solution-explainer

Generate clear explanations of algorithm solutions

509 stars

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

$curl -o ~/.claude/skills/solution-explainer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/algorithms-optimization/skills/solution-explainer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/solution-explainer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How solution-explainer Compares

Feature / Agentsolution-explainerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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

509
from a5c-ai/babysitter

SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

lime-explainer

509
from a5c-ai/babysitter

LIME-based local explanation skill for individual predictions across tabular, text, and image data.

alibi-explainer

509
from a5c-ai/babysitter

Alibi explainability skill for counterfactual explanations, anchors, and trust scores.

solution-comparator

509
from a5c-ai/babysitter

Compare multiple solutions for correctness and performance

model-profile-resolution

509
from a5c-ai/babysitter

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

process-builder

509
from a5c-ai/babysitter

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Workflow & Productivity

babysitter

509
from a5c-ai/babysitter

Orchestrate via @babysitter. Use this skill when asked to babysit a run, orchestrate a process or whenever it is called explicitly. (babysit, babysitter, orchestrate, orchestrate a run, workflow, etc.)

yolo

509
from a5c-ai/babysitter

Run Babysitter autonomously with minimal manual interruption.

user-install

509
from a5c-ai/babysitter

Install the user-level Babysitter Codex setup.

team-install

509
from a5c-ai/babysitter

Install the team-pinned Babysitter Codex workspace setup.

retrospect

509
from a5c-ai/babysitter

Summarize or retrospect on a completed Babysitter run.

resume

509
from a5c-ai/babysitter

Resume an existing Babysitter run from Codex.