Context Engineering
技能 已验证 活跃Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
To educate users on the principles and practices of context engineering, enabling them to write, edit, and optimize agent prompts for better performance and reliability.
功能
- Explains context components and constraints
- Details attention mechanisms and context windows
- Provides practical guidance on file-system access and hybrid strategies
- Covers context budgeting and degradation patterns
- Offers optimization techniques like compaction and masking
使用场景
- Writing and optimizing agent prompts
- Improving the reliability of AI agents
- Understanding limitations of LLM context windows
- Designing effective agent workflows
非目标
- Providing specific prompt templates for all use cases
- Automating context management without user intervention
- Increasing the LLM's raw context window size
Trust
- info:Issues AttentionOpen issues: 6 in last 90d, Closed issues: 8 in last 90d. The closure rate is ~57%, indicating moderate maintainer engagement.
安装
请先添加 Marketplace
/plugin marketplace add NeoLabHQ/context-engineering-kit/plugin install customaize-agent@context-engineering-kit质量评分
已验证类似扩展
Create Command
100Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration
Project Development
100This skill should be used when the user asks to "start an LLM project", "design batch pipeline", "evaluate task-model fit", "structure agent project", or mentions pipeline architecture, agent-assisted development, cost estimation, or choosing between LLM and traditional approaches.
Write A Skill
100Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.
Arize Prompt Optimization
100Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Prompt Optimization
100应用提示重复以提高非推理 LLM 的准确性
Agent Evaluation
99Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.