No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by bonigarcia · MCP Server · ★ 159
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Context Engineering Context engineering can be defined as the practice of designing systems that provide a Large Language Model (LLM) and AI agents with all the necessary information to complete a task effectively. It goes beyond prompt engineering since it focuses on building a comprehensive and structured context from various sources like instructions, external knowledge, memory, tools, and state. The central idea is that the success of a complex LLM-based system depends more on the quality and completeness of the context provided than on the specific wording of the prompt itself. Tobi Lütke, the CEO of Shopify, coined the term context engineering in a tweet on June 19, 2025. He defined context engineering as the art of providing all the context for the task to be plausibly solvable by the LLM. This novel concept captures the essence of the current evolution of LLM-based systems, inspiring others (like me) to understand and define this emerging discipline. Since then, I've been working on a book entitled Context Engineering: Build Consistent, Accurate, Predictable AI Systems, published by Manning.
| Stars | 159 |
| Forks | 26 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 58.123976038343/100 |
| Last Updated | 2026-10-02 |
| Created | 2025-10-16 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
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context-engineering is Context Engineering: Build Consistent, Accurate, Predictable AI Systems. It is categorized as a MCP Server with 159 GitHub stars.
context-engineering is primarily written in Python. It covers topics such as agent-skills, agentic-ai, context-engineering.
You can find installation instructions and usage details in the context-engineering GitHub repository at github.com/bonigarcia/context-engineering. The project has 159 stars and 26 forks, indicating an active community.
context-engineering is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to context-engineering on Agent Skills Hub include remind, zypher-agent, oreilly-ai-agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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