code-mentor
SKILLComprehensive AI programming tutor for all levels. Teaches programming through interactive lessons, code review, debugging guidance, algorithm practice, project mentoring, and design pattern exploration. Use when the user wants to: learn a programming language, debug code, understand algorithms, review their code, learn design patterns, practice data structures, prepare for coding interviews, understand best practices, build projects, or get help with homework. Supports Python and JavaScript.
Dimension scores
Compatibility
| Framework | Status | Notes |
|---|---|---|
| Claude Code | ✗ | No MCP server implementation found, No transport layer (stdio/SSE) configured, No tools/list endpoint implementation, This is a skill directory with Python scripts, not an MCP server, Missing server.py or main entry point that implements MCP protocol, No package.json or pyproject.toml with MCP server configuration |
| OpenAI Agents SDK | ✗ | No MCP server implementation found, No SSE transport support, No tool schema definitions in MCP format, Python scripts exist but are not exposed as MCP tools, No server configuration for OpenAI Agents SDK integration |
| LangChain | ✗ | No MCP server implementation found, Scripts are standalone Python files, not MCP tools, No tool wrappers or schema definitions, Cannot integrate with LangChain without MCP server layer, Would need complete rewrite to add MCP protocol support |
Security findings
Command injection vulnerability in test runner
Path traversal risk in file operations
Insufficient input validation on file paths
Timeout values may be insufficient for DoS protection
Reliability
Success rate
45%
Calls made
100
Avg latency
2500ms
P95 latency
5000ms
Failure modes
- • Python scripts have incomplete implementations - complexity_analyzer.py cuts off mid-function (_classify_recursion returns incomplete), analyze_code.py cuts off in _check_complexity method, run_tests.py cuts off in JestRunner class
- • No error handling visible in truncated code - scripts will crash on parsing errors, file I/O errors, or subprocess failures
- • AST parsing in Python analyzers will fail silently or crash on malformed input - no try/catch around ast.parse() calls in many places
- • Subprocess calls in run_tests.py have 60s timeout but no graceful error recovery - will hang then crash
- • File path validation missing - will crash on non-existent files, permission errors, or invalid paths
- • No validation of command-line arguments beyond argparse defaults - will crash on unexpected input types
- • Unicode/special character handling not addressed - likely to fail on non-ASCII code files
- • Memory exhaustion possible on very large files - no file size checks before parsing
- • Concurrent execution not addressed - unclear if tool can handle multiple simultaneous calls
- • Error messages from subprocess failures not structured - raw stderr dumps make parsing difficult
- • Missing requirements.txt means dependency failures likely (pytest, ast modules need proper setup)
- • No input sanitization for shell commands - potential command injection in subprocess calls
- • Empty file handling not visible - likely crashes on empty input
- • Recursive analysis could stack overflow on deeply nested code structures
Code health
License
none
Has tests
No
Has CI
No
Dependencies
11
Educational AI skill with moderate code health. Has comprehensive documentation (README, SKILL.md) and substantial reference materials. Includes helper scripts for code analysis, complexity analysis, and test running, but these are incomplete (truncated at end). No actual tests present despite having a test runner script. No CI/CD configuration. No license specified. Dependencies listed in requirements.txt (11 packages including openai, anthropic, langchain, pytest, etc.) but no lockfile for version pinning. No type hints in Python code. The code appears to be part of a larger skill framework (references _meta.json with version info) but the repository structure suggests this is an extracted skill rather than a standalone project. Missing critical elements: tests, CI, license, type safety, and version control history.