Best use case
solution-explainer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate clear explanations of algorithm solutions
Teams using solution-explainer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/solution-explainer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How solution-explainer Compares
| Feature / Agent | solution-explainer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate clear explanations of algorithm solutions
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Solution Explainer Skill
## Purpose
Generate clear, educational explanations of algorithm solutions suitable for interviews, learning, and documentation.
## Capabilities
- Step-by-step solution walkthrough
- Time/space complexity explanation
- Alternative approach comparison
- Common mistake highlights
- Visual aids generation
- Interview-style explanation formatting
## Target Processes
- interview-problem-explanation
- leetcode-problem-solving
- mock-coding-interview
- algorithm-implementation
## Explanation Framework
1. **Problem Understanding**: Restate the problem clearly
2. **Approach Overview**: High-level strategy
3. **Algorithm Details**: Step-by-step breakdown
4. **Complexity Analysis**: Time and space with justification
5. **Code Walkthrough**: Annotated implementation
6. **Edge Cases**: Special scenarios handled
7. **Alternatives**: Other valid approaches
## Input Schema
```json
{
"type": "object",
"properties": {
"problem": { "type": "string" },
"solution": { "type": "string" },
"language": { "type": "string" },
"depth": {
"type": "string",
"enum": ["brief", "standard", "detailed"]
},
"includeVisuals": { "type": "boolean", "default": false },
"interviewStyle": { "type": "boolean", "default": false }
},
"required": ["problem", "solution"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"explanation": { "type": "string" },
"complexity": { "type": "object" },
"commonMistakes": { "type": "array" },
"alternatives": { "type": "array" },
"visuals": { "type": "array" }
},
"required": ["success", "explanation"]
}
```Related Skills
shap-explainer
SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.
lime-explainer
LIME-based local explanation skill for individual predictions across tabular, text, and image data.
alibi-explainer
Alibi explainability skill for counterfactual explanations, anchors, and trust scores.
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