contextly 1.1.0

The ultimate context-engineering workspace CLI for LLMs.

Context-Ly CLI

Context-Ly CLI is a proprietary Context Intelligence Engine designed to help developers generate high-quality, token-efficient context for Large Language Models (LLMs).

Rather than manually explaining your project to an AI assistant in every session, Context-Ly analyzes your repository, discovers conventions, learns team rules, and generates structured context files that help AI tools understand your codebase more effectively.

The CLI acts as a persistent Context Memory Layer for your repository, enabling consistent AI interactions across development workflows.

Features

• Repository analysis and context generation
• Automatic framework and dependency detection
• Architecture visualization through project structure analysis
• Team convention discovery and memory management
• Persistent project-specific context storage
• LLM-ready Context Pack generation
• Repository complexity and token usage inspection
• Context-as-Code workflow with version-controlled project memory

Installation

Install Context-Ly directly from PyPI:

pip install contextly

Verify the installation:

contextly --help

Prerequisites

• Python 3.9 or later
• A local Git repository or project directory to analyze

No external services or API keys are required for the core functionality.

Quick Start

Initialize Context-Ly in your project:

contextly init

Analyze your repository and generate a complete project context:

contextly analyze

This command automatically:

• Reads your README documentation
• Scans the project structure
• Detects frameworks and dependencies
• Discovers conventions and stored memory
• Generates a comprehensive PROJECT_CONTEXT.md

The generated file can be used directly with AI coding assistants and LLMs.

Commands

contextly init

Initialize Context-Ly in the current project.

contextly init

Creates:

.contextly/
├── config.yaml
├── memory/
└── packs/

contextly analyze

Generate a complete repository context file.

contextly analyze

This command:

• Reads project documentation
• Analyzes repository structure
• Detects frameworks and technologies
• Loads stored team conventions
• Generates PROJECT_CONTEXT.md

Output:

PROJECT_CONTEXT.md

contextly discover

Run the Pattern Discovery Engine.

contextly discover

Discovers repository conventions such as:

• TailwindCSS usage
• Zustand state management
• React Query patterns
• Service-layer architecture hints
• Framework-specific conventions

The command provides insight into patterns already present within the codebase.

contextly learn --auto

Convert discovered conventions into permanent project memory.

contextly learn --auto

Example:

Save convention: TailwindCSS (Uses TailwindCSS for styling.)? [y/N]

Approved conventions are stored in:

.contextly/memory/rules.yaml

This creates a persistent memory layer that can be committed to source control and shared across teams.

contextly memory

Inspect all stored project memory and conventions.

contextly memory

Displays all saved rules, conventions, and architectural preferences currently remembered by Context-Ly.

contextly pack <directory>

Generate an LLM-ready Context Pack from a specific directory.

contextly pack src/components

The command:

• Reads all files in the target directory
• Calculates token usage
• Bundles the content into a reusable Context Pack

Output location:

.contextly/packs/

Useful for sharing focused portions of a large codebase with an LLM.

contextly inspect

Analyze repository complexity and token consumption.

contextly inspect

Provides visibility into:

• Large files
• Potential token-heavy directories
• Context window bottlenecks
• Repository complexity hotspots

This helps identify areas that may negatively impact AI context quality.

contextly export <pack_name>

Fuses your memory rules and the specified context pack into a single, comprehensive Context Payload.

contextly export cli

The output is instantly copied to your clipboard, ready to be pasted directly into an LLM.

contextly explain <domain>

Extracts a highly-optimized structural context payload for a specific domain based on the AST Knowledge Graph.

contextly explain core

It copies a JSON payload to your clipboard, allowing the LLM to understand the architecture without wasting tokens scanning raw files.

Understanding Context-Ly Ignore Philosophies

Context-Ly utilizes two distinct "ignore" policies depending on the operation:

1. Packing & Inspection (contextly pack, contextly inspect): These commands respect your .gitignore and .contextlyignore files. This ensures that generated packs and token counts omit irrelevant files (like node_modules, compiled binaries, etc.), producing concise, token-efficient context for the LLM.
2. Discovery & Intelligence (contextly discover, contextly learn): The Pattern Discovery Engine ignores your .gitignore. It uses a minimal, hardcoded skip-list (only completely toxic directories like .git or .venv). This allows Context-Ly to correctly discover architectural patterns and package dependencies in valid directories (like a frontend/ folder) that you might have legitimately added to .gitignore to keep your root repository clean.

Example Workflow

contextly init

contextly discover

contextly learn --auto

contextly analyze

Result:

.contextly/
PROJECT_CONTEXT.md

Your repository now has a persistent memory layer and an AI-ready context file generated from both repository analysis and learned team conventions.

Why Context-Ly?

Modern AI coding tools are powerful, but they often lack project-specific context.

Context-Ly bridges that gap by transforming repository knowledge, team conventions, and architectural patterns into structured context that can be consistently shared with LLMs.

The goal is simple:

Build context once. Use it everywhere.

Changelog

For all release notes and version history, please see the CHANGELOG.md.

Contributing

Contributions, issues, and feature requests are welcome.

If you discover a bug, have an idea for improving repository intelligence, or want to contribute new scanners and analysis capabilities, please open an issue or submit a pull request.

License

This project is proprietary and distributed under the Contextly End-User License Agreement (EULA).

See the LICENSE file for details.