Context Engineering Kit

A hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint, focused on improving agent result quality and predictability.

The marketplace is based on prompts our company's developers have used daily for a long time, supplemented by plugins from benchmarked papers and high-quality projects.

Key Features

• Simple to Use - Easy to install and use without any dependencies. Contains automatically used skills and self-explanatory commands.
• Token-Efficient - Carefully crafted prompts and architecture, preferring command-oriented skills with sub-agents over general information skills when possible, to minimize populating context with unnecessary information.
• Quality-Focused - Each plugin is focused on meaningfully improving agent results in a specific area.
• Granular - Install only the plugins you need. Each plugin loads only its specific agents, commands, and skills, without overlap or redundant skills.
• Scientifically proven - Plugins are based on proven techniques and patterns validated by reputable benchmarks and studies.
• Open-Standards - Skills are based on agentskills.io specification. The SDD plugin is based on the Arc42 specification standard for software development documentation.

News

Updates from key releases:

• v3.1.0: Improved Spec-Driven Development plugin generated code quality by embedding DDD/SOLID rules in the developer agent and adding a dedicated code-reviewer agent that applies functional and OOP best-practices rules together with Muda waste analysis to reduce code complexity and duplication.
• v3.0.0: Added support for AMP and Hermes agents. Tech Stack plugin now automatically injects typescript best practices when agent reads or writes TypeScript files.
• v2.2.0: Subagent-Driven Development plugin now works as a distilled version of SDD plugin using meta-judge and judge sub-agents for specification generation on the fly and in parallel to implementation. DDD plugin now includes Clean Architecture, DDD, SOLID, Functional Programming, and other pattern examples as rules that are automatically added to the context during code writing.
• v2.1.0: Spec-Driven Development plugin agents include high-level code quality guidelines from DDD plugin.
• v2.0.0: Spec-Driven Development plugin was rewritten from scratch. It is now able to produce working code in 99% of cases on real-life production projects!

Quick Start

Step 1: Install Marketplace and Plugins

/plugin marketplace add NeoLabHQ/context-engineering-kit

/plugin install reflexion@NeoLabHQ/context-engineering-kit

gemini extensions install https://github.com/NeoLabHQ/context-engineering-kit

agy plugin install https://github.com/NeoLabHQ/context-engineering-kit/antigravity

npx skills add NeoLabHQ/context-engineering-kit

npx openskills install NeoLabHQ/context-engineering-kit
npx openskills sync

Step 2: Use Plugin

> claude "implement user authentication"
# Claude implements user authentication, then you can ask it to reflect on implementation

> /reflect
# It analyses results and suggests improvements
# If issues are obvious, it will fix them immediately
# If they are minor, it will suggest improvements that you can respond to
> fix the issues

# If you would like to prevent issues found during reflection from appearing again,
# ask Claude to extract resolution strategies and save the insights to project memory
> /memorize

Alternatively, you can use the reflect word in the initial prompt:

> claude "implement user authentication, then reflect"
# Claude implements user authentication,
# then hook automatically runs /reflect

In order to use this hook, you need to have bun installed. However, it is not required for the overall command.

Documentation

You can find the complete Context Engineering Kit documentation here.

However, the main plugins we recommend starting with are Subagent-Driven Development and Spec-Driven Development.

Agent Reliability Engineering

The three plugins in this marketplace are designed to improve how accurately and consistently the agent follows provided instructions and to reduce hallucinations and bias toward incorrect solutions. They are not competitors but rather complementary to each other, because they allow you to balance reliability vs. token cost. Here is a high-level comparison of different agent usage approaches and the probability of receiving results that are fully accurate and include zero hallucinations, based on task complexity:

Reliability metrics are based on more than year of real development usage on production projects.

Plugins List

To view all available pl