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添加总结
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tycon
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# Traycer AI (EN)
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# Traycer AI
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## 内容列表
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- [phase_mode_prompts](./phase_mode_prompts.md)
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- [phase_mode_tools](./phase_mode_tools.md)
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- [plan_mode_tools](./plan_mode_tools.md)
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- 📄 [phase_mode_prompts](/en/en/traycer-ai/phase_mode_prompts.md)
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- 📄 [phase_mode_tools](/en/en/traycer-ai/phase_mode_tools.md)
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- 📄 [plan_mode_tools](/en/en/traycer-ai/plan_mode_tools.md)
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## Summary of Product Tool Documents
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This directory defines the core operational logic of the Traycer.AI assistant, which is designed as a technical lead operating within an IDE, capable of breaking down complex coding tasks into high-level phases. Its functionalities are implemented through two distinct modes, each with its dedicated system prompts and toolsets.
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*完整还原。*
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- **Phase Mode**:
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- **`phase_mode_prompts.md`**: Defines the AI's role in this mode—as a technical lead, focusing on breaking down user queries into high-level phases, with read-only access to the codebase.
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- **`phase_mode_tools.md`**: Provides the toolset available in this mode, primarily including code exploration (e.g., `read_file`, `grep_search`), code navigation (e.g., `find_references`, `go_to_definition`), and finally the `write_phases` tool for outputting phased plans.
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- **Plan Mode**:
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- **`plan_mode_tools.md`**: Defines the tools available in Plan Mode. The toolset in this mode is similar to Phase Mode but adds the `think` tool for complex reasoning, and the `agent` and `hand_over_to_approach_agent` tools for creating and handing over tasks to specialized agents, indicating that this mode focuses more on detailed implementation planning and task assignment.
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In summary, `traycer-ai`, through the definition of these two modes, constructs a two-tier AI agent system capable of both high-level task decomposition and detailed implementation planning, aiming to systematically solve complex software engineering tasks.
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## phase_mode_prompts.txt
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```text
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````text
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You are `@traycerai` (aka `Traycer.AI`), a large language model based on the state-of-the-art architecture. Never mention that you were created by Anthropic.
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<role>
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NOTE: You must use one of the provided tools to generate your response. TEXT only response is strictly prohibited.
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</important>
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```
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````
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## phase_mode_tools.json
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## Traycer AI Phase Mode Tools Overview
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This document defines the set of tools available to Traycer AI in Phase Mode. These tools are primarily used for codebase exploration, file analysis, and task decomposition, helping the AI understand the user's codebase and break down tasks into executable phases.
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### Core Tool Categories
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1. **File Operation Tools**
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- `read_file`: Reads the content of files at specified paths, supporting structured summaries for large files
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- `read_partial_file`: Reads specific line ranges from files, improving efficiency for large files
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- `list_dir`: Lists directory contents, used for discovery and understanding file structure
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2. **Search Tools**
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- `file_search`: Fuzzy search based on file paths
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- `grep_search`: Fast text-based regex search
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- `file_outlines`: Gets a symbol outline for all files in a specified directory
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3. **Code Navigation Tools**
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- `find_references`: Finds references (usage, mentions, etc.) of functions, methods, classes, etc.
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- `go_to_definition`: Jumps to the definition of a symbol
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- `go_to_implementations`: Finds implementations of abstract class or function symbols
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4. **Analysis and Diagnostic Tools**
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- `get_diagnostics`: Retrieves diagnostic information for files, including errors, warnings, and suggestions
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- `web_search`: Performs web searches to obtain external knowledge and documentation
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5. **Interaction Tools**
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- `ask_user_for_clarification`: Asks the user for clarification or input on key design decisions
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- `explanation_response`: Provides clear explanations and optional Mermaid diagrams
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- `write_phases`: Breaks down coding tasks into independently executable phases
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Each tool adheres to strict parameter specifications, ensuring the AI can efficiently explore the codebase, analyze tasks, and generate reasonable phase breakdowns.
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```json
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{
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"read_file": {
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## plan_mode_tools.json
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## Traycer AI Plan Mode Tools Overview
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This document defines the set of tools available to Traycer AI in Plan Mode. These tools focus on codebase analysis, file operations, and implementation plan formulation, helping the AI deeply understand the codebase and generate detailed implementation plans.
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### Core Tool Categories
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1. **File Operation Tools**
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- `read_file`: Reads the content of files at specified paths, supporting structured summaries for large files
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- `read_partial_file`: Reads specific line ranges from files, improving efficiency for large files
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- `list_dir`: Lists directory contents, used for discovery and understanding file structure
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2. **Search Tools**
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- `file_search`: Fuzzy search based on file paths
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- `grep_search`: Fast text-based regex search
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- `file_outlines`: Gets a symbol outline for all files in a specified directory
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3. **Code Navigation Tools**
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- `find_references`: Finds references (usage, mentions, etc.) of functions, methods, classes, etc.
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- `go_to_definition`: Jumps to the definition of a symbol
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- `go_to_implementations`: Finds implementations of abstract class or function symbols
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4. **Analysis and Diagnostic Tools**
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- `get_diagnostics`: Retrieves diagnostic information for files, including errors, warnings, and suggestions
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- `web_search`: Performs web searches to obtain external knowledge and documentation
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- `think`: A thinking tool for complex reasoning or brainstorming
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5. **Planning Tools**
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- `agent`: Creates specialized agents for specific tasks
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- `hand_over_to_approach_agent`: Hands over tasks to an approach agent to write high-level approaches
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- `explanation_response`: Provides clear explanations and optional Mermaid diagrams
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### Differences from Phase Mode
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The Plan Mode toolset is similar to Phase Mode but has the following key differences:
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1. Added `think` tool for complex reasoning
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2. Added `agent` and `hand_over_to_approach_agent` tools for planning and task assignment
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3. Stricter parameter requirements for some tools
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These tools help Traycer AI in Plan Mode to deeply analyze the codebase, formulate detailed implementation plans, and create specialized agents to execute specific tasks.
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```json
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{
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"list_dir": {
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