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- Introduced a comprehensive design document for a Code Semantic Graph aimed at enhancing static analysis capabilities. - Defined the architecture, core components, and implementation steps for analyzing function calls, data flow, and dependencies. - Included detailed specifications for nodes and edges in the graph, along with database schema for storage. - Outlined phases for implementation, technical challenges, success metrics, and application scenarios.
48 lines
1.5 KiB
Markdown
48 lines
1.5 KiB
Markdown
# Context Requirements
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Before implementation, always:
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- Identify 3+ existing similar patterns before implementation
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- Map dependencies and integration points
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- Understand testing framework and coding conventions
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## Context Gathering
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### Use Exa
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- Researching external APIs, libraries, frameworks
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- Need recent documentation beyond knowledge cutoff
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- Looking for implementation examples in public repos
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- User mentions specific library/framework names
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- Questions about "best practices" or "how does X work"
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### Use read_file (MCP)
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- Reading multiple related files at once
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- Directory traversal with pattern matching
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- Searching file content with regex
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- Need to limit depth/file count for large directories
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- Batch operations on multiple files
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- Pattern-based filtering (glob + content regex)
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### Use codex_lens
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- Large codebase (>500 files) requiring repeated searches
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- Need semantic understanding of code relationships
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- Working across multiple sessions
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- Symbol-level navigation needed
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- Finding all implementations of interface/class
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- Tracking function calls across codebase
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### Use smart_search
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- Unknown file locations
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- Concept/semantic search ("authentication logic", "payment processing")
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- Medium-sized codebase (100-500 files)
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- One-time or infrequent searches
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- Natural language queries about code structure
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**Mode Selection**:
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- `auto`: Let tool decide (default)
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- `exact`: Known exact pattern
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- `fuzzy`: Typo-tolerant search
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- `semantic`: Concept-based search
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- `graph`: Dependency analysis
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