format + lint

Author: bhouston

.changeset/convert-to-zod.md

@@ -1,6 +1,6 @@
 ---
-"mycoder-agent": minor
-"mycoder": minor
+'mycoder-agent': minor
+'mycoder': minor
 ---
 
 Convert from JsonSchema7Type to ZodSchema for tool parameters and returns, required for Vercel AI SDK integration.

docs/LargeCodeBase_Plan.md

@@ -11,16 +11,19 @@ This document presents research findings on how leading AI coding tools handle l
 While detailed technical documentation on Claude Code's internal architecture is limited in public sources, we can infer several approaches from Anthropic's general AI architecture and Claude Code's capabilities:
 
 1. **Chunking and Retrieval Augmentation**:
+
    - Claude Code likely employs retrieval-augmented generation (RAG) to handle large codebases
    - Files are likely chunked into manageable segments with semantic understanding
    - Relevant code chunks are retrieved based on query relevance
 
 2. **Hierarchical Code Understanding**:
+
    - Builds a hierarchical representation of code (project → modules → files → functions)
    - Maintains a graph of relationships between code components
    - Prioritizes context based on relevance to the current task
 
 3. **Incremental Context Management**:
+
    - Dynamically adjusts the context window to include only relevant code
    - Maintains a "working memory" of recently accessed or modified files
    - Uses sliding context windows to process large files sequentially
@@ -35,16 +38,19 @@ While detailed technical documentation on Claude Code's internal architecture is
 Aider's approach to handling large codebases can be inferred from its open-source codebase and documentation:
 
 1. **Git Integration**:
+
    - Leverages Git to track file changes and understand repository structure
    - Uses Git history to prioritize recently modified files
    - Employs Git's diff capabilities to minimize context needed for changes
 
 2. **Selective File Context**:
+
    - Only includes relevant files in the context rather than the entire codebase
    - Uses heuristics to identify related files based on imports, references, and naming patterns
    - Implements a "map-reduce" approach where it first analyzes the codebase structure, then selectively processes relevant files
 
 3. **Prompt Engineering and Chunking**:
+
    - Designs prompts that can work with limited context by focusing on specific tasks
    - Chunks large files and processes them incrementally
    - Uses summarization to compress information about non-focal code parts
@@ -90,6 +96,7 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ```
 
 **Implementation Details:**
+
 - Create a lightweight indexer that runs during project initialization
 - Generate embeddings for code files, focusing on API definitions, function signatures, and documentation
 - Build a graph of relationships between files based on imports/exports and references
@@ -120,6 +127,7 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ```
 
 **Implementation Details:**
+
 - Develop a working set manager that tracks currently relevant files
 - Implement a relevance scoring algorithm that considers:
   - Semantic similarity to the current task
@@ -148,6 +156,7 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ```
 
 **Implementation Details:**
+
 - Chunk files at meaningful boundaries (functions, classes, modules)
 - Implement overlapping chunks to maintain context across boundaries
 - Develop a progressive loading strategy:
@@ -181,6 +190,7 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ```
 
 **Implementation Details:**
+
 - Implement a multi-level caching system:
   - Token cache: Store tokenized representations of files to avoid re-tokenization
   - Embedding cache: Store vector embeddings for semantic search
@@ -209,6 +219,7 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ```
 
 **Implementation Details:**
+
 - Improve task decomposition to identify parallelizable sub-tasks
 - Implement smart context distribution to sub-agents:
   - Provide each sub-agent with only the context it needs
@@ -222,16 +233,19 @@ Based on the research findings, we recommend the following enhancements to MyCod
 ## Implementation Roadmap
 
 ### Phase 1: Foundation (1-2 months)
+
 - Develop the basic indexing system for project structure and file metadata
 - Implement a simple relevance-based context selection mechanism
 - Create a basic chunking strategy for large files
 
 ### Phase 2: Advanced Features (2-3 months)
+
 - Implement the semantic indexing system with code embeddings
 - Develop the full context management system with working sets
 - Create the multi-level caching system
 
 ### Phase 3: Optimization and Integration (1-2 months)
+
 - Enhance sub-agent coordination for parallel processing
 - Optimize performance with better caching and context management
 - Integrate all components into a cohesive system

docs/SentryIntegration.md

@@ -17,6 +17,7 @@ npm install @sentry/node --save
 ## Configuration
 
 By default, Sentry is:
+
 - Enabled in production environments
 - Disabled in development environments (unless explicitly enabled)
 - Configured to capture 100% of transactions
@@ -56,7 +57,9 @@ Sentry.init({
   tracesSampleRate: 1.0,
   environment: process.env.NODE_ENV || 'development',
   release: `mycoder@${packageVersion}`,
-  enabled: process.env.NODE_ENV !== 'development' || process.env.ENABLE_SENTRY === 'true',
+  enabled:
+    process.env.NODE_ENV !== 'development' ||
+    process.env.ENABLE_SENTRY === 'true',
 });
 
 // Capture errors
@@ -76,6 +79,7 @@ mycoder test-sentry
 ```
 
 This command will:
+
 1. Generate a test error that includes the package version
 2. Report it to Sentry.io
 3. Output the result to the console
@@ -85,6 +89,7 @@ Note: In development environments, you may need to set `ENABLE_SENTRY=true` for
 ## Privacy
 
 Error reports sent to Sentry include:
+
 - Stack traces
 - Error messages
 - Environment information

packages/agent/src/core/types.ts

@@ -29,7 +29,7 @@ export type Tool<TParams = Record<string, any>, TReturn = any> = {
   logReturns?: (returns: TReturn, context: ToolContext) => void;
 
   execute: (params: TParams, context: ToolContext) => Promise<TReturn>;
-  
+
   // Keep JsonSchema7Type for backward compatibility and Vercel AI SDK integration
   parametersJsonSchema?: JsonSchema7Type;
   returnsJsonSchema?: JsonSchema7Type;

packages/agent/src/tools/system/respawn.ts

@@ -11,7 +11,9 @@ const parameterSchema = z.object({
   respawnContext: z.string().describe('The context to keep after respawning'),
 });
 
-const returnSchema = z.string().describe('A message indicating that the respawn has been initiated');
+const returnSchema = z
+  .string()
+  .describe('A message indicating that the respawn has been initiated');
 
 export const respawnTool: Tool = {
   name: 'respawn',

packages/cli/src/commands/tools.ts

@@ -54,7 +54,8 @@ export const command: CommandModule<object, ToolsArgs> = {
         // Parameters section
         console.log('Parameters:');
         // Use parametersJsonSchema if available, otherwise convert from ZodSchema
-        const parametersSchema = (tool as any).parametersJsonSchema || tool.parameters;
+        const parametersSchema =
+          (tool as any).parametersJsonSchema || tool.parameters;
         console.log(
           formatSchema(
             parametersSchema as {