codebase-search▌
supercent-io/skills-template · updated Apr 30, 2026
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Search and navigate large codebases with semantic search, grep patterns, and file discovery.
- ›Supports three search modes: semantic search for conceptual queries, grep for exact text and regex patterns, and glob for file discovery by type or naming convention
- ›Includes workflow guidance for common scenarios like tracing function calls, understanding feature implementations, locating bugs, and performing impact analysis
- ›Provides language-specific patterns for Python, JavaScript, TypeScr
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_nameHow to use codebase-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add codebase-search
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches codebase-search from GitHub repository supercent-io/skills-template and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate codebase-search. Access the skill through slash commands (e.g., /codebase-search) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★26 reviews- ★★★★★Chen Nasser· Sep 21, 2024
Useful defaults in codebase-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Sep 17, 2024
Keeps context tight: codebase-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Arya Menon· Sep 9, 2024
We added codebase-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kofi Malhotra· Aug 28, 2024
Solid pick for teams standardizing on skills: codebase-search is focused, and the summary matches what you get after install.
- ★★★★★Naina Flores· Aug 12, 2024
codebase-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ganesh Mohane· Aug 8, 2024
codebase-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakshi Patil· Jul 27, 2024
Solid pick for teams standardizing on skills: codebase-search is focused, and the summary matches what you get after install.
- ★★★★★Isabella Anderson· Jul 19, 2024
codebase-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Amelia Taylor· Jul 11, 2024
Registry listing for codebase-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amelia Abebe· Jul 3, 2024
codebase-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
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