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Zhongwei Li
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commands/background.md
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237
commands/background.md
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---
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description: Fires off a agent in the background to complete tasks autonomously
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argument-hint: [user-prompt] | [task-file-name]
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allowed-tools: Read, Task, TodoWrite
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---
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# Background PySpark Data Engineer Agent
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Launch a PySpark data engineer agent to work autonomously in the background on ETL tasks, data pipeline fixes, or code reviews.
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## Usage
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**Option 1: Direct prompt**
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```
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/background "Fix the validation issues in g_xa_mg_statsclasscount.py"
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```
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**Option 2: Task file from .claude/tasks/**
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```
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/background code_review_fixes_task_list.md
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```
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## Variables
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- `TASK_INPUT`: Either a direct prompt string or a task file name from `.claude/tasks/`
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- `TASK_FILE_PATH`: Full path to task file if using a task file
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- `PROMPT_CONTENT`: The actual prompt to send to the agent
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## Instructions
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### 1. Determine Task Source
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Check if `$ARGUMENTS` looks like a file name (ends with `.md` or contains no spaces):
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- If YES: It's a task file name from `.claude/tasks/`
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- If NO: It's a direct user prompt
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### 2. Load Task Content
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**If using task file:**
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1. List all available task files in `.claude/tasks/` directory
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2. Find the task file matching the provided name (exact match or partial match)
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3. Read the task file content
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4. Use the full task file content as the prompt
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**If using direct prompt:**
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1. Use the `$ARGUMENTS` directly as the prompt
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### 3. Launch PySpark Data Engineer Agent
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Launch the specialized `pyspark-data-engineer` agent using the Task tool:
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**Important Configuration:**
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- **subagent_type**: `pyspark-data-engineer`
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- **model**: `sonnet` (default) or `opus` for complex tasks
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- **description**: Short 3-5 word description based on task type
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- **prompt**: Complete, detailed instructions including:
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- The task content (from file or direct prompt)
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- Explicit instruction to follow `.claude/CLAUDE.md` best practices
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- Instruction to run quality gates (syntax check, linting, formatting)
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- Instruction to create a comprehensive final report
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**Prompt Template:**
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```
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You are a PySpark data engineer working on the Unify 2.1 Data Migration project using Azure Synapse Analytics.
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CRITICAL INSTRUCTIONS:
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- Read and follow ALL guidelines in .claude/CLAUDE.md
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- Use .claude/rules/python_rules.md for coding standards
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- Maximum line length: 240 characters
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- No blank lines inside functions
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- Use @synapse_error_print_handler decorator on all methods
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- Use NotebookLogger for all logging (not print statements)
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- Use TableUtilities methods for DataFrame operations
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TASK TO COMPLETE:
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{TASK_CONTENT}
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QUALITY GATES (MUST RUN BEFORE COMPLETION):
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1. Syntax validation: python3 -m py_compile <file_path>
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2. Linting: ruff check python_files/
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3. Formatting: ruff format python_files/
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FINAL REPORT REQUIREMENTS:
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Provide a comprehensive report including:
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1. Summary of changes made
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2. Files modified with line numbers
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3. Quality gate results (syntax, linting, formatting)
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4. Testing recommendations
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5. Any issues encountered and resolutions
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6. Next steps or follow-up tasks
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Work autonomously and complete all tasks in the list. Use your available tools to read files, make edits, run tests, and validate your work.
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```
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### 4. Inform User
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After launching the agent, inform the user:
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- Agent has been launched in the background
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- Task being worked on (summary)
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- Estimated completion time (if known from task file)
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- The agent will work autonomously and provide a final report
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## Task File Structure
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Expected task file format in `.claude/tasks/`:
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```markdown
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# Task Title
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**Date Created**: YYYY-MM-DD
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**Priority**: HIGH/MEDIUM/LOW
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**Estimated Total Time**: X minutes
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**Files Affected**: N
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## Task 1: Description
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**File**: path/to/file.py
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**Line**: 123
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**Estimated Time**: X minutes
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**Severity**: CRITICAL/HIGH/MEDIUM/LOW
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**Current Code**:
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```python
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# code
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```
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**Required Fix**:
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```python
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# fixed code
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```
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**Reason**: Explanation
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**Testing**: How to verify
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---
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(Repeat for each task)
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```
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## Examples
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### Example 1: Using Task File
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```
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User: /background code_review_fixes_task_list.md
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Agent Response:
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1. Lists available task files
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2. Finds and reads code_review_fixes_task_list.md
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3. Launches pyspark-data-engineer agent with task content
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4. Informs user: "PySpark data engineer agent launched to complete 9 code review fixes (est. 27 minutes)"
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```
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### Example 2: Using Direct Prompt
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```
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User: /background "Add data validation methods to the statsclasscount gold table and ensure they are called in the transform method"
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Agent Response:
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1. Uses the prompt directly
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2. Launches pyspark-data-engineer agent with the prompt
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3. Informs user: "PySpark data engineer agent launched to add data validation methods"
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```
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### Example 3: Partial Task File Name Match
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```
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User: /background code_review
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Agent Response:
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1. Lists task files and finds "code_review_fixes_task_list.md"
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2. Confirms match with user or proceeds if unambiguous
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3. Launches agent with task content
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```
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## Available Task Files
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List available task files from `.claude/tasks/` directory when user runs the command without arguments or with "list" argument:
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```
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/background
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/background list
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```
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Output:
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```
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Available task files in .claude/tasks/:
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1. code_review_fixes_task_list.md (9 tasks, 27 min, HIGH priority)
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Usage:
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/background <task-file-name> - Run agent with task file
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/background "your prompt" - Run agent with direct prompt
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/background list - Show available task files
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```
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## Agent Workflow
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The pyspark-data-engineer agent will:
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1. **Read Context**: Load .claude/CLAUDE.md, .claude/rules/python_rules.md
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2. **Analyze Tasks**: Break down task list into actionable items
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3. **Execute Changes**: Read files, make edits, apply fixes
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4. **Validate Work**: Run syntax checks, linting, formatting
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5. **Test Changes**: Execute relevant tests if available
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6. **Generate Report**: Comprehensive summary of all work completed
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## Best Practices
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### For Task Files
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- Keep tasks atomic and well-defined
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- Include file paths and line numbers
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- Provide current code and required fix
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- Specify testing requirements
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- Estimate time for each task
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- Prioritize tasks (CRITICAL, HIGH, MEDIUM, LOW)
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### For Direct Prompts
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- Be specific about files and functionality
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- Reference table/database names
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- Specify layer (bronze, silver, gold)
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- Include any business requirements
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- Mention quality requirements
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## Success Criteria
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Agent task completion requires:
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- ✅ All code changes implemented
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- ✅ Syntax validation passes (python3 -m py_compile)
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- ✅ Linting passes (ruff check)
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- ✅ Code formatted (ruff format)
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- ✅ No new issues introduced
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- ✅ Comprehensive final report provided
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## Notes
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- The agent has access to all project files and tools
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- It follows medallion architecture patterns (bronze/silver/gold)
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- It uses established utilities (SparkOptimiser, TableUtilities, NotebookLogger)
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- It respects project coding standards (240 char lines, no blanks in functions)
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- It works autonomously without requiring additional user input
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- Results are reported back when complete
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