Awesome-omni-skill Jwt Authentication
This skill provides comprehensive patterns for implementing JWT (JSON
install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/backend/jwt-authentication" ~/.claude/skills/diegosouzapw-awesome-omni-skill-jwt-authentication && rm -rf "$T"
manifest:
skills/backend/jwt-authentication/SKILL.mdsafety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
- references .env files
- references API keys
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content
Jwt Authentication
Skill Profile
(Select at least one profile to enable specific modules)
- DevOps
- Backend
- Frontend
- AI-RAG
- Security Critical
Overview
This skill provides comprehensive patterns for implementing JWT (JSON Web Token) authentication in web applications. It covers token generation, verification, access/refresh token strategy, secure storage in httpOnly cookies, token rotation, revocation, and production-ready security practices.
Why This Matters
- Stateless Authentication: JWTs enable stateless authentication, reducing database load
- Scalability: No server-side session storage required, ideal for microservices
- Cross-Origin Support: Works seamlessly with CORS for SPA and mobile apps
- Security: Proper implementation with httpOnly cookies and refresh tokens provides robust security
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- Environment variables:
,JWT_ACCESS_SECRET
,JWT_REFRESH_SECRET
,JWT_ISSUERJWT_AUDIENCE - Request headers:
or cookiesAuthorization: Bearer <token> - Configuration: token expiry times, algorithm selection
- Environment variables:
- Entry Conditions:
- JWT secrets configured in environment
- User database with credentials
- Redis or in-memory store for token revocation (optional)
- Outputs:
- Generated access and refresh tokens
- Decoded token payload
- Authentication status and user information
- Artifacts Required (Deliverables):
- JWT generation and verification utilities
- Authentication middleware
- Token refresh endpoint
- Token revocation system
- Acceptance Evidence:
- Test coverage report (>80%)
- Security audit results
- Token flow verification
- Success Criteria:
- Tokens are generated with proper claims and expiration
- Access tokens expire within configured time
- Refresh tokens can obtain new access tokens
- Revoked tokens cannot be used
Skill Composition
- Depends on: hashing-algorithms, database-patterns
- Compatible with: api-key-management, oauth2-implementation, rbac-patterns
- Conflicts with: session-management (different authentication strategies)
- Related Skills: password-hashing, csrf-protection
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
# Example implementation following best practices def example_function(): # Your implementation here pass
Assumptions / Constraints / Non-goals
- Assumptions:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
- Constraints:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
- Non-goals:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training
Compatibility & Prerequisites
- Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
- Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
- Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
- Environment Setup:
keys:.env.example
,API_KEY
(no values)DATABASE_URL
Test Scenario Matrix (QA Strategy)
| Type | Focus Area | Required Scenarios / Mocks |
|---|---|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
Technical Guardrails & Security Threat Model
1. Security & Privacy (Threat Model)
- Top Threats: Injection attacks, authentication bypass, data exposure
- Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
- Authorization: Validate user permissions before state changes
2. Performance & Resources
- Execution Efficiency: Consider time complexity for algorithms
- Memory Management: Use streams/pagination for large data
- Resource Cleanup: Close DB connections/file handlers in finally blocks
3. Architecture & Scalability
- Design Pattern: Follow SOLID principles, use Dependency Injection
- Modularity: Decouple logic from UI/Frameworks
4. Observability & Reliability
- Logging Standards: Structured JSON, include trace IDs
request_id - Metrics: Track
,error_rate
,latencyqueue_depth - Error Handling: Standardized error codes, no bare except
- Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%
Agent Directives & Error Recovery
(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)
- Thinking Process: Analyze root cause before fixing. Do not brute-force.
- Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- Self-Review: Check against Guardrails & Anti-patterns before finalizing.
- Output Constraints: Output ONLY the modified code block. Do not explain unless asked.
Definition of Done (DoD) Checklist
- Tests passed + coverage met
- Lint/Typecheck passed
- Logging/Metrics/Trace implemented
- Security checks passed
- Documentation/Changelog updated
- Accessibility/Performance requirements met (if frontend)
Anti-patterns / Pitfalls
- ⛔ Don't: Log PII, catch-all exception, N+1 queries
- ⚠️ Watch out for: Common symptoms and quick fixes
- 💡 Instead: Use proper error handling, pagination, and logging
Reference Links & Examples
- Internal documentation and examples
- Official documentation and best practices
- Community resources and discussions
Versioning & Changelog
- Version: 1.0.0
- Changelog:
- 2026-02-22: Initial version with complete template structure