codebase-search

name	codebase-search
description	Search and navigate large codebases efficiently. Use when finding specific code patterns, tracing function calls, understanding code structure, or locating bugs. Handles semantic search, grep patterns, AST analysis.
allowed-tools	Read Grep Glob Bash
metadata	{"tags":"codebase-search, grep, code-navigation, pattern-matching","platforms":"Claude, ChatGPT, Gemini"}

Codebase Search

When to use this skill

• Finding specific functions or classes
• Tracing function calls and dependencies
• Understanding code structure and architecture
• Finding usage examples
• Identifying code patterns
• Locating bugs or issues
• Code archaeology (understanding legacy code)
• Impact analysis before changes

Instructions

Step 1: Understand what you're looking for

Feature implementation:

• Where is feature X implemented?
• How does feature Y work?
• What files are involved in feature Z?

Bug location:

• Where is this error coming from?
• What code handles this case?
• Where is this data being modified?

API usage:

• How is this API used?
• Where is this function called?
• What are examples of using this?

Configuration:

• Where are settings defined?
• How is this configured?
• What are the config options?

Step 2: Choose search strategy

Semantic search (for conceptual questions):

Use when: You understand what you're looking for conceptually
Examples:
- "How do we handle user authentication?"
- "Where is email validation implemented?"
- "How do we connect to the database?"

Benefits:
- Finds relevant code by meaning
- Works with unfamiliar codebases
- Good for exploratory searches

Grep (for exact text/patterns):

Use when: You know exact text or patterns
Examples:
- Function names: "def authenticate"
- Class names: "class UserManager"
- Error messages: "Invalid credentials"
- Specific strings: "API_KEY"

Benefits:
- Fast and precise
- Works with regex patterns
- Good for known terms

Glob (for file discovery):

Use when: You need to find files by pattern
Examples:
- "**/*.test.js" (all test files)
- "**/config*.yaml" (config files)
- "src/**/*Controller.py" (controllers)

Benefits:
- Quickly find files by type
- Discover file structure
- Locate related files

Step 3: Search workflow

1. Start broad, then narrow:

Step 1: Semantic search "How does authentication work?"
Result: Points to auth/ directory

Step 2: Grep in auth/ for specific function
Pattern: "def verify_token"
Result: Found in auth/jwt.py

Step 3: Read the file
File: auth/jwt.py
Result: Understand implementation

2. Use directory targeting:

# Start without target (search everywhere)
Query: "Where is user login implemented?"
Target: []

# Refine with specific directory
Query: "Where is login validated?"
Target: ["backend/auth/"]

3. Combine searches:

# Find where feature is implemented
Semantic: "user registration flow"

# Find all files involved
Grep: "def register_user"

# Find test files
Glob: "**/*register*test*.py"

# Understand the implementation
Read: registration.py, test_registration.py

Step 4: Common search patterns

Find function definition:

# Python
grep -n "def function_name" --type py

# JavaScript
grep -n "function functionName" --type js
grep -n "const functionName = " --type js

# TypeScript
grep -n "function functionName" --type ts
grep -n "export const functionName" --type ts

# Go
grep -n "func functionName" --type go

# Java
grep -n "public.*functionName" --type java

Find class definition:

# Python
grep -n "class ClassName" --type py

# JavaScript/TypeScript
grep -n "class ClassName" --type js,ts

# Java
grep -n "public class ClassName" --type java

# C++
grep -n "class ClassName" --type cpp

Find class/function usage:

# Python
grep -n "ClassName(" --type py
grep -n "function_name(" --type py

# JavaScript
grep -n "new ClassName" --type js
grep -n "functionName(" --type js

Find imports/requires:

# Python
grep -n "from.*import.*ModuleName" --type py
grep -n "import.*ModuleName" --type py

# JavaScript
grep -n "import.*from.*module-name" --type js
grep -n "require.*module-name" --type js

# Go
grep -n "import.*package-name" --type go

Find configuration:

# Config files
glob "**/*config*.{json,yaml,yml,toml,ini}"

# Environment variables
grep -n "process\\.env\\." --type js
grep -n "os\\.environ" --type py

# Constants
grep -n "^[A-Z_]+\\s*=" --type py
grep -n "const [A-Z_]+" --type js

Find TODO/FIXME:

grep -n "TODO|FIXME|HACK|XXX" -i

Find error handling:

# Python
grep -n "try:|except|raise" --type py

# JavaScript
grep -n "try|catch|throw" --type js

# Go
grep -n "if err != nil" --type go

Step 5: Advanced techniques

Trace data flow:

1. Find where data is created
   Semantic: "Where is user object created?"

2. Search for variable usage
   Grep: "user\\." with context lines

3. Follow transformations
   Read: Files that modify user

4. Find where it's consumed
   Grep: "user\\." in relevant files

Find all callsites of a function:

1. Find function definition
   Grep: "def process_payment"
   Result: payments/processor.py:45

2. Find all imports of that module
   Grep: "from payments.processor import"
   Result: Multiple files

3. Find all calls to the function
   Grep: "process_payment\\("
   Result: All callsites

4. Read each callsite for context
   Read: Each file with context

Understand a feature end-to-end:

1. Find API endpoint
   Semantic: "Where is user registration endpoint?"
   Result: routes/auth.py

2. Trace to controller
   Read: routes/auth.py
   Find: Calls to AuthController.register

3. Trace to service
   Read: controllers/auth.py
   Find: Calls to UserService.create_user

4. Trace to database
   Read: services/user.py
   Find: Database operations

5. Find tests
   Glob: "**/*auth*test*.py"
   Read: Test files for examples

Find related files:

1. Start with known file
   Example: models/user.py

2. Find imports of this file
   Grep: "from models.user import"

3. Find files this imports
   Read: models/user.py
   Note: Import statements

4. Build dependency graph
   Map: All related files

Impact analysis:

Before changing function X:

1. Find all callsites
   Grep: "function_name\\("

2. Find all tests
   Grep: "test.*function_name" -i

3. Check related functionality
   Semantic: "What depends on X?"

4. Review each usage
   Read: Each file using function

5. Plan changes
   Document: Impact and required updates

Step 6: Search optimization

Use appropriate context:

# See surrounding context
grep -n "pattern" -C 5  # 5 lines before and after
grep -n "pattern" -B 3  # 3 lines before
grep -n "pattern" -A 3  # 3 lines after

Case sensitivity:

# Case insensitive
grep -n "pattern" -i

# Case sensitive (default)
grep -n "Pattern"

File type filtering:

# Specific type
grep -n "pattern" --type py

# Multiple types
grep -n "pattern" --type py,js,ts

# Exclude types
grep -n "pattern" --glob "!*.test.js"

Regex patterns:

# Any character: .
grep -n "function.*Name"

# Start of line: ^
grep -n "^class"

# End of line: $
grep -n "TODO$"

# Optional: ?
grep -n "function_name_?()"

# One or more: +
grep -n "[A-Z_]+"

# Zero or more: *
grep -n "import.*"

# Alternatives: |
grep -n "TODO|FIXME"

# Groups: ()
grep -n "(get|set)_user"

# Escape special chars: \
grep -n "function\(\)"

Best practices

1. Start with semantic search: For unfamiliar code or conceptual questions
2. Use grep for precision: When you know exact terms
3. Combine multiple searches: Build understanding incrementally
4. Read surrounding context: Don't just look at matching lines
5. Check file history: Use git blame for context
6. Document findings: Note important discoveries
7. Verify assumptions: Read actual code, don't assume
8. Use directory targeting: Narrow scope when possible
9. Follow the data: Trace data flow through the system
10. Check tests: Tests often show usage examples

Common search scenarios

Scenario 1: Understanding a bug

1. Find error message
   Grep: "exact error message"

2. Find where it's thrown
   Read: File with error

3. Find what triggers it
   Semantic: "What causes X error?"

4. Find related code
   Grep: Related function names

5. Check tests
   Glob: "**/*test*.py"
   Look: For related test cases

Scenario 2: Learning a new codebase

1. Find entry point
   Semantic: "Where does the application start?"
   Common files: main.py, index.js, app.py

2. Find main routes/endpoints
   Grep: "route|endpoint|@app\\."

3. Find data models
   Semantic: "Where are data models defined?"
   Common: models/, entities/

4. Find configuration
   Glob: "**/*config*"

5. Read README and docs
   Read: README.md, docs/

Scenario 3: Refactoring preparation

1. Find all usages
   Grep: "function_to_change"

2. Find tests
   Grep: "test.*function_to_change"

3. Find dependencies
   Semantic: "What does X depend on?"

4. Check imports
   Grep: "from.*import.*X"

5. Document scope
   List: All affected files

Scenario 4: Adding a feature

1. Find similar features
   Semantic: "How is similar feature implemented?"

2. Find where to add code
   Semantic: "Where should new feature go?"

3. Check patterns
   Read: Similar implementations

4. Find tests to emulate
   Glob: Test files for similar features

5. Check documentation
   Grep: "TODO.*new feature" -i

Tools integration

Git integration:

# Who changed this line?
git blame filename

# History of a file
git log -p filename

# Find when function was added
git log -S "function_name" --source --all

# Find commits mentioning X
git log --grep="feature name"

IDE integration:

• Use "Go to Definition" for quick navigation
• Use "Find References" for usage
• Use "Find in Files" for broad search
• Use symbol search for classes/functions

Documentation:

• Check inline comments
• Look for docstrings
• Read README files
• Check architecture docs

Troubleshooting

No results found:

• Check spelling and case sensitivity
• Try semantic search instead of grep
• Broaden search scope (remove directory target)
• Try different search terms
• Check if files are in .gitignore

Too many results:

• Add directory targeting
• Use more specific patterns
• Filter by file type
• Use exact phrases (quotes)

Wrong results:

• Be more specific in query
• Use grep instead of semantic for exact terms
• Add context to semantic queries
• Check file types

References

• Ripgrep User Guide
• Regular Expressions Tutorial
• Git Blame Guide

Examples

Example 1: Basic usage

Example 2: Advanced usage

Workflow Context

Unity3D C# 스크립트와 에셋에서 패턴을 검색합니다.

• 트리거: JEO Workflow 5 (성능 최적화) PLAN 단계
• 연동: codebase-search + unity-mcp: find_in_file → 문제 코드 탐색 → performance-optimization