A Socratic teaching agent that guides developers through algorithm challenges without giving away answers immediately.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install algorithm-mentor
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install algorithm-mentor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Algorithm Mentor?
Algorithm Mentor is a specialized tool within the Openclaw Skills ecosystem designed to transform how developers learn data structures and algorithms. Instead of providing instant solutions, it employs a Socratic teaching method that forces the learner to articulate problem logic, identify constraints, and evolve code from a brute-force approach to an optimized version. It is specifically tailored for LeetCode preparation and deep conceptual understanding.
The skill acts as a persistent coach that monitors your coding progress in real-time. By adhering to strict educational principles, it ensures that users actually internalize algorithmic patterns rather than just copying and pasting. After each successful challenge, it automates the tedious parts of documentation, creating a structured knowledge base of your learning journey.
Algorithm Mentor Use Cases
- Preparing for technical interviews by practicing LeetCode problems with a guided mentor.
- Learning TypeScript or JavaScript through the lens of algorithmic problem solving.
- Moving away from solution-dependence and developing independent problem-solving intuition.
- Building a personal, documented library of algorithm solutions and complexity analyses.
How Algorithm Mentor Works
- The agent asks the user to explain the problem in their own words and identify input/output formats.
- It guides the user to conceptualize a brute-force approach and calculate its time/space complexity.
- The user implements the initial solution, and the agent reviews the code for correctness only after the user signals completion.
- It suggests optimization directions (e.g., Hash Maps, Two Pointers) and explains necessary new data structures.
- The user implements the optimized version with step-by-step guidance from the mentor.
- The agent generates a comprehensive README.md and solution file, updating the overall progress tracker and review schedule.
Algorithm Mentor Setup
To get started with this mentor in your environment, ensure you have the Openclaw Skills framework initialized and add the algorithm-mentor configuration to your agent profile.
# Example directory initialization for algorithm training
mkdir algorithm-training
cd algorithm-training
touch progress.md
Configure your AI agent to reference the Algorithm Mentor instructions when opening any file within the training directory.
Algorithm Mentor Data Schema & Taxonomy
The skill maintains a highly organized directory structure to ensure long-term retention and easy review.
| File/Folder | Purpose |
|---|---|
solutions/ |
Root directory for all completed algorithm challenges. |
[id]-[name]/ |
Individual folders for each problem (e.g., 01-two-sum). |
solution.ts |
The final optimized TypeScript implementation. |
README.md |
Comprehensive solution documentation including complexity and logic. |
progress.md |
A high-level tracker for all attempted and mastered Openclaw Skills challenges. |
Algorithm Mentor Advanced Features
- Socratic Questioning Engine: Prevents the AI from leaking answers before the user attempts the logic.
- Automated Documentation: Generates full Markdown reports including Ebbinghaus forgetting curve review schedules.
- Integrated TypeScript Teaching: Provides contextual syntax explanations and compares them with JavaScript for better mental mapping.
- Multi-Step Verification: Reads and analyzes user-written code files to provide specific feedback on line-level logic errors.
SKILL.md
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