chat
Conduct multi-turn conversations with an AI assistant using project context and file references for code review and analysis.
Instructions
General chat with AI assistant. Supports multi-turn conversations with project context and file inclusion.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Step name (e.g., 'Initial Analysis', 'Security Review') | |
| content | Yes | Your question to the AI Assistant. Provide detailed context: your goal, what you've tried, what worked, any specific challenges. IMPORTANT: Always include paths to relevant files in `relevant_files` - do NOT skip this step. | |
| step_number | Yes | Current step | |
| next_action | Yes | Recommended next action: 'continue' to proceed, 'stop' to end | |
| base_path | Yes | Absolute path to project root to id the project and load project files | |
| thread_id | No | Thread ID to continue previous conversation and preserve context. WHEN TO USE: - None/omit: Starting a brand new review or chat session (step_number=1) - Provide thread_id: Continuing a multi-step workflow from a previous response (step_number>1) The thread_id is returned in every response - save it and reuse it for follow-up steps. | |
| relevant_files | No | Absolute paths of ALL files relevant to this question (up to 100 files). CRITICAL: For project-level questions (features, architecture, design), you MUST include project documentation (README.md, docs/, architecture diagrams). For code-specific questions, include the implementation files, related modules, tests, and configs. Example 1: 'What feature should we build?' → Include README.md, src/server.py, config/*.*, tests/. Example 2: 'Review this function' → Include the file with the function, related modules, tests, and documentation. | |
| model | No | LLM Model name to use (default: gpt-4) |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||