What is the primary function of the code-mentor skill?

Turns the OpenClaw agent into an expert code mentor that can explain coding concepts, review code, suggest improvements, generate code snippets, and test code using tools.

What are the key behaviors of the code-mentor persona?

Patient, encouraging, Socratic questioning, adapts to user skill level, breaks down complex topics, provides working examples, emphasizes best practices.

How does the skill handle code review?

Identifies bugs, inefficiencies, and style issues; suggests fixes with explanations; uses code_execution to verify.

What types of user queries does the skill handle?

Beginner questions, debugging help, code reviews, concept explanations, code generation, best practices, interview prep.

Does the skill support multiple programming languages?

Primarily Python via code_execution tool, but can explain concepts in any language.

OpenClaw skill

code-mentor

An OpenClaw skill that equips agents to act as code mentors. It provides tools including explain_concept for breaking down programming ideas, generate_code for creating examples in specified languages, debug_code for identifying and fixing issues, and execute_code for running snippets in a sandbox. These enable structured mentoring sessions on coding topics.

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How this skill works

Teaches coding concepts, reviews code, debugs issues, and provides best practices
Triggers on tasks matching 'code|programming|bug|debug|review|fix|optimize|explain|refactor'
Greets the user and confirms the programming language if not specified
Asks for the code snippet or problem description if not provided
Analyzes the code by checking syntax, logic, efficiency, style
Explains issues found with line numbers and reasons
Provides corrected or improved code
Suggests best practices and alternatives
Offers to explain further or handle follow-ups
Supports languages Python, JavaScript, Java, C++, Rust, Go
Does not execute code
Limits reviews to code no longer than 500 lines
Uses persona of patient coding tutor with Socratic method, encouraging

When to use it

When a user requests explanations of programming concepts
When a user submits code for review and critique
When a user needs help debugging code issues
When a user seeks guidance on coding projects or best practices

Best practices

Set OPENAI_API_KEY environment variable
Override MODEL for non-default LLM usage
Use low temperature (0.1 default) for consistent code advice
Provide clear coding queries prefixed with skill trigger
Enable analytics for usage tracking

Example use cases

Explaining programming concepts: Uses the explain_programming_concept tool to provide simple explanations of concepts specified by the user.
Reviewing user code: Uses the review_code tool to analyze provided code in a given language for correctness, style, and improvements.
Generating code snippets: Uses the generate_code tool to create code examples for specific tasks in a chosen programming language.
Debugging code: Uses the debug_code tool to identify bugs in provided code, optionally using an error message, and suggest fixes.

FAQs

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