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5. Preset Tasks

Preset tasks provide quick access to common development workflows. Instead of writing task descriptions from scratch, use these pre-defined templates to jumpstart your AI interactions.

What Are Preset Tasks?​

Preset tasks are pre-written task descriptions that target common development scenarios. They automatically populate the "Task Description" area with specific instructions optimized for particular types of work.

Available Preset Tasks​

The Action Panel features several preset task buttons:

AI Summary​

  • Purpose: Generate comprehensive summaries of selected code
  • Use case: Understanding unfamiliar codebases, creating documentation
  • Instructions: Analyzes code structure, purpose, and key functionality
Smart Preview Integration

The AI Summary task is designed to instruct the AI to place the generated summary at the very beginning of each relevant source code file (e.g., as a leading comment block). This placement is crucial for optimal use with Athanor's "Smart Preview" feature, which displays the initial lines of a file. Having the summary at the top ensures it provides immediate context when using Smart Preview.

Refactor Code​

  • Purpose: Improve code quality and structure
  • Use case: Modernizing legacy code, applying best practices
  • Instructions: Focuses on maintainability, readability, and performance

Unit Tests​

  • Purpose: Generate comprehensive test coverage
  • Use case: Adding tests to existing code, test-driven development
  • Instructions: Creates thorough test cases with edge case coverage

PR Changelog​

  • Purpose: Generate release notes and change summaries
  • Use case: Documenting changes for pull requests or releases
  • Instructions: Analyzes differences and creates user-friendly summaries

How to Use Preset Tasks​

Basic Workflow​

  1. Select files in the File Explorer that you want the task to apply to
  2. Click the desired preset task button (e.g., "AI Summary")
  3. Review the auto-populated task description
  4. Modify the description if needed for your specific requirements
  5. Generate prompt using an appropriate prompt template (usually "Coder" or "Writer")

Example: Using AI Summary​

  1. Select userService.ts, userController.ts, and userTypes.ts
  2. Click "AI Summary" preset task
  3. Task description populates with instructions for the AI, including placing the summary at the start of the files.
  4. Click "Coder" prompt to generate documentation-focused prompt.
  5. Copy to AI which will provide an AI-generated summary at the beginning of each file, ready for Athanor's Smart Preview feature.

Example: Using Refactor Code​

  1. Select files containing legacy or problematic code (one file at a time is often best)
  2. Click "Refactor Code" preset task
  3. Task description populates with refactoring instructions
  4. Add context if you have specific refactoring goals
  5. Use "Coder" prompt for implementation-focused output

Example: Using PR Changelog​

Generate Pull Request descriptions and Changelog entries from commit messages.

  1. Paste commit messages into the Context field.
    • You can use a command like this to get changes since the last commit in main:
    git log --oneline --no-merges $(git merge-base HEAD main)..HEAD
  2. Click the "PR Changelog" preset task button in the Action Panel.
  3. Copy the generated prompt to your preferred AI assistant (e.g., ChatGPT, Claude, Gemini, etc.).
  4. Use the AI's response (PR description and changelog text) for platforms such as GitHub and release notes.

Editing Preset Tasks​

You can modify the auto-populated task descriptions to better fit your needs:

Adding Specificity​

After clicking a preset task, edit the description to:

  • Target specific functions or classes
  • Focus on particular aspects (performance, security, etc.)
  • Include project-specific requirements
  • Add context about coding standards or patterns

Combining with Context​

Use the Context field to provide additional information:

  • Specific coding style guidelines
  • Performance requirements
  • Compatibility constraints
  • Integration requirements

Tips for Better Results​

File Selection Strategy​

  • Include related files: Dependencies, interfaces, types
  • Add context files: README, configuration files
  • Consider scope: Don't over-select for focused tasks

Task Description Enhancement​

  • Be specific: Add details about your particular requirements
  • Include constraints: Mention limitations or requirements
  • Set expectations: Specify the type of output you want

Troubleshooting Common Issues​

Generic Responses​

If AI responses are too generic:

  • Add more specific context to the task description
  • Include examples of what you're looking for
  • Select more relevant files for context

Inappropriate Suggestions​

If suggestions don't fit your project:

  • Modify the preset task description
  • Add project-specific requirements
  • Include coding style or pattern examples

Incomplete Coverage​

If the AI misses important aspects:

  • Break the task into smaller, more focused requests
  • Use multiple preset tasks for comprehensive coverage
  • Provide additional context about missed areas

Ready to explore more advanced features? Let's look at other useful Athanor capabilities!