| name | guid-convention-extraction |
| description | Extracts naming conventions, code style guidelines, and structural patterns specific to the project. Analyzes variable naming, function signatures, file organization, and comment styles. |
| model | inherit |
Convention Extraction
Pattern Description
What: Identifies and documents recurring conventions and stylistic choices within a project's codebase.
When: Use this skill to understand a project's established coding standards, when onboarding to a new codebase, or when ensuring consistency during development.
Context: Applicable across various programming languages and frameworks, focusing on observable patterns rather than prescriptive rules.
Project-Specific Conventions
Naming Conventions
- Variables: [e.g.,
camelCasefor JS,snake_casefor Python] - Functions/Methods: [e.g.,
verbNoun()for actions] - Classes/Interfaces: [e.g.,
PascalCase] - Files: [e.g.,
componentName.tsx,utility-function.js]
Code Style Guidelines
- Indentation: [e.g., 2 spaces, 4 spaces, tabs]
- Brace Style: [e.g., K&R, Allman]
- Line Length: [e.g., max 80 characters]
- Comments: [e.g., JSDoc, inline, single-line vs multi-line]
Structural Patterns
- File Organization: [e.g.,
src/components,src/utils,lib/] - Module Structure: [e.g., barrel files, index.ts exports]
- Dependency Injection: [e.g., manual, framework-assisted]
- Error Handling: [e.g.,
try-catch, custom error classes] - Concurrency: [e.g., async/await, threads, goroutines]
- Logging: [e.g., consistent log levels, structured logging]
Common Pitfalls
❌ Inconsistent Application
Problem: Conventions are documented but not consistently followed. Why It Fails: Leads to code readability issues and developer confusion. Better Approach: Automate linting and formatting where possible; conduct regular code reviews.
❌ Over-Prescription
Problem: Too many rigid rules, stifling developer flexibility. Why It Fails: Can make coding tedious and discourage adoption. Better Approach: Focus on key conventions that impact readability and maintainability.
Convention Analysis for Skill Creation
Identifying Skill Opportunities
When extracting conventions, look for patterns that could benefit from dedicated skills:
- Recurring Workflows: Repeated processes that could be codified
- Complex Patterns: Multi-step conventions that require coordination
- Team-Specific Approaches: Unique solutions different from standard practices
- Integration Patterns: How different technologies or components work together
Pattern Frequency Assessment
Consider these factors when evaluating convention relevance for skill creation:
- Usage Frequency: How often do developers encounter this pattern?
- Complexity Level: Is this simple enough to remember or complex enough to benefit from documentation?
- Team Impact: Does this affect multiple team members or workflows?
- Maintenance Needs: Will this pattern evolve or change over time?
Tool Integration Opportunities
Some conventions may suggest specific tool selections:
- Code Formatting: Linters, formatters, pre-commit hooks
- Testing Patterns: Specific testing frameworks or assertion libraries
- Build Processes: Build tools, bundlers, automation scripts
- Documentation: Documentation generators, template systems
Related Resources
Related Skills
- universal-technology-discovery: To understand the technologies driving conventions.
- repository-pattern-recognition: For understanding how conventions relate to repository structure.
- skill-creation: For creating skills based on extracted conventions and patterns.
- tool-selection-guidance: For selecting tools that support established conventions.
Related Agents
- code-reviewer-agent: To enforce extracted conventions.
- frontend-specialist: For frontend-specific convention guidance.
- backend-specialist: For backend and API convention patterns.