smithery.ai

markdown-doc-writer

Technical documentation writer specializing in creating clear, well-structured markdown documents for algorithms, system design, interview preparation, and code documentation. Use when writing README files, algorithm explanations, system design docs, or technical guides.

First seen Mar 18, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsRead, Glob, Grep, Edit, Write

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 9,534 B
  • docs SUMMARY.md 298 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Markdown Documentation Writer

When to use this Skill

Use this Skill when:

  • Writing README files
  • Creating algorithm explanations
  • Documenting system design solutions
  • Writing interview preparation guides
  • Creating cheat sheets and reference materials
  • Adding code documentation

Documentation Standards

1. Structure Guidelines

Every document should have:

  1. Clear title (H1)
  2. Brief description
  3. Table of contents (for long docs)
  4. Well-organized sections (H2, H3)
  5. Code examples
  6. Complexity analysis (for algorithms)
  7. References/links

Standard Template:

# Title

Brief description of what this document covers.

## Table of Contents
- [Section 1](#section-1)
- [Section 2](#section-2)

## Section 1

Content...

## Section 2

Content...

## References
- [Link 1](url)

2. Algorithm Documentation Format

Use this structure for algorithm problems:

# Problem Number: Problem Title

**Difficulty**: Easy/Medium/Hard
**Topics**: Array, Two Pointers, Hash Table
**Companies**: Google, Amazon, Meta

## Problem Statement

[Clear description of the problem]

**Example 1:**

Input: [example input] Output: [example output] Explanation: [why this is the output]


**Constraints:**
- [List constraints]

## Approach

### Intuition

[Explain the key insight in simple terms]

### Algorithm

1. [Step 1]
2. [Step 2]
3. [Step 3]

### Complexity Analysis

- **Time Complexity**: O(n) - [Explain why]
- **Space Complexity**: O(1) - [Explain why]

## Solution

### Java

class Solution { public ReturnType method(InputType param) { // Implementation } }


### Python

class Solution: def method(self, param: InputType) -> ReturnType: # Implementation


## Alternative Approaches

### Approach 2: [Name]

[Description]

**Complexity**: O(?) time, O(?) space

### Comparison

| Approach | Time | Space | Notes |
|----------|------|-------|-------|
| Approach 1 | O(n) | O(1) | Optimal |
| Approach 2 | O(n²) | O(1) | Simpler code |

## Key Takeaways

- [Learning point 1]
- [Learning point 2]

## Related Problems

- [Problem A](link)
- [Problem B](link)

3. System Design Documentation Format

Follow the template structure:

# System Name: Brief Description

## 1. Requirements

### Functional Requirements
- Feature 1: [Description]
- Feature 2: [Description]

### Non-Functional Requirements
- **Scale**: X million DAU, Y QPS
- **Performance**: p99 latency < Z ms
- **Availability**: 99.9% uptime

## 2. Capacity Estimation

### Traffic
- Daily Active Users: 100M
- Requests per user: 10/day
- QPS: 100M * 10 / 86400 ≈ 11,574

### Storage
- Per user data: 1KB
- Total: 100M * 1KB = 100GB

### Bandwidth
- Average request size: 10KB
- Bandwidth: 11,574 QPS * 10KB ≈ 115MB/s

## 3. API Design

POST /api/resource GET /api/resource/{id} PUT /api/resource/{id} DELETE /api/resource/{id}


## 4. High-Level Architecture

[Client] → [Load Balancer] → [App Servers] ↓ [Cache] [DB]


## 5. Database Design

### Schema

CREATE TABLE users ( id BIGSERIAL PRIMARY KEY, username VARCHAR(50) UNIQUE NOT NULL, created_at TIMESTAMP DEFAULT NOW() );


### Indexing Strategy
- Index on `username` for fast lookup
- Index on `created_at` for sorting

## 6. Detailed Component Design

### Component 1: [Name]

**Responsibility**: [What it does]

**Technology**: [Specific tech choice]

**Scaling**: [How to scale]

## 7. Scalability & Reliability

### Caching Strategy
- [Cache what, where, why]

### Sharding Strategy
- [How to partition data]

### Replication
- [Master-slave setup]

## 8. Trade-offs & Alternatives

| Decision | Chosen | Alternative | Rationale |
|----------|--------|-------------|-----------|
| Database | PostgreSQL | MongoDB | Need ACID |

## 9. Monitoring & Alerting

- Metrics to track: [List]
- Alerts: [When to trigger]

## 10. Security Considerations

- Authentication: [Method]
- Authorization: [Method]
- Data encryption: [At rest, in transit]

## References
- [External resources]

4. Code Formatting

Inline code: Use backticks for variable names, commands, short code

Code blocks: Use fenced code blocks with language

public class Example { // Code here }

Supported languages:

  • java, python, javascript, sql, bash
  • json, yaml, xml, markdown
  • c, cpp, scala, go

5. Visual Elements

Tables:

| Column 1 | Column 2 | Column 3 |
|----------|----------|----------|
| Data 1   | Data 2   | Data 3   |

Lists:

Unordered:
- Item 1
  - Nested item
- Item 2

Ordered:
1. First step
2. Second step
3. Third step

Emphasis:

*italic* or _italic_
**bold** or __bold__
***bold italic***
`code`
~~strikethrough~~

Links:

[Link text](URL)
[Link with title](URL "Title")
[Reference link][ref]

[ref]: URL "Title"

Images:

![Alt text](image-url)
![Alt text](image-url "Title")

6. Complexity Analysis Documentation

Standard format:

## Complexity Analysis

### Time Complexity: O(n log n)
- Sorting takes O(n log n)
- Single pass takes O(n)
- Overall: O(n log n)

### Space Complexity: O(n)
- Hash map stores n elements: O(n)
- Result array: O(n)
- Overall: O(n)

### Optimization Notes
- Can reduce space to O(1) by modifying input in-place
- Trade-off: Destroys original input

Complexity cheat sheet to reference:

| Notation | Name | Example |
|----------|------|---------|
| O(1) | Constant | Array access |
| O(log n) | Logarithmic | Binary search |
| O(n) | Linear | Array scan |
| O(n log n) | Linearithmic | Merge sort |
| O(n²) | Quadratic | Nested loops |
| O(2ⁿ) | Exponential | Recursive Fibonacci |
| O(n!) | Factorial | Permutations |

7. Writing Style Guidelines

Be Clear:

  • Use simple language
  • Avoid jargon unless necessary
  • Define technical terms on first use
  • Use active voice

Be Concise:

  • Remove unnecessary words
  • Use bullet points for lists
  • Break long paragraphs
  • One idea per paragraph

Be Consistent:

  • Use same terminology throughout
  • Follow naming conventions
  • Maintain consistent formatting
  • Use templates for similar documents

Examples:

❌ Bad:

The algorithm basically works by iterating through the array and
then it checks if the element is what we're looking for.

✅ Good:

The algorithm iterates through the array to find the target element.

8. Interview Preparation Docs

Pattern template:

# Pattern Name

## When to Use
- [Characteristic 1]
- [Characteristic 2]

## Template Code

def pattern_template(arr): # Step 1: Setup

# Step 2: Main logic

# Step 3: Return result


## Example Problems
1. [Problem 1](link) - Easy
2. [Problem 2](link) - Medium
3. [Problem 3](link) - Hard

## Key Points
- [Tip 1]
- [Tip 2]

9. Cheat Sheet Format

Keep it scannable:

# Topic Cheat Sheet

## Quick Reference

| Operation | Syntax | Complexity |
|-----------|--------|------------|
| Access | arr[i] | O(1) |
| Search | arr.indexOf(x) | O(n) |

## Common Patterns

### Pattern 1

// Code snippet

**Use when**: [Description]

### Pattern 2

// Code snippet

**Use when**: [Description]

## Gotchas
- ⚠️ [Common mistake 1]
- ⚠️ [Common mistake 2]

10. Document Maintenance

Version control:

  • Use git to track changes
  • Write meaningful commit messages
  • Keep documents up to date with code

Cross-references:

  • Link related documents
  • Reference source code files
  • Point to external resources

Validation:

  • Check all links work
  • Verify code examples compile
  • Test complexity analysis accuracy

Project-Specific Guidelines

For CS_basics repository:

  1. Algorithm problems: Use detailed format with multiple languages
  2. System design: Follow 00_template.md structure
  3. Cheat sheets: Keep in doc/ directory
  4. Cross-language: Maintain consistency across Java/Python implementations
  5. Interview prep: Focus on pattern recognition and problem-solving approach

File organization:

doc/
├── algorithm_patterns/
│   ├── two_pointers.md
│   └── sliding_window.md
├── data_structure/
│   └── complexity_chart.md
└── system_design/
    └── case_studies/

Quality Checklist

Before finalizing documentation:

  • Clear title and description
  • Proper heading hierarchy
  • Code examples tested and working
  • Complexity analysis included
  • Consistent formatting
  • No broken links
  • Spell-checked
  • Follows project conventions
  • Related content linked

Tools & References

Markdown validation:

  • Check syntax with markdown linters
  • Preview before committing
  • Use consistent line breaks

Useful symbols:

  • ✅ Checkmark for correct approach
  • ❌ X for incorrect approach
  • ⚠️ Warning for gotchas
  • 💡 Bulb for tips
  • 📝 Note for important points