recursive directory traversal with file filtering, source code to markdown conversion with syntax preservation, github repository cloning and batch conversion, multi-format output generation with customizable structure, binary file detection and intelligent skipping, file size and line count metadata extraction, comment and docstring preservation with language-specific parsing, configuration file support for batch processing, progress reporting and logging with detailed conversion metrics, language-specific code block formatting with syntax hints

auto-md

ModelFree

Convert Files / Folders / GitHub Repos Into AI / LLM-ready Files

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

recursive directory traversal with file filtering

Medium confidence

Walks local filesystem hierarchies using Python's os.walk() or pathlib, applying configurable ignore patterns (gitignore-style rules, binary file detection, size thresholds) to selectively include/exclude files before processing. Maintains directory structure metadata for context preservation during conversion.

Solves for

I need to convert an entire project folder into LLM-ready format without manually selecting filesI want to exclude node_modules, .git, and other irrelevant directories automaticallyI need to preserve the folder structure context when feeding code to an LLM

Best for

developers preparing local codebases for LLM analysis or fine-tuning

teams automating documentation generation from source trees

researchers building datasets from open-source projects

Requires

Python 3.7+

Read permissions on target filesystem

Sufficient disk space for output markdown files

Limitations

No built-in support for symlinks or circular references — may cause infinite loops on recursive symlink structures

Performance degrades on very large directories (100k+ files) without caching

Ignore patterns must be manually configured; no automatic detection of project-specific exclusion rules

What makes it unique

Implements gitignore-compatible filtering rules during traversal rather than post-processing, reducing memory overhead and enabling early termination of excluded branches

vs alternatives

More efficient than generic file-listing tools because it filters during traversal rather than collecting all files first, critical for large monorepos

source code to markdown conversion with syntax preservation

Medium confidence

Parses source code files across 20+ languages (Python, JavaScript, Java, C++, etc.) and wraps them in markdown code blocks with language-specific syntax highlighting hints. Extracts file metadata (path, size, line count) and embeds it as frontmatter or comments to preserve context for LLM consumption.

Solves for

I want to feed my entire codebase to Claude or GPT as markdown for analysis without losing syntax highlighting contextI need to convert code snippets into a format that preserves language information for LLM understandingI'm building a dataset of code examples and need consistent markdown formatting across multiple languages

Best for

developers preparing code for LLM-based code review or refactoring

AI researchers building code understanding datasets

teams documenting APIs by converting source code to markdown

Requires

Python 3.7+

Source code files in supported languages

Write permissions for output directory

Limitations

No semantic analysis — treats all code as plain text, missing language-specific structure (AST parsing not implemented)

Large files (>10MB) may be truncated or cause memory issues during conversion

Binary files and compiled code are skipped; no decompilation or bytecode analysis

What makes it unique

Embeds file metadata (path, size, line count) directly into markdown output as structured comments, enabling LLMs to understand code context without separate metadata files

vs alternatives

Simpler and faster than AST-based tools like tree-sitter because it avoids parsing overhead, making it suitable for quick bulk conversions where semantic analysis isn't needed

github repository cloning and batch conversion

Medium confidence

Accepts GitHub repository URLs, clones them locally using git CLI, then applies the full directory traversal and markdown conversion pipeline. Handles authentication via SSH keys or personal access tokens, manages temporary clone directories, and cleans up after processing to avoid disk bloat.

Solves for

I want to convert an entire open-source GitHub repo into markdown for LLM analysis without manually cloning itI need to batch-process multiple GitHub repositories and convert them all to a unified markdown formatI'm analyzing a private GitHub repo and need to preserve authentication during the clone step

Best for

researchers analyzing open-source codebases at scale

developers building LLM-powered code search or recommendation systems

teams automating documentation generation from GitHub projects

Requires

Python 3.7+

git CLI installed and in PATH

GitHub repository URL (public or private with valid credentials)

Limitations

Requires git CLI to be installed and configured; no pure Python git implementation fallback

Large repositories (>1GB) may timeout or exhaust disk space during cloning

Private repositories require valid GitHub credentials; no built-in credential management or secure storage

What makes it unique

Integrates git cloning directly into the conversion pipeline rather than requiring separate manual clone steps, with automatic cleanup of temporary directories to prevent disk space leaks

vs alternatives

More convenient than manual git clone + conversion workflows because it handles cloning, filtering, and conversion in a single command, reducing user friction for bulk repository analysis

multi-format output generation with customizable structure

Medium confidence

Generates markdown output in multiple structural formats: flat single-file (all code concatenated), hierarchical (directory structure preserved), or indexed (with table of contents and cross-references). Supports custom templates for frontmatter, separators, and metadata injection to adapt output for different LLM consumption patterns.

Solves for

I need a single markdown file with my entire codebase for feeding to Claude in one promptI want to preserve directory structure in the output so the LLM understands code organizationI need to generate a table of contents and cross-references for easier navigation by LLMs

Best for

developers optimizing prompt context for different LLM models with varying context window sizes

teams generating documentation that needs to be both human-readable and LLM-friendly

researchers building structured datasets from source code with consistent formatting

Requires

Python 3.7+

Template files (if using custom templates)

Configuration file specifying output format preferences

Limitations

No automatic optimization for context window limits — users must manually split large outputs

Custom templates require understanding of markdown syntax; no visual template builder

Cross-references are text-based (markdown links) and may not be understood by all LLMs

What makes it unique

Supports multiple output topologies (flat vs. hierarchical) with pluggable template system, allowing users to optimize output structure for different LLM consumption patterns without code changes

vs alternatives

More flexible than fixed-format converters because it allows users to choose output structure based on their specific LLM's context window and comprehension patterns

binary file detection and intelligent skipping

Medium confidence

Uses file extension whitelisting and magic number detection (reading first N bytes) to identify binary files (compiled binaries, images, archives) and automatically exclude them from conversion. Logs skipped files for transparency and allows users to override detection rules via configuration.

Solves for

I want to convert my project without wasting time and tokens on binary files that LLMs can't understandI need to know which files were skipped and why during conversionI want to force inclusion of certain file types that are normally detected as binary

Best for

developers converting mixed-language projects with compiled artifacts

teams automating codebase conversion without manual file selection

researchers building clean code datasets without binary noise

Requires

Python 3.7+

Read permissions on files being analyzed

Optional: custom configuration file for override rules

Limitations

Magic number detection only checks first 512 bytes; sophisticated polyglot files may be misclassified

No support for compressed archives (zip, tar, gzip) — treats them as binary and skips them entirely

Extension-based detection can be fooled by misnamed files (e.g., .txt file containing binary data)

What makes it unique

Combines extension-based and magic number detection for binary identification, with configurable override rules, reducing false positives compared to extension-only approaches

vs alternatives

More accurate than simple extension-based filtering because it inspects file content, preventing inclusion of misnamed binary files that would waste LLM tokens

file size and line count metadata extraction

Medium confidence

Parses each source file to extract and embed metadata: total lines, code lines (excluding comments/blanks), file size in bytes, and language. Stores this metadata in markdown frontmatter or inline comments, enabling LLMs to understand code complexity and make informed decisions about processing.

Solves for

I want the LLM to know the size and complexity of each file so it can prioritize analysisI need to filter out very large files that might exceed context limitsI want to generate statistics about my codebase (total lines, file count) from the markdown output

Best for

developers preparing code for LLM analysis with awareness of file complexity

teams generating codebase statistics and metrics

researchers building datasets with rich metadata for code understanding models

Requires

Python 3.7+

Read permissions on source files

Limitations

Line counting is naive (counts all lines including comments); no semantic line counting (actual code lines)

No support for minified or single-line files — line counts may not reflect actual complexity

Metadata extraction adds ~5-10% overhead to conversion time for large codebases

What makes it unique

Embeds file metrics directly into markdown output as structured metadata, allowing LLMs to understand code complexity without separate analysis passes

vs alternatives

More integrated than separate metrics tools because metadata is embedded in the conversion output, making it immediately available to LLMs without post-processing

comment and docstring preservation with language-specific parsing

Medium confidence

Detects and preserves comments and docstrings during conversion using language-specific patterns (Python docstrings, JavaScript JSDoc, Java Javadoc, etc.). Maintains comment context relative to code blocks, enabling LLMs to understand intent and documentation without semantic analysis.

Solves for

I want the LLM to see my code comments and docstrings so it understands the intentI need to preserve documentation strings for API understandingI want to extract and highlight important comments for LLM focus

Best for

developers converting well-documented codebases for LLM analysis

teams generating API documentation from source code

researchers analyzing code intent and documentation patterns

Requires

Python 3.7+

Source code with comments/docstrings

Limitations

Comment detection is regex-based, not AST-based; may miss or misidentify comments in edge cases (e.g., comments in strings)

No semantic understanding of comment relevance; all comments treated equally

Inline comments may be separated from code during formatting, losing context

What makes it unique

Uses language-specific regex patterns to preserve comments and docstrings in context, rather than stripping them, maintaining semantic information for LLM comprehension

vs alternatives

Better for documentation-heavy codebases than minification-style tools because it preserves intent-bearing comments that help LLMs understand code purpose

configuration file support for batch processing

Medium confidence

Reads YAML or JSON configuration files specifying multiple repositories, output formats, filtering rules, and processing options. Enables users to define batch jobs declaratively without command-line arguments, supporting parameterization for different environments and use cases.

Solves for

I want to define a batch job that converts 10 GitHub repos with consistent settingsI need to version control my conversion settings and reuse them across team membersI want to parameterize conversion rules for different project types (Python vs. JavaScript)

Best for

teams automating bulk repository conversion with consistent settings

researchers running large-scale codebase analysis pipelines

DevOps engineers integrating auto-md into CI/CD workflows

Requires

Python 3.7+

YAML or JSON configuration file

Write permissions for output directories

Limitations

No schema validation for configuration files; invalid configs fail at runtime with unclear errors

No environment variable substitution; credentials must be hardcoded or passed separately

No support for configuration inheritance or templating; each config is independent

What makes it unique

Supports declarative configuration files for batch processing, allowing users to define complex multi-repository jobs without scripting or command-line complexity

vs alternatives

More maintainable than shell scripts for batch processing because configuration is version-controlled and human-readable, enabling team collaboration on conversion settings

progress reporting and logging with detailed conversion metrics

Medium confidence

Tracks and reports conversion progress in real-time: files processed, files skipped, total lines converted, output file size, and estimated time remaining. Logs detailed information about each file (path, size, language, skip reason) to a structured log file for debugging and auditing.

Solves for

I want to see progress while converting a large repository so I know it's not stuckI need to debug why certain files were skipped or not converted correctlyI want to generate a report of what was converted and what was excluded

Best for

developers converting large codebases and needing visibility into progress

teams auditing conversion results for completeness and accuracy

researchers tracking conversion metrics across multiple repositories

Requires

Python 3.7+

Write permissions for log files

Terminal or file system for log output

Limitations

Progress estimation assumes linear processing time; actual time may vary based on file sizes and I/O patterns

Logging adds ~2-5% overhead to conversion time for large codebases

Log files can grow large (>100MB) for very large repositories; no built-in log rotation

What makes it unique

Provides real-time progress reporting with detailed per-file logging, enabling users to monitor large conversions and debug issues without post-processing log analysis

vs alternatives

More informative than silent conversion because it provides visibility into what's being processed and why, critical for debugging large batch jobs

language-specific code block formatting with syntax hints

Medium confidence

Detects source code language from file extension and wraps code in markdown code blocks with language-specific syntax hints (e.g., python, javascript). Ensures LLMs can apply language-specific understanding and syntax highlighting, improving comprehension of language-specific idioms.

Solves for

I want the LLM to understand the language of each code block so it can apply language-specific knowledgeI need markdown output that renders with proper syntax highlighting in viewersI want to ensure code blocks are properly formatted for LLM consumption across multiple languages

Best for

developers converting polyglot codebases with multiple languages

teams generating documentation that needs proper syntax highlighting

researchers building language-specific code understanding datasets

Requires

Python 3.7+

Source code files with standard extensions

Limitations

Language detection is extension-based only; no content-based language detection for ambiguous files

No support for language aliases or variants (e.g., TypeScript detected as JavaScript)

Syntax hints are markdown-standard; some LLMs may not recognize all language identifiers

What makes it unique

Automatically detects language from file extension and applies markdown syntax hints, ensuring LLMs receive properly formatted code blocks without manual annotation

vs alternatives

More convenient than manual language annotation because it infers language from file extension, reducing user effort for large codebases

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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get-llms-txt

Generate LLM-friendly llms.txt files from markdown and MDX content files

recursive directory traversal with file filtering

1 shared capability

Repository28

llm-code-highlighter

Condense source code for LLM analysis by extracting essential highlights, utilizing a simplified version of Paul Gauthier's repomap technique from Aider Chat.

batch directory processing with recursive traversal

1 shared capability

MCP Server48

DesktopCommanderMCP

This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities

recursive filesystem traversal with depth control and context overflow protection

1 shared capability

Best For

✓developers preparing local codebases for LLM analysis or fine-tuning
✓teams automating documentation generation from source trees
✓researchers building datasets from open-source projects
✓developers preparing code for LLM-based code review or refactoring
✓AI researchers building code understanding datasets
✓teams documenting APIs by converting source code to markdown
✓researchers analyzing open-source codebases at scale
✓developers building LLM-powered code search or recommendation systems

Known Limitations

⚠No built-in support for symlinks or circular references — may cause infinite loops on recursive symlink structures
⚠Performance degrades on very large directories (100k+ files) without caching
⚠Ignore patterns must be manually configured; no automatic detection of project-specific exclusion rules
⚠No semantic analysis — treats all code as plain text, missing language-specific structure (AST parsing not implemented)
⚠Large files (>10MB) may be truncated or cause memory issues during conversion
⚠Binary files and compiled code are skipped; no decompilation or bytecode analysis

Requirements

Python 3.7+Read permissions on target filesystemSufficient disk space for output markdown filesSource code files in supported languagesWrite permissions for output directorygit CLI installed and in PATHGitHub repository URL (public or private with valid credentials)Network connectivity to github.com

Input / Output

Accepts: local filesystem paths, directory structures, file patterns (glob-style), source code files, code snippets, entire codebases, GitHub repository URLs, GitHub usernames/organization names, repository lists (CSV, JSON), converted markdown files, custom template definitions, file paths, file extensions, file content (first N bytes), YAML configuration files, JSON configuration files, configuration parameters, conversion process state, file processing events

Produces: filtered file lists, directory tree metadata, file paths with metadata, markdown files, formatted code blocks, structured text with metadata, consolidated documentation, metadata about processed repos, single markdown file, hierarchical markdown structure, indexed markdown with TOC, custom-formatted output, skip logs with reasons, configuration overrides, metadata dictionaries, markdown frontmatter, inline comments with metadata, statistics summaries, markdown with preserved comments, code blocks with documentation, batch processing results, multiple markdown outputs, processing logs, progress reports, structured log files, conversion metrics, summary statistics, markdown code blocks, formatted code with language hints

UnfragileRank

Adoption14%(40% weight)

Quality21%(20% weight)

Ecosystem60%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

10 capabilities

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Repository Details

163

Stars

Forks

Python

Language

NOASSERTION

License

Topics

aiai-toolconvertgithubllmllm-toolsmdpythonpython-convertpython-scriptscrape

Last commit: Jan 31, 2025

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Convert Files / Folders / GitHub Repos Into AI / LLM-ready Files

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Capabilities10 decomposed

recursive directory traversal with file filtering

Medium confidence

Solves for

Best for

developers preparing local codebases for LLM analysis or fine-tuning

teams automating documentation generation from source trees

researchers building datasets from open-source projects

Requires

Python 3.7+

Read permissions on target filesystem

Sufficient disk space for output markdown files

Limitations

No built-in support for symlinks or circular references — may cause infinite loops on recursive symlink structures

Performance degrades on very large directories (100k+ files) without caching

Ignore patterns must be manually configured; no automatic detection of project-specific exclusion rules

What makes it unique

Implements gitignore-compatible filtering rules during traversal rather than post-processing, reducing memory overhead and enabling early termination of excluded branches

vs alternatives

More efficient than generic file-listing tools because it filters during traversal rather than collecting all files first, critical for large monorepos

source code to markdown conversion with syntax preservation

Medium confidence

Solves for

Best for

developers preparing code for LLM-based code review or refactoring

AI researchers building code understanding datasets

teams documenting APIs by converting source code to markdown

Requires

Python 3.7+

Source code files in supported languages

Write permissions for output directory

Limitations

No semantic analysis — treats all code as plain text, missing language-specific structure (AST parsing not implemented)

Large files (>10MB) may be truncated or cause memory issues during conversion

Binary files and compiled code are skipped; no decompilation or bytecode analysis

What makes it unique

Embeds file metadata (path, size, line count) directly into markdown output as structured comments, enabling LLMs to understand code context without separate metadata files

vs alternatives

Simpler and faster than AST-based tools like tree-sitter because it avoids parsing overhead, making it suitable for quick bulk conversions where semantic analysis isn't needed

github repository cloning and batch conversion

Medium confidence

Solves for

Best for

researchers analyzing open-source codebases at scale

developers building LLM-powered code search or recommendation systems

teams automating documentation generation from GitHub projects

Requires

Python 3.7+

git CLI installed and in PATH

GitHub repository URL (public or private with valid credentials)

Limitations

Requires git CLI to be installed and configured; no pure Python git implementation fallback

Large repositories (>1GB) may timeout or exhaust disk space during cloning

Private repositories require valid GitHub credentials; no built-in credential management or secure storage

What makes it unique

Integrates git cloning directly into the conversion pipeline rather than requiring separate manual clone steps, with automatic cleanup of temporary directories to prevent disk space leaks

vs alternatives

More convenient than manual git clone + conversion workflows because it handles cloning, filtering, and conversion in a single command, reducing user friction for bulk repository analysis

multi-format output generation with customizable structure

Medium confidence

Solves for

Best for

developers optimizing prompt context for different LLM models with varying context window sizes

teams generating documentation that needs to be both human-readable and LLM-friendly

researchers building structured datasets from source code with consistent formatting

Requires

Python 3.7+

Template files (if using custom templates)

Configuration file specifying output format preferences

Limitations

No automatic optimization for context window limits — users must manually split large outputs

Custom templates require understanding of markdown syntax; no visual template builder

Cross-references are text-based (markdown links) and may not be understood by all LLMs

What makes it unique

Supports multiple output topologies (flat vs. hierarchical) with pluggable template system, allowing users to optimize output structure for different LLM consumption patterns without code changes

vs alternatives

More flexible than fixed-format converters because it allows users to choose output structure based on their specific LLM's context window and comprehension patterns

binary file detection and intelligent skipping

Medium confidence

Solves for

Best for

developers converting mixed-language projects with compiled artifacts

teams automating codebase conversion without manual file selection

researchers building clean code datasets without binary noise

Requires

Python 3.7+

Read permissions on files being analyzed

Optional: custom configuration file for override rules

Limitations

Magic number detection only checks first 512 bytes; sophisticated polyglot files may be misclassified

No support for compressed archives (zip, tar, gzip) — treats them as binary and skips them entirely

Extension-based detection can be fooled by misnamed files (e.g., .txt file containing binary data)

What makes it unique

Combines extension-based and magic number detection for binary identification, with configurable override rules, reducing false positives compared to extension-only approaches

vs alternatives

More accurate than simple extension-based filtering because it inspects file content, preventing inclusion of misnamed binary files that would waste LLM tokens

file size and line count metadata extraction

Medium confidence

Solves for

Best for

developers preparing code for LLM analysis with awareness of file complexity

teams generating codebase statistics and metrics

researchers building datasets with rich metadata for code understanding models

Requires

Python 3.7+

Read permissions on source files

Limitations

Line counting is naive (counts all lines including comments); no semantic line counting (actual code lines)

No support for minified or single-line files — line counts may not reflect actual complexity

Metadata extraction adds ~5-10% overhead to conversion time for large codebases

What makes it unique

Embeds file metrics directly into markdown output as structured metadata, allowing LLMs to understand code complexity without separate analysis passes

vs alternatives

More integrated than separate metrics tools because metadata is embedded in the conversion output, making it immediately available to LLMs without post-processing

comment and docstring preservation with language-specific parsing

Medium confidence

Solves for

Best for

developers converting well-documented codebases for LLM analysis

teams generating API documentation from source code

researchers analyzing code intent and documentation patterns

Requires

Python 3.7+

Source code with comments/docstrings

Limitations

Comment detection is regex-based, not AST-based; may miss or misidentify comments in edge cases (e.g., comments in strings)

No semantic understanding of comment relevance; all comments treated equally

Inline comments may be separated from code during formatting, losing context

What makes it unique

Uses language-specific regex patterns to preserve comments and docstrings in context, rather than stripping them, maintaining semantic information for LLM comprehension

vs alternatives

Better for documentation-heavy codebases than minification-style tools because it preserves intent-bearing comments that help LLMs understand code purpose

configuration file support for batch processing

Medium confidence

Solves for

Best for

teams automating bulk repository conversion with consistent settings

researchers running large-scale codebase analysis pipelines

DevOps engineers integrating auto-md into CI/CD workflows

Requires

Python 3.7+

YAML or JSON configuration file

Write permissions for output directories

Limitations

No schema validation for configuration files; invalid configs fail at runtime with unclear errors

No environment variable substitution; credentials must be hardcoded or passed separately

No support for configuration inheritance or templating; each config is independent

What makes it unique

Supports declarative configuration files for batch processing, allowing users to define complex multi-repository jobs without scripting or command-line complexity

vs alternatives

More maintainable than shell scripts for batch processing because configuration is version-controlled and human-readable, enabling team collaboration on conversion settings

progress reporting and logging with detailed conversion metrics

Medium confidence

Solves for

Best for

developers converting large codebases and needing visibility into progress

teams auditing conversion results for completeness and accuracy

researchers tracking conversion metrics across multiple repositories

Requires

Python 3.7+

Write permissions for log files

Terminal or file system for log output

Limitations

Progress estimation assumes linear processing time; actual time may vary based on file sizes and I/O patterns

Logging adds ~2-5% overhead to conversion time for large codebases

Log files can grow large (>100MB) for very large repositories; no built-in log rotation

What makes it unique

Provides real-time progress reporting with detailed per-file logging, enabling users to monitor large conversions and debug issues without post-processing log analysis

vs alternatives

More informative than silent conversion because it provides visibility into what's being processed and why, critical for debugging large batch jobs

language-specific code block formatting with syntax hints

Medium confidence

Solves for

Best for

developers converting polyglot codebases with multiple languages

teams generating documentation that needs proper syntax highlighting

researchers building language-specific code understanding datasets

Requires

Python 3.7+

Source code files with standard extensions

Limitations

Language detection is extension-based only; no content-based language detection for ambiguous files

No support for language aliases or variants (e.g., TypeScript detected as JavaScript)

Syntax hints are markdown-standard; some LLMs may not recognize all language identifiers

What makes it unique

Automatically detects language from file extension and applies markdown syntax hints, ensuring LLMs receive properly formatted code blocks without manual annotation

vs alternatives

More convenient than manual language annotation because it infers language from file extension, reducing user effort for large codebases

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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auto-md

Capabilities10 decomposed

recursive directory traversal with file filtering

source code to markdown conversion with syntax preservation

github repository cloning and batch conversion

multi-format output generation with customizable structure

binary file detection and intelligent skipping

file size and line count metadata extraction

comment and docstring preservation with language-specific parsing

configuration file support for batch processing

progress reporting and logging with detailed conversion metrics

language-specific code block formatting with syntax hints

Related Artifactssharing capabilities

Top AI Directories

markitdown

markdownify-mcp

get-llms-txt

llm-code-highlighter

DesktopCommanderMCP

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to auto-md

Are you the builder of auto-md?

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Data Sources

auto-md

Capabilities10 decomposed

recursive directory traversal with file filtering

source code to markdown conversion with syntax preservation

github repository cloning and batch conversion

multi-format output generation with customizable structure

binary file detection and intelligent skipping

file size and line count metadata extraction

comment and docstring preservation with language-specific parsing

configuration file support for batch processing

progress reporting and logging with detailed conversion metrics

language-specific code block formatting with syntax hints

Related Artifactssharing capabilities

Top AI Directories

markitdown

markdownify-mcp

get-llms-txt

llm-code-highlighter

DesktopCommanderMCP

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to auto-md

Are you the builder of auto-md?

Get the weekly brief

Data Sources