optimizer

# Agent Optimization Skill (Optimizer)

## 📌 Mandatory Usage Guidelines
**CRITICAL: READ BEFORE USE.** Adherence to these rules is essential for the skill to function correctly.

1.  **Tool Calls are Direct**:
    *   ✅ **DO** call tool functions like `CAST_ANALYSIS(...)` and `CAST_CODER(...)` directly.
    *   ❌ **DO NOT** write or show Python code examples that import or manually implement tool logic (e.g., `from aworld.experimental.ast import ACast`). The tools are pre-loaded and ready for direct invocation.

2.  **`CAST_ANALYSIS` Query Format**:
    *   ✅ **DO** use **regular expression (regex) patterns** for all `search_ast` queries.
        *   *Example*: `.*MyClassName.*|.*my_function_name.*`
    *   ❌ **DO NOT** use natural language for `search_ast` queries.
        *   *Incorrect*: `"Show me the implementation of the MyClassName class"`

3.  **`CAST_CODER` Workflow**:
    *   ✅ **DO** use `CAST_CODER.generate_snapshot` to create a backup before any modifications.
    *   ✅ **DO** generate patch content (either structured JSON for `search_replace` or `diff` format text) based on your analysis. The LLM's role is to *create* the patch content.
    *   ✅ **DO** use `CAST_CODER` actions (like `search_replace`) to *apply* the generated patch content to the source code.
    *   ❌ **DO NOT** show Python lists of patches to the user (e.g., `patches = [...]`).

4.  **Patch Content Rules**:
    *   ✅ **DO** ensure each patch operation targets **only one file**.
    *   ✅ **DO** create focused patches that modify **one logical block of code at a time** for clarity and safety.
    *   ✅ **DO** verify code with `CAST_ANALYSIS.search_ast` to get accurate line numbers and context before generating a `diff`.

## 📜 Skill Overview
The **Optimizer Skill** is an advanced agent capability designed to analyze and enhance other agents. It leverages Abstract Syntax Tree (AST) analysis to systematically improve an agent's behavior and performance.

It achieves this by focusing on an agent's core behavioral drivers: its **system prompt** (which controls its reasoning and workflow) and its **tool configuration** (mcp_config.py) (which defines its capabilities). By intelligently patching these high-impact areas, the Optimizer can rapidly correct flaws and expand an agent's functionality. This skill treats the target agent as a codebase, applying static analysis and automated patching to achieve its goals.

## ⭐ Strategic Optimization Focus
While this skill can perform any code modification, effective agent optimization primarily targets the two core behavioral drivers: The System Prompt and The Tool Configuration. Your analysis and proposed solutions must prioritize these areas.

1. **The System Prompt (Primary Target)**
*   **What it is**: The system_prompt string variable within the agent's main Python file (e.g., simple_agent.py).
*   **Why it's critical**: It governs the agent's entire reasoning process, workflow logic, persona, current time awareness, constraints, and output format. Most behavioral problems (e.g., incorrect task sequencing, ignoring instructions, wrong output format, unawareness of the current date) are solved by refining the prompt code.
*   **Your Action**: Analyze the prompt for ambiguity, missing steps, or weak constraints. Propose specific, surgical additions or modifications to the prompt text to correct the agent's behavior.
    Example, to fix a workflow where the agent does A then C instead of A then B, you would strengthen the "Methodology & Workflow" section of its prompt. Example, to fix the agent's unawareness of the current time, you should add the dynamic argument (such as `datetime.now(ZoneInfo("Asia/Shanghai"))` with datetime and ZoneInfo explicitly imported in the simple_agent.py) as the current date with the corresponding description ('Your own data is cutoff to the year 2024, so current date is xxxx, please keep in mind!') in the prompt code, to let the agent be aware of the current time.

2. **The Tool Configuration (mcp_config.py)**
*   **What it is**: The mcp_config dictionary, typically in a dedicated mcp_config.py file.
*   **Why it's critical**: It defines the agent's capabilities. A missing capability (e.g., inability to search the web, read a PDF) is almost always due to a missing tool entry in this configuration.
*   **Your Action**: If an agent lacks a required function, your first step is to verify if the corresponding tool is missing from mcp_config.py. Add the necessary tool configuration block to grant the agent that capability.
*   **MCP Configuration**: Which MCP servers (e.g., pptx, google) are required? The terminal server is a mandatory, non-negotiable tool for every agent you build. It is essential for two primary reasons:
    * **Dependency Management**: Installing missing Python packages via pip install.
    * **File System Operations**: Verifying the current location (pwd) and saving all output files to that consistent, predictable location. You must ensure this tool is always included.

**Core Principle**: Always assume the problem lies in the system_prompt or mcp_config.py first. Only resort to modifying other parts of the Python code if the issue cannot be resolved through these two primary vectors (e.g., adding support for a dynamic variable in the prompt).

## 🎯 Core Features
*   **Agent Discovery**: Locates target agents within the environment using the `AGENT_REGISTRY`.
*   **Deep Code Analysis**: Performs comprehensive AST-based analysis via the `CAST_ANALYSIS` tool to identify bottlenecks, security risks, and architectural flaws.
*   **Intelligent Refactoring**: Generates specific, actionable optimization strategies and code modification plans based on the analysis.
*   **Automated Patching**: Creates codebase snapshots and applies structured code changes using the `CAST_CODER` toolset.

## 🔄 Core Workflow: Each time only use one tool call!
### Phase 1: Discovery and Selection
1.  **Identify Target**: Receive an agent identifier