Md to json parser

Skill kay-ou/ClaudeSkills/.claude/skills/md-to-json-parser

A Claude Skills collection providing ptrade‑dev strategy guardrails with lifecycle checks, API‑aware validation, and error‑pattern detection, plus structured parsing and webpage‑to‑Markdown tools—enabling LLMs to reliably analyze, generate, and debug PTrade/SimTradeLab strategies

Install

npx -y skills add kay-ou/ClaudeSkills --skill md-to-json-parser

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SKILL.md

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name: md-to-json-parser description: Structure Markdown documents into JSON, supporting headings, paragraphs, tables, code blocks, etc. This skill should be used when users need to parse markdown files, convert markdown to structured data, extract content from markdown documents, analyze markdown structure, or process .md files programmatically. Keywords: markdown解析, markdown转JSON, 提取markdown结构, 分析markdown文档, parse markdown, convert md to json, extract markdown content, markdown structure analysis, process markdown files, 处理markdown文件 inputs:

md_file_path outputs:
structured_json instructions: |

Markdown to JSON Parser

This skill parses Markdown documents and converts them into structured JSON format, making it easier for Claude to understand and work with document content programmatically.

When to Use This Skill

Use this skill when you need to:

Extract structured data from Markdown files
Convert documentation into JSON for processing
Analyze document structure and content
Process Markdown files programmatically

Processing Steps

Step 1: Load and Validate Markdown File

Read the Markdown file from the provided path
Validate file exists and is readable
Handle encoding issues (default to UTF-8)

Step 2: Parse Document Structure

Extract headings (H1-H6) with their hierarchy
Identify paragraphs and text content
Locate tables and convert to structured format
Find code blocks with their language annotations
Detect lists (ordered and unordered)
Identify links, images, and other inline elements

Step 3: Convert Tables to Arrays

Parse table headers as column names
Convert each row to a JSON object
Preserve cell content with proper escaping
Handle merged cells appropriately

Step 4: Preserve Code Blocks

Maintain original formatting and indentation
Preserve language annotations
Keep special characters and syntax intact
Handle multi-line code blocks correctly

Step 5: Generate Structured JSON Output

Create hierarchical structure reflecting document organization
Include metadata like word count, heading count, etc.
Preserve relationships between elements
Ensure JSON is valid and well-formed

Output Format

The structured JSON includes:

{
  "metadata": {
    "title": "Document Title",
    "word_count": 1500,
    "heading_count": 8,
    "table_count": 3,
    "code_block_count": 5
  },
  "structure": {
    "headings": [
      {"level": 1, "text": "Main Title", "id": "main-title"}
    ],
    "paragraphs": [
      {"text": "Content...", "word_count": 120}
    ],
    "tables": [
      {
        "headers": ["Column1", "Column2"],
        "rows": [
          {"Column1": "data1", "Column2": "data2"}
        ]
      }
    ],
    "code_blocks": [
      {
        "language": "python",
        "content": "def example():\n    pass"
      }
    ]
  }
}

Error Handling

Handle missing files gracefully
Manage encoding issues
Deal with malformed Markdown
Report parsing errors clearly
Validate JSON output format