llm-min.txt: Min.js Style Compression of Tech Docs for LLM Context 🤖

📜 Table of Contents

llm-min.txt: Min.js Style Compression of Tech Docs for LLM Context 🤖

What is `llm-min.txt` and Why is it Important?

If you've ever used an AI coding assistant (like GitHub Copilot, Cursor, or others powered by Large Language Models - LLMs), you've likely encountered situations where they don't know about the latest updates to programming libraries. This knowledge gap exists because AI models have a "knowledge cutoff" – a point beyond which they haven't learned new information. Since software evolves rapidly, this limitation can lead to outdated recommendations and broken code.

Several innovative approaches have emerged to address this challenge:

llms.txt A community-driven initiative where contributors create reference files (llms.txt) containing up-to-date library information specifically formatted for AI consumption.
Context7 A service that dynamically provides contextual information to AIs, often by intelligently summarizing documentation.

While these solutions are valuable, they face certain limitations:

llms.txt files can become extraordinarily large – some exceeding 800,000 tokens (word fragments). This size can overwhelm many AI systems' context windows.

Many shorter llms.txt variants simply contain links to official documentation, requiring the AI to fetch and process those documents separately. Even the comprehensive versions (llms-full.txt) often exceed what most AI assistants can process at once. Additionally, these files may not always reflect the absolute latest documentation.
Context7 operates somewhat as a "black box" – while useful, its precise information selection methodology isn't fully transparent to users. It primarily works with GitHub code repositories or existing llms.txt files, rather than any arbitrary software package.

llm-min.txt offers a fresh approach:

Inspired by min.js files in web development (JavaScript with unnecessary elements removed), llm-min.txt adopts a similar philosophy for technical documentation. Instead of feeding an AI a massive, verbose manual, we leverage another AI to distill that documentation into a super-condensed, highly structured summary. The resulting llm-min.txt file captures only the most essential information needed to understand a library's usage, packaged in a format optimized for AI assistants rather than human readers.

Modern AI reasoning capabilities excel at this distillation process, creating remarkably efficient knowledge representations that deliver maximum value with minimal token consumption.

Understanding `llm-min.txt`: A Machine-Optimized Format 🧩

The llm-min.txt file utilizes the Structured Knowledge Format (SKF) – a compact, machine-optimized format designed for efficient AI parsing rather than human readability. This format organizes technical information into distinct, highly structured sections with precise relationships.

Key Elements of the SKF Format:

Header Metadata: Every file begins with essential contextual information:
- # IntegratedKnowledgeManifest_SKF: Format identifier and version
- # SourceDocs: [...]: Original documentation sources
- # GenerationTimestamp: ...: Creation timestamp
- # PrimaryNamespace: ...: Top-level package/namespace, critical for understanding import paths
Three Core Structured Sections: The content is organized into distinct functional categories:
- # SECTION: DEFINITIONS (Prefix: D): Describes the static aspects of the library:
  - Canonical component definitions with unique global IDs (e.g., D001:G001_MyClass)
  - Namespace paths relative to PrimaryNamespace
  - Method signatures with parameters and return types
  - Properties/fields with types and access modifiers
  - Static relationships like inheritance or interface implementation
  - Important: This section effectively serves as the glossary for the file, as the traditional glossary (G section) is used during generation but deliberately omitted from the final output to save space.
- # SECTION: INTERACTIONS (Prefix: I): Captures dynamic behaviors within the library:
  - Method invocations (INVOKES)
  - Component usage patterns (USES_COMPONENT)
  - Event production/consumption
  - Error raising and handling logic, with references to specific error types
- # SECTION: USAGE_PATTERNS (Prefix: U): Provides concrete usage examples:
  - Common workflows for core functionality
  - Step-by-step sequences involving object creation, configuration, method invocation, and error handling
  - Each pattern has a descriptive name (e.g., U_BasicCrawl) with numbered steps (U_BasicCrawl.1, U_BasicCrawl.2)
Line-Based Structure: Each item appears on its own line following precise formatting conventions that enable reliable machine parsing.

Example SKF Format (Simplified):

# IntegratedKnowledgeManifest_SKF/1.4 LA
# SourceDocs: [example-lib-docs]
# GenerationTimestamp: 2024-05-28T12:00:00Z
# PrimaryNamespace: example_lib

# SECTION: DEFINITIONS (Prefix: D)
# Format_PrimaryDef: Dxxx:Gxxx_Entity [DEF_TYP] [NAMESPACE "relative.path"] [OPERATIONS {op1:RetT(p1N:p1T)}] [ATTRIBUTES {attr1:AttrT1}] ("Note")
# ---
D001:G001_Greeter [CompDef] [NAMESPACE "."] [OPERATIONS {greet:Str(name:Str)}] ("A simple greeter class")
D002:G002_AppConfig [CompDef] [NAMESPACE "config"] [ATTRIBUTES {debug_mode:Bool("RO")}] ("Application configuration")
# ---

# SECTION: INTERACTIONS (Prefix: I)
# Format: Ixxx:Source_Ref INT_VERB Target_Ref_Or_Literal ("Note_Conditions_Error(Gxxx_ErrorType)")
# ---
I001:G001_Greeter.greet INVOKES G003_Logger.log ("Logs greeting activity")
# ---

# SECTION: USAGE_PATTERNS (Prefix: U)
# Format: U_Name:PatternTitleKeyword
#         U_Name.N:[Actor_Or_Ref] ACTION_KEYWORD (Target_Or_Data_Involving_Ref) -> [Result_Or_State_Change_Involving_Ref]
# ---
U_BasicGreeting:Basic User Greeting
U_BasicGreeting.1:[User] CREATE (G001_Greeter) -> [greeter_instance]
U_BasicGreeting.2:[greeter_instance] INVOKE (greet name='Alice') -> [greeting_message]
# ---
# END_OF_MANIFEST

The llm-min-guideline.md file (generated alongside llm-min.txt) provides detailed decoding instructions and schema definitions that enable an AI to correctly interpret the SKF format. It serves as the essential companion document explaining the notation, field meanings, and relationship types used throughout the file.

Does it Really Work? Visualizing the Impact

llm-min.txt achieves dramatic token reduction while preserving the essential knowledge needed by AI assistants. The chart below compares token counts between original library documentation (llm-full.txt) and the compressed llm-min.txt versions:

These results demonstrate token reductions typically ranging from 90-95%, with some cases exceeding 97%. This extreme compression, combined with the highly structured SKF format, enables AI tools to ingest and process library documentation far more efficiently than with raw text.

In our samples directory, you can examine these impressive results firsthand:

sample/crawl4ai/llm-full.txt: Original documentation (uncompressed)
sample/crawl4ai/llm-min.txt: The compressed SKF representation
sample/crawl4ai/llm-min-guideline.md: The format decoder companion file, also seen in llm-min-guideline.md

Most compressed files contain around 10,000 tokens – well within the processing capacity of modern AI assistants.

How to use it?

Simply reference the files in your AI-powered IDE's conversation, and watch your assistant immediately gain detailed knowledge of the library:

How does it perform?

It's necessary to make a benchmark but incredibly hard. LLM code generation is stochastic and the quality of the generated code depends on many factors. crawl4ai / google-genai / svelte are all packages current LLM failed to generate correct code for. Using llm-min will largely improve the success rate of code generation.

Quick Start 🚀

Getting started with llm-min is straightforward:

1. Installation:

For regular users (recommended):

pip install llm-min

# Install required browser automation tools
playwright install

For contributors and developers:

# Clone the repository (if not already done)
# git clone https://github.com/your-repo/llm-min.git
# cd llm-min

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies with UV (faster than pip)
uv sync
uv pip install -e .

# Optional: Set up pre-commit hooks for code quality
# uv pip install pre-commit
# pre-commit install

2. Set Up Your Gemini API Key: 🔑

llm-min uses Google's Gemini AI to generate compressed documentation. You'll need a Gemini API key to proceed:

Best practice: Set an environment variable named GEMINI_API_KEY with your key value:

# Linux/macOS
export GEMINI_API_KEY=your_api_key_here

# Windows (Command Prompt)
set GEMINI_API_KEY=your_api_key_here

# Windows (PowerShell)
$env:GEMINI_API_KEY="your_api_key_here"

Alternative: Supply your key directly via the --gemini-api-key command-line option.

You can obtain a Gemini API key from the Google AI Studio or Google Cloud Console.

3. Generate Your First llm-min.txt File: 💻

Choose one of the following input sources:

Input Source Options	Short	Type	What it does
`--input-folder`	`-i`	`DIRECTORY`	📁 Process local documentation files. Recursively scans a directory for `.md`, `.txt`, and `.rst` files. Web crawling is skipped when using this option.
`--package`	`-pkg`	`TEXT`	📦 Process a Python package. Automatically finds and crawls the package's documentation website.
`--doc-url`	`-u`	`TEXT`	🌐 Process a documentation website. Directly crawls the specified URL.

Configuration Options	Short	Type	What it does
`--output-dir`	`-o`	`DIRECTORY`	Where to save the generated files (default: `llm_min_docs`).
`--output-name`	`-n`	`TEXT`	Give a custom name for the subfolder inside `output-dir`.
`--library-version`	`-V`	`TEXT`	Specify the library version (useful when using `--input-folder` or `--doc-url`).
`--max-crawl-pages`	`-p`	`INTEGER`	Max web pages to read (default: 200; 0 means no limit). Only applies to web crawling.
`--max-crawl-depth`	`-D`	`INTEGER`	How many links deep to follow on a website (default: 3). Only applies to web crawling.
`--chunk-size`	`-c`	`INTEGER`	How much text to give the AI at once (default: 0, which enables adaptive chunking). If 0, `llm-min` automatically determines an optimal size.
`--gemini-api-key`	`-k`	`TEXT`	Your Gemini API Key (if not set as an environment variable).
`--gemini-model`	`-m`	`TEXT`	Which Gemini model to use (default: `gemini-2.5-flash-lite-preview-06-17`).
`--force-reprocess`			Force reprocessing even if `llm-full.txt` exists and ignore intermediate files.
`--save-fragments`		`BOOLEAN`	Save intermediate fragments for debugging and retry capability (default: True).
`--verbose`	`-v`		Show more detailed messages while it's working.

Example Commands:

# 📦 Process the "typer" Python package, save to "my_docs" folder
llm-min -pkg "typer" -o my_docs -p 50

# 🌐 Process the FastAPI documentation website
llm-min -u "https://fastapi.tiangolo.com/" -o my_docs -p 50

# 📁 Process documentation files in a local folder
llm-min -i "./docs" -o my_docs

# 📁 Process local files with custom output name and version
llm-min -i "./my-project-docs" -o my_docs -n "my-project" -V "1.2.3"

# 📁 Process a project's entire documentation directory structure
llm-min -i "/path/to/project/documentation" -o project_docs --verbose

Local Folder Processing Details: 📁

When using --input-folder, llm-min will:

Recursively scan the specified directory for documentation files
Process files with extensions: .md (Markdown), .txt (Plain text), .rst (reStructuredText)
Combine all found files into a single content stream
Skip web crawling entirely (making it faster and not requiring internet connectivity)
Preserve the original combined content in llm-full.txt and generate the compressed llm-min.txt

This is particularly useful for:

Internal/proprietary documentation that isn't available online
Local project documentation that you're developing
Offline processing when internet access is limited
Custom documentation in various formats

4. Programmatic Usage in Python: 🐍

You can also integrate llm-min directly into your Python applications:

from llm_min import LLMMinGenerator
import os

# Configuration for the AI processing
llm_config = {
    "api_key": os.environ.get("GEMINI_API_KEY"),  # Use environment variable
    "model_name": "gemini-2.5-flash-lite-preview-06-17",  # Recommended model
    "chunk_size": 600000,  # Characters per AI processing batch
    "max_crawl_pages": 200,  # Maximum pages to crawl (only for web crawling)
    "max_crawl_depth": 3,  # Link following depth (only for web crawling)
}

# Initialize the generator (output files will go to ./my_output_docs/[source_name]/)
generator = LLMMinGenerator(output_dir="./my_output_docs", llm_config=llm_config)

# 📦 Generate llm-min.txt for a Python package
try:
    generator.generate_from_package("requests")
    print("✅ Successfully created documentation for 'requests'!")
except Exception as e:
    print(f"❌ Error processing 'requests': {e}")

# 🌐 Generate llm-min.txt from a documentation URL
try:
    generator.generate_from_url("https://fastapi.tiangolo.com/")
    print("✅ Successfully processed FastAPI documentation!")
except Exception as e:
    print(f"❌ Error processing URL: {e}")

# 📁 Generate llm-min.txt from local documentation files
try:
    # Read and combine all documentation files from a local folder
    import pathlib
    docs_folder = pathlib.Path("./my-project-docs")
    
    # Collect content from supported file types
    content = ""
    for ext in [".md", ".txt", ".rst"]:
        for file_path in docs_folder.rglob(f"*{ext}"):
            with open(file_path, encoding="utf-8") as f:
                content += f"# File: {file_path.name}\n\n"
                content += f.read() + "\n\n---\n\n"
    
    # Process the combined content
    generator.generate_from_text(
        input_content=content, 
        source_name="my-project",
        library_version="1.0.0"  # Optional
    )
    print("✅ Successfully processed local documentation!")
except Exception as e:
    print(f"❌ Error processing local files: {e}")

For a complete list of command-line options, run:

llm-min --help

Output Directory Structure 📂

When llm-min completes its processing, it creates the following organized directory structure:

your_chosen_output_dir/
└── name_of_package_or_website/
    ├── llm-full.txt             # Complete documentation text (original content)
    ├── llm-min.txt              # Compressed SKF/1.4 LA structured summary
    └── llm-min-guideline.md     # Essential format decoder for AI interpretation

For example, running llm-min -pkg "requests" -o my_llm_docs produces:

my_llm_docs/
└── requests/
    ├── llm-full.txt             # Original documentation
    ├── llm-min.txt              # Compressed SKF format (D, I, U sections)
    └── llm-min-guideline.md     # Format decoding instructions

Important: The llm-min-guideline.md file is a critical companion to llm-min.txt. It provides the detailed schema definitions and format explanations that an AI needs to correctly interpret the structured data. When using llm-min.txt with an AI assistant, always include this guideline file as well.

Choosing the Right AI Model (Why Gemini) 🧠

llm-min utilizes Google's Gemini family of AI models for document processing. While you can select a specific Gemini model via the --gemini-model option, we strongly recommend using the default: gemini-2.5-flash-lite-preview-06-17.

This particular model offers an optimal combination of capabilities for documentation compression:

Advanced Reasoning: Excels at understanding complex technical documentation and extracting the essential structural relationships needed for the SKF format.
Exceptional Context Window: With a 1-million token input capacity, it can process large documentation chunks at once, enabling more coherent and comprehensive analysis.
Cost Efficiency: Provides an excellent balance of capability and affordability compared to other large-context models.

The default model has been carefully selected to deliver the best results for the llm-min compression process across a wide range of documentation styles and technical domains.

How it Works: A Look Inside (src/llm_min) ⚙️

The llm-min tool employs a sophisticated multi-stage process to transform verbose documentation into a compact, machine-optimized SKF manifest:

Input Processing: Based on your command-line options, llm-min gathers documentation from the appropriate source:
- Package (--package "requests"): Automatically discovers and crawls the package's documentation website
- URL (--doc-url "https://..."): Directly crawls the specified documentation website
- Local Folder (--input-folder "./docs"): Recursively scans for .md, .txt, and .rst files and combines their content
Text Preparation: The collected documentation is cleaned and segmented into manageable chunks for processing. The original text is preserved as llm-full.txt.
Three-Step AI Analysis Pipeline (Gemini): This is the heart of the SKF manifest generation, orchestrated by the compact_content_to_structured_text function in compacter.py:
- Step 1: Global Glossary Generation (Internal Only):
  - Each document chunk is analyzed using the SKF_PROMPT_CALL1_GLOSSARY_TEMPLATE prompt to identify key technical entities and generate a chunk-local glossary fragment with temporary Gxxx IDs.
  - These fragments are consolidated via the SKF_PROMPT_CALL1_5_MERGE_GLOSSARY_TEMPLATE prompt, which resolves duplicates and creates a unified entity list.
  - The re_id_glossary_items function then assigns globally sequential Gxxx IDs (G001, G002, etc.) to these consolidated entities.
  - This global glossary is maintained in memory throughout the process but is not included in the final llm-min.txt output to conserve space.
- Step 2: Definitions & Interactions (D & I) Generation:
  - For the first document chunk (or if there's only one chunk), the AI uses the SKF_PROMPT_CALL2_DETAILS_SINGLE_CHUNK_TEMPLATE with the global glossary to generate initial D and I items.
  - For subsequent chunks, the SKF_PROMPT_CALL2_DETAILS_ITERATIVE_TEMPLATE is used, providing both the global glossary and previously generated D&I items as context to avoid duplication.
  - As each chunk is processed, newly identified D and I items are accumulated and assigned sequential global IDs (D001, D002, etc. and I001, I002, etc.).
- Step 3: Usage Patterns (U) Generation:
  - Similar to Step 2, the first chunk uses SKF_PROMPT_CALL3_USAGE_SINGLE_CHUNK_TEMPLATE, receiving the global glossary, all accumulated D&I items, and the current chunk text.
  - Subsequent chunks use SKF_PROMPT_CALL3_USAGE_ITERATIVE_TEMPLATE, which additionally receives previously generated U-items to enable pattern continuation and avoid duplication.
  - Usage patterns are identified with descriptive names (e.g., U_BasicNetworkFetch) and contain numbered steps (e.g., U_BasicNetworkFetch.1, U_BasicNetworkFetch.2).
Final Assembly: The complete llm-min.txt file is created by combining:
- The SKF manifest header (protocol version, source docs, timestamp, primary namespace)
- The accumulated DEFINITIONS section
- The accumulated INTERACTIONS section
- The accumulated USAGE_PATTERNS section
- A final # END_OF_MANIFEST marker

Conceptual Pipeline Overview:

User Input      →  Doc Gathering   →  Text Processing   →  AI Step 1: Glossary   →  In-Memory Global    →  AI Step 2: D&I     →  Accumulated D&I
(CLI/Python)       (Package/URL)      (Chunking)           (Extract + Merge)        Glossary (Gxxx)        (Per chunk)          (Dxxx, Ixxx)
                                                                                                                                     ↓
           ┌─────────────────────────────────────────────────────────────────────────────────────────────────┐                      ↓
           ↓                                                                                                 ↑                      ↓
Final SKF Manifest   ←   Assembly   ←   Accumulated Usage   ←   AI Step 3: Usage   ←   Global Glossary + Accumulated D&I
(llm-min.txt)            (D,I,U)        Patterns (U_Name.N)      (Per chunk)           (Required context for generating valid U-items)

This multi-stage approach ensures that the SKF manifest is comprehensive, avoids duplication across chunks, and maintains consistent cross-references between entities, definitions, interactions, and usage patterns.

What's Next? Future Plans 🔮

We're exploring several exciting directions to evolve llm-min:

Public Repository for Pre-Generated Files 🌐 A central hub where the community could share and discover llm-min.txt files for popular libraries would be valuable. This would eliminate the need for individual users to generate these files repeatedly and ensure consistent, high-quality information. Key challenges include quality control, version management, and hosting infrastructure costs.
Code-Based Documentation Inference 💻 An intriguing possibility is using source code analysis (via Abstract Syntax Trees) to automatically generate or augment documentation summaries. While initial experiments have shown this to be technically challenging, particularly for complex libraries with dynamic behaviors, it remains a promising research direction that could enable even more accurate documentation.
Model Control Protocol Integration 🤔 While technically feasible, implementing llm-min as an MCP server doesn't fully align with our current design philosophy. The strength of llm-min.txt lies in providing reliable, static context – a deterministic reference that reduces the uncertainty sometimes associated with dynamic AI integrations. We're monitoring user needs to determine if a server-based approach might deliver value in the future.

We welcome community input on these potential directions!

Common Questions (FAQ) ❓

Q: Do I need a reasoning-capable model to generate an llm-min.txt file? 🧠

A: Yes, generating an llm-min.txt file requires a model with strong reasoning capabilities like Gemini. The process involves complex information extraction, entity relationship mapping, and structured knowledge representation. However, once generated, the llm-min.txt file can be effectively used by any competent coding model (e.g., Claude 3.5 Sonnet) to answer library-specific questions.

Q: Does llm-min.txt preserve all information from the original documentation? 📚

A: No, llm-min.txt is explicitly designed as a lossy compression format. It prioritizes programmatically relevant details (classes, methods, parameters, return types, core usage patterns) while deliberately omitting explanatory prose, conceptual discussions, and peripheral information. This selective preservation is what enables the dramatic token reduction while maintaining the essential technical reference information an AI assistant needs.

Q: Why does generating an llm-min.txt file take time? ⏱️

A: Creating an llm-min.txt file involves a sophisticated multi-stage AI pipeline:

Gathering and preprocessing documentation
Analyzing each chunk to identify entities (glossary generation)
Consolidating entities across chunks
Extracting detailed definitions and interactions from each chunk
Generating representative usage patterns

This intensive process can take several minutes, particularly for large libraries. However, once created, the resulting llm-min.txt file can be reused indefinitely, providing much faster reference information for AI assistants.

Q: I received a "Gemini generation stopped due to MAX_TOKENS limit" error. What should I do? 🛑

A: This error indicates that the Gemini model reached its output limit while processing a particularly dense or complex documentation chunk. Try reducing the --chunk-size option (e.g., from 600,000 to 300,000 characters) to give the model smaller batches to process. While this might slightly increase API costs due to more separate calls, it often resolves token limit errors.

Q: What's the typical cost for generating one llm-min.txt file? 💰

A: Processing costs vary based on documentation size and complexity, but for a moderate-sized library, expect to spend between $0.01 and $1.00 USD in Gemini API charges. Key factors affecting cost include:

Total documentation size
Number of chunks processed
Complexity of the library's structure
Selected Gemini model

For current pricing details, refer to the Google Cloud AI pricing page.

Q: Can I process local documentation files without internet access? 📁

A: Yes! The --input-folder option is perfect for offline processing. When using this option, llm-min will:

Skip web crawling entirely (no internet required for content gathering)
Only need internet access for the Gemini API calls during compression
Support .md, .txt, and .rst files recursively in any directory structure
Work with internal/proprietary documentation that isn't publicly available online

This makes it ideal for processing private documentation, local development docs, or when working with limited internet connectivity.

Q: Did you vibe code this project 🤖

A: Yes, definitely. This project was developed using Roocode with a custom configuration called Rooroo.

Want to Help? Contributing 🤝

We welcome contributions to make llm-min even better! 🎉

Whether you're reporting bugs, suggesting features, or submitting code changes via pull requests, your involvement helps improve this tool for everyone. Check our GitHub repository for contribution guidelines and open issues.

License 📜

This project is licensed under the MIT License. See the LICENSE file for complete details.

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
.github/workflows		.github/workflows
assets		assets
sample		sample
src		src
tests		tests
tools		tools
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.roomodes		.roomodes
CHANGELOG.MD		CHANGELOG.MD
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Repository files navigation

llm-min.txt: Min.js Style Compression of Tech Docs for LLM Context 🤖

📜 Table of Contents

What is `llm-min.txt` and Why is it Important?

Understanding `llm-min.txt`: A Machine-Optimized Format 🧩

Does it Really Work? Visualizing the Impact

Quick Start 🚀

Output Directory Structure 📂

Choosing the Right AI Model (Why Gemini) 🧠

How it Works: A Look Inside (src/llm_min) ⚙️

What's Next? Future Plans 🔮

Common Questions (FAQ) ❓

Want to Help? Contributing 🤝

License 📜

About

Uh oh!

Releases 1

Packages

Uh oh!

Contributors 2

Uh oh!

Languages

License

marv1nnnnn/llm-min.txt

Folders and files

Latest commit

History

Repository files navigation

llm-min.txt: Min.js Style Compression of Tech Docs for LLM Context 🤖

📜 Table of Contents

What is llm-min.txt and Why is it Important?

Understanding llm-min.txt: A Machine-Optimized Format 🧩

Does it Really Work? Visualizing the Impact

Quick Start 🚀

Output Directory Structure 📂

Choosing the Right AI Model (Why Gemini) 🧠

How it Works: A Look Inside (src/llm_min) ⚙️

What's Next? Future Plans 🔮

Common Questions (FAQ) ❓

Want to Help? Contributing 🤝

License 📜

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases 1

Packages 0

Uh oh!

Contributors 2

Uh oh!

Languages

What is `llm-min.txt` and Why is it Important?

Understanding `llm-min.txt`: A Machine-Optimized Format 🧩

Packages