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+---
+steps:
+  - name: Install sq from GitHub releases
+    run: |
+      # Install sq binary from official install script
+      # This downloads the latest release from GitHub and installs it to /usr/local/bin
+      /bin/sh -c "$(curl -fsSL https://sq.io/install.sh)"
+      
+  - name: Verify sq installation
+    run: |
+      sq version
+      echo "sq is installed and ready to use"
+---
+<!--
+## sq Data Wrangler
+
+This shared configuration provides setup for `sq`, a command-line tool that offers jq-style access to structured data sources including SQL databases, CSV, Excel, and other document formats.
+
+### About sq
+
+`sq` is the lovechild of sql+jq. It executes jq-like queries or database-native SQL, can join across sources (e.g., join a CSV file to a Postgres table), and outputs to multiple formats including JSON, Excel, CSV, HTML, Markdown, and XML.
+
+**Links:**
+- Documentation: https://sq.io/
+- GitHub Repository: https://github.com/neilotoole/sq
+- Terminal Trove: https://terminaltrove.com/sq/
+- Docker Image: https://github.com/neilotoole/sq/pkgs/container/sq
+
+### Installation
+
+The shared workflow installs the sq binary directly from GitHub releases using the official install script. This downloads the latest version and installs it to `/usr/local/bin`.
+
+### Usage in Workflows
+
+Import this shared configuration to make sq available in your workflow:
+
+```yaml
+imports:
+  - shared/sq.md
+```
+
+Then use sq commands in your workflow steps:
+
+```bash
+# Inspect a data file
+sq inspect database.db
+
+# Query a CSV file with jq-like syntax
+sq '.actor | .first_name, .last_name' actors.csv
+
+# Join data from multiple sources
+sq '@csv_data | join @postgres_db.users' data.csv
+```
+
+### Common Use Cases
+
+1. **Query structured data files**: Use jq-like syntax to query CSV, Excel, JSON files
+2. **Cross-source joins**: Combine data from different sources (databases, files)
+3. **Data format conversion**: Convert between formats (CSV to JSON, Excel to Markdown, etc.)
+4. **Database inspection**: View metadata about database structure
+5. **Database operations**: Copy, truncate, or drop tables
+6. **Data comparison**: Use `sq diff` to compare tables or databases
+
+### Example Workflow
+
+```yaml
+---
+on:
+  workflow_dispatch:
+imports:
+  - shared/sq.md
+permissions:
+  contents: read
+safe-outputs:
+  create-issue:
+    expires: 2d
+    title-prefix: "[data-analysis] "
+---
+
+# Data Analysis with sq
+
+Analyze the CSV files in the repository using sq and create a summary report.
+
+Use sq to:
+1. Inspect the data structure
+2. Query for interesting patterns
+3. Generate summary statistics
+4. Create a formatted report
+
+Available sq commands:
+- `sq inspect file.csv`
+- `sq '.table | select(.column > 100)' file.csv`
+- `sq --json '.table' file.csv`
+```
+
+### Tips
+
+- sq is installed directly and available in PATH
+- Use relative paths from the workspace root
+- Specify output format with flags like `--json`, `--csv`, `--markdown`
+- For databases, use connection strings or add sources with `sq add`
+- All operations work directly on files in the workspace
+-->
+
+You have access to the `sq` data wrangling tool for working with structured data sources.
+
+**sq capabilities:**
+- Query CSV, Excel, JSON, and database files using jq-like syntax
+- Join data across different source types
+- Convert between data formats
+- Inspect database structures and metadata
+- Perform database operations (copy, truncate, drop tables)
+- Compare data with `sq diff`
+
+**Using sq in this workflow:**
+The sq binary is installed and available in PATH. Use it directly:
+```bash
+sq [command] [arguments]
+```
+
+**Example commands:**
+```bash
+# Inspect a data file
+sq inspect file.csv
+
+# Query data with jq-like syntax
+sq '.table | .column' file.csv
+
+# Output as JSON
+sq --json '.table' file.csv
+
+# Filter and aggregate
+sq '.table | where(.value > 100) | count' file.csv
+
+# Convert to different format
+sq --markdown '.table' file.csv
+```
+
+For more information, see: https://sq.io/docs/