaks Confidence in every step of the process Now, let’s explore how we can build an automated CI pipeline to help mitigate the issues mentioned above. As a proof-of-concept, we’ll create a pipeline to streamline our product-content workflow. The pipeline will use LLM to review the latest products on our store, optimize the title, add SEO title and description, and generate a summary for the team to review. The Stack Here’s what powers the workflow: Shopify — where our products live GitHub Actions — for orchestration and automation ShopCTL — A command line utility for Shopify store management OpenAI API — to revise product titles, generate SEO content, and provide suggestions Python and some Bash scripts — for the enrichment logic and updates Shopify — where our products live Shopify Shopify GitHub Actions — for orchestration and automation GitHub Actions GitHub Actions ShopCTL — A command line utility for Shopify store management ShopCTL ShopCTL OpenAI API — to revise product titles, generate SEO content, and provide suggestions OpenAI API OpenAI API Python and some Bash scripts — for the enrichment logic and updates Python Python Bash Bash First Things First — Setting Up the Stack Let’s start by setting up a GitHub Actions workflow. We’ll store pipeline configs in the .github/workflows/ directory. Create a file named enrich-products.yml inside the workflows directory. This file will define jobs for our product-content workflow. .github/workflows/ enrich-products.yml # .github/workflows/enrich-products.yml name: Shopify Product Enrichment on: workflow_dispatch: # .github/workflows/enrich-products.yml name: Shopify Product Enrichment on: workflow_dispatch: The workflow_dispatch event in GitHub Actions allows you to manually trigger a workflow from the GitHub interface or via the API, or you can schedule it to run automatically at a specific time. workflow_dispatch schedule it to run automatically schedule it to run automatically API Keys API Keys We’d need a few API keys to complete our configuration: OPENAI_API_KEY for AI operations and SHOPIFY_ACCESS_TOKEN to communicate with our store. OPENAI_API_KEY SHOPIFY_ACCESS_TOKEN Get the OpenAI API key from your OpenAI account, and set it as a secret in GitHub . Getting a Shopify access token is tricky since you need to create a dummy app to do so. Follow this official guide to get one. your OpenAI account , your OpenAI account as a secret in GitHub as a secret in GitHub this official guide this official guide ShopCTL ShopCTL We’ll use a command-line tool to export and update our products. Let’s create a custom action that we can reuse to reference in our pipeline. Create a file called setup-shopctl.yml inside the actions directory and add the following config. command-line tool command-line tool setup-shopctl.yml # .github/workflows/actions/setup-shopctl.yml name: Setup ShopCTL description: Installs Go and ShopCTL CLI runs: using: "composite" steps: - name: Set up Go uses: actions/setup-go@v5 with: go-version: "1.24" - name: Install ShopCTL shell: bash run: | sudo apt-get update sudo apt-get install -y libx11-dev go install github.com/ankitpokhrel/shopctl/cmd/shopctl@main echo "$HOME/go/bin" >> "$GITHUB_PATH" # .github/workflows/actions/setup-shopctl.yml name: Setup ShopCTL description: Installs Go and ShopCTL CLI runs: using: "composite" steps: - name: Set up Go uses: actions/setup-go@v5 with: go-version: "1.24" - name: Install ShopCTL shell: bash run: | sudo apt-get update sudo apt-get install -y libx11-dev go install github.com/ankitpokhrel/shopctl/cmd/shopctl@main echo "$HOME/go/bin" >> "$GITHUB_PATH" Apart from custom actions, we need to add a configuration for the store we’re operating. Create a folder called shopctl on the repo’s root and add the following config in a file called .shopconfig.yml . Replace all occurrences of store1 with your store alias. shopctl .shopconfig.yml store1 # shopctl/.shopcofig.yml ver: v0 contexts: - alias: store1 store: store1.myshopify.com currentContext: store1 # shopctl/.shopcofig.yml ver: v0 contexts: - alias: store1 store: store1.myshopify.com currentContext: store1 Finalizing the Pipeline Full source for the pipeline can be found here. Full source for the pipeline can be found here. Full source for the pipeline can be found here . here here Our pipeline has four stages, viz: Export -> Enrich -> Update -> Notify Export -> Enrich -> Update -> Notify Stage 1: Export Products Stage 1: Export Products The first step in our pipeline is to export the latest products from our store. Add a job called export-products in the enrich-products.yml file we created earlier. export-products enrich-products.yml jobs: export-products: runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} # The secret we set earlier SHOPIFY_CONFIG_HOME: ${{ github.workspace }} # This will tell shopctl to use current dir to look for .shopconfig outputs: has-data: ${{ steps.check.outputs.has_data }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Export products run: | mkdir -p data # Export latest data (last 7 days) using the shopctl tool as latest_products.tar.gz shopctl export -r product="created_at:>=$(date -v -7d +%Y-%m-%d)" -o data/ -n latest_products -vvv - name: Check if export has data id: check run: | if [ -s data/latest_products.tar.gz ]; then echo "has_data=true" >> "$GITHUB_OUTPUT" else echo "has_data=false" >> "$GITHUB_OUTPUT" echo "No products found to process" fi - name: Upload exported products if: steps.check.outputs.has_data == 'true' uses: actions/upload-artifact@v4 with: name: exported-products path: data/latest_products.tar.gz jobs: export-products: runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} # The secret we set earlier SHOPIFY_CONFIG_HOME: ${{ github.workspace }} # This will tell shopctl to use current dir to look for .shopconfig outputs: has-data: ${{ steps.check.outputs.has_data }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Export products run: | mkdir -p data # Export latest data (last 7 days) using the shopctl tool as latest_products.tar.gz shopctl export -r product="created_at:>=$(date -v -7d +%Y-%m-%d)" -o data/ -n latest_products -vvv - name: Check if export has data id: check run: | if [ -s data/latest_products.tar.gz ]; then echo "has_data=true" >> "$GITHUB_OUTPUT" else echo "has_data=false" >> "$GITHUB_OUTPUT" echo "No products found to process" fi - name: Upload exported products if: steps.check.outputs.has_data == 'true' uses: actions/upload-artifact@v4 with: name: exported-products path: data/latest_products.tar.gz The job above will set up ShopCTL using the custom action we created earlier. It will export all products created in the last 7 days and upload them as artifacts if any new products exist. ShopCTL ShopCTL Stage 2a: Review Catalog Stage 2a: Review Catalog The next thing we want to do is to review our catalog. We’ll use the OpenAI API to review product data samples and identify the following: Issues or inconsistencies in tags, product types, or variants Missing or inconsistent inventory information Gaps in product configuration or variant structure Duplicate or overly similar products General recommendations to improve catalog quality and its completeness Issues or inconsistencies in tags, product types, or variants Missing or inconsistent inventory information Gaps in product configuration or variant structure Duplicate or overly similar products General recommendations to improve catalog quality and its completeness review-catalog: needs: export-products runs-on: ubuntu-latest env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Download product export uses: actions/download-artifact@v4 with: name: exported-products path: data/ - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.13" - name: Install dependencies run: pip install openai - name: Run catalog review script run: | # Assuming your script is saved in scripts/review_catalog.py python scripts/review_catalog.py \ data/latest_products.tar.gz \ data/review_summary.md - name: Upload catalog summary uses: actions/upload-artifact@v4 with: name: catalog-review-summary path: data/review_summary.md - name: Final summary run: echo "✅ Shopify product catalog review completed!" review-catalog: needs: export-products runs-on: ubuntu-latest env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Download product export uses: actions/download-artifact@v4 with: name: exported-products path: data/ - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.13" - name: Install dependencies run: pip install openai - name: Run catalog review script run: | # Assuming your script is saved in scripts/review_catalog.py python scripts/review_catalog.py \ data/latest_products.tar.gz \ data/review_summary.md - name: Upload catalog summary uses: actions/upload-artifact@v4 with: name: catalog-review-summary path: data/review_summary.md - name: Final summary run: echo "✅ Shopify product catalog review completed!" Notice the needs section. We want to run it after products are exported and made available as artifacts. We also need to set up Python, as our review script is written in Python. You can use any language of your choice here. The script generates review_summary.md , which is uploaded as an artifact in the next step (example output below). needs review_summary.md ## Identified Issues ### 1. Missing or Inconsistent Information: - Some products have missing or inconsistent `productType` (e.g. `"gid://shopify/Product/8790718087392"`, `"gid://shopify/Product/879071795632 ## Identified Issues ### 1. Missing or Inconsistent Information: - Some products have missing or inconsistent `productType` (e.g. `"gid://shopify/Product/8790718087392"`, `"gid://shopify/Product/879071795632 The sample script and the prompt can be found here . found here found here Stage 2b: Enrich Products Stage 2b: Enrich Products Similar to the review-catalog job, add an enrich-products job that will run the script to review the product title and generate an SEO title and description for the product using OpenAI. This job runs in parallel with the review catalog job and generates a CSV with details on metadata to update. review-catalog enrich-products job job The sample script and the prompt can be found here . found here found here Stage 3: Update Products Stage 3: Update Products Once the metadata is generated in stage 2b, we can update products using ShopCTL . We’ll use a bash script instead of Python at this stage. ShopCTL ShopCTL Add a job called update-products , as shown below. update-products update-products: needs: enrich-products runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} SHOPIFY_CONFIG_HOME: ${{ github.workspace }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Download enriched products uses: actions/download-artifact@v4 with: name: enriched-products path: data/ - name: Apply updates using shopctl run: | mkdir -p logs touch logs/audit.txt while IFS=, read -r pid new_title seo_title seo_desc; do # Strip leading/trailing quotes seo_desc="${seo_desc%\"}" seo_desc="${seo_desc#\"}" # Use shopctl to update product details if output=$(shopctl product update "$pid" \ --title "$new_title" \ --seo-title "$seo_title" \ --seo-desc "$seo_desc" 2>&1); then echo "$pid,success" >> logs/audit.txt else sanitized_error=$(echo "$output" | tr '\n' ' ' | sed 's/,/ /g') echo "$pid,failure,$sanitized_error" >> logs/audit.txt fi done < <(tail -n +2 data/enriched_products.csv) - name: Upload audit log uses: actions/upload-artifact@v4 with: name: product-audit-log path: logs/audit.txt - name: Final summary run: echo "✅ Shopify product enrichment and updates completed!" update-products: needs: enrich-products runs-on: ubuntu-latest env: SHOPIFY_ACCESS_TOKEN: ${{ secrets.SHOPIFY_ACCESS_TOKEN }} SHOPIFY_CONFIG_HOME: ${{ github.workspace }} steps: - name: Checkout repo uses: actions/checkout@v3 - name: Setup ShopCTL uses: ./.github/workflows/actions/setup-shopctl - name: Download enriched products uses: actions/download-artifact@v4 with: name: enriched-products path: data/ - name: Apply updates using shopctl run: | mkdir -p logs touch logs/audit.txt while IFS=, read -r pid new_title seo_title seo_desc; do # Strip leading/trailing quotes seo_desc="${seo_desc%\"}" seo_desc="${seo_desc#\"}" # Use shopctl to update product details if output=$(shopctl product update "$pid" \ --title "$new_title" \ --seo-title "$seo_title" \ --seo-desc "$seo_desc" 2>&1); then echo "$pid,success" >> logs/audit.txt else sanitized_error=$(echo "$output" | tr '\n' ' ' | sed 's/,/ /g') echo "$pid,failure,$sanitized_error" >> logs/audit.txt fi done < <(tail -n +2 data/enriched_products.csv) - name: Upload audit log uses: actions/upload-artifact@v4 with: name: product-audit-log path: logs/audit.txt - name: Final summary run: echo "✅ Shopify product enrichment and updates completed!" The job is relatively simple; it uses a bash script to read from the CSV file generated in the previous step, update the product using ShopCTL, and create a log file. Stage 4: Notify Stage 4: Notify Now, the only thing remaining is to notify interested parties that the job has been completed (or failed) and what has changed. You can either send a Slack notification or email the details. We will simply fetch and print the logs for the tutorial’s sake. notify: needs: [review-catalog, update-products] runs-on: ubuntu-latest steps: - name: Download audit log uses: actions/download-artifact@v4 with: name: product-audit-log path: logs/ - name: Download catalog review uses: actions/download-artifact@v4 with: name: catalog-review-summary path: data/ - name: Print audit summary run: | ls -lah logs/ ls -lah data/ echo "🧾 Shopify Product Update Audit" echo "-------------------------------" total=$(wc -l < logs/audit.txt) updated=$(grep -c ',success' logs/audit.txt || true) failed=$(grep -c ',failure' logs/audit.txt || true) echo "✅ Success: $updated" echo "❌ Failed: $failed" echo "📦 Total Processed: $total" echo "" echo "📋 Detailed Audit:" cat logs/audit.txt - name: Print catalog review summary run: | echo "" echo "🧠 Catalog Review Summary" echo "-------------------------" cat data/review_summary.md notify: needs: [review-catalog, update-products] runs-on: ubuntu-latest steps: - name: Download audit log uses: actions/download-artifact@v4 with: name: product-audit-log path: logs/ - name: Download catalog review uses: actions/download-artifact@v4 with: name: catalog-review-summary path: data/ - name: Print audit summary run: | ls -lah logs/ ls -lah data/ echo "🧾 Shopify Product Update Audit" echo "-------------------------------" total=$(wc -l < logs/audit.txt) updated=$(grep -c ',success' logs/audit.txt || true) failed=$(grep -c ',failure' logs/audit.txt || true) echo "✅ Success: $updated" echo "❌ Failed: $failed" echo "📦 Total Processed: $total" echo "" echo "📋 Detailed Audit:" cat logs/audit.txt - name: Print catalog review summary run: | echo "" echo "🧠 Catalog Review Summary" echo "-------------------------" cat data/review_summary.md Putting It All Together The example above showcases how you can leverage available tools to create something unique and powerful, tailored to your use case, without handing over sensitive store data to external apps. While our proof-of-concept skips over a few production-grade essentials — like using a staging store for manual approvals and proper error handling — it gives you a general idea of how to get started. Takeaway This level of flexibility and control opens up limitless possibilities — from automated A/B testing on product copies, multi-language enrichment workflows, dynamic pricing experiments, and automated inventory cleanup to personalized recommendations and beyond. With every step in your control, you can experiment with new ideas, adapt quickly to market shifts, and scale operations effortlessly as your business grows.
