codereview
Conduct systematic code reviews across quality, security, performance, and architecture using multiple AI models in parallel.
Instructions
Systematic code review using external models. Covers quality, security, performance, and architecture.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Step name (e.g., 'Initial Analysis', 'Security Review') | |
| content | Yes | Your code review request for the expert reviewer. Step 1: Describe the project and define review objectives and focus areas. Step 2+: Report findings organized by quality, security, performance, architecture. Include: what to review, focus areas (security/concurrency/logic), specific concerns, confidence level. Exclude: code snippets (use `relevant_files`), issue lists (use `issues_found`). | |
| 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. | |
| models | No | List of LLM models to run in parallel (minimum 1) (will use default models (['gpt-4', 'gpt-3.5-turbo']) if not specified) | |
| issues_found | No | REQUIRED: List of issues identified with severity levels, locations, and detailed descriptions. IMPORTANT: This list is CUMULATIVE across steps. Include ALL issues found in previous steps PLUS new ones. Each dict must contain these keys: 'severity' (required, one of: 'critical', 'high', 'medium', 'low'), 'location' (required, format: 'filename:line_number' or 'filename' if line unknown), 'description' (required, detailed explanation of the issue). Example: [{'severity': 'high', 'location': 'auth.py:45', 'description': 'SQL injection vulnerability in login query - user input not sanitized'}]. Empty list is acceptable if no issues found yet. |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||