context-engineer
Master context engineering and prompt engineering for AI agents and LLMs. Optimize system prompts, craft few-shot examples, implement chain-of-thought reasoning, manage context windows, design structured outputs, and build self-improving prompt patterns. Covers Anthropic, OpenAI, and Google best practices. Includes prompt optimizer that audits drafts against best practices, and context builder that generates optimal context windows for any task. Built for AI agents — Python stdlib only, no dependencies. Use for prompt optimization, system prompt design, agent instruction writing, LLM output debugging, context window management, and few-shot example crafting.
Why use this skill?
Master context and prompt engineering with the Context Engineer skill for OpenClaw. Audit, build, and optimize agent prompts using proven LLM best practices.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aiwithabidi/a6-context-engineerWhat This Skill Does
The context-engineer skill acts as a comprehensive framework for mastering LLM interactions. It is designed to bridge the gap between human intent and machine execution through structured prompt engineering, system prompt optimization, and context window management. Instead of relying on guesswork, this skill provides a systematic audit process that evaluates your prompts against the gold-standard practices of industry leaders like Anthropic, OpenAI, and Google. It provides a toolkit to build robust agent instructions, craft high-precision few-shot examples, and implement chain-of-thought patterns that force models to reason through complex problems before delivering final output.
Installation
Installation is handled through the OpenClaw CLI. Ensure your environment is configured correctly, then execute the following command in your terminal: clawhub install openclaw/skills/skills/aiwithabidi/a6-context-engineer. This skill runs entirely in Python without external dependencies, ensuring privacy and local execution security.
Use Cases
This skill is indispensable for developers and AI operators building complex agent pipelines. Primary use cases include:
- Refining Sub-Agent Instructions: Ensuring that autonomous sub-agents follow consistent behavioral patterns defined in SOUL.md.
- Reducing Hallucinations: Utilizing few-shot examples and structured output schema to force deterministic behavior.
- Context Optimization: Curating the input window to ensure critical data is prioritized, effectively managing token density for complex multi-step tasks.
- Debugging Model Failures: Analyzing why a model failed to perform a specific action and identifying flaws in instruction hierarchy.
Example Prompts
- "context-engineer: Audit my system prompt for the data-analysis agent and suggest 3 few-shot examples to improve JSON output consistency."
- "context-engineer: Build a context window for a complex code review task, including role assignment for a Senior Security Engineer and strict XML tag structure."
- "context-engineer: Analyze my draft prompt for the content writer agent and provide a critique based on the 10 Commandments of Prompting."
Tips & Limitations
To get the best results, always prioritize clear structural constraints. Use XML tags (e.g., <instructions>, <context>, <examples>) to help the LLM delineate between different input types. Remember that this tool is a static analyzer and constructor; it does not replace the need for empirical testing of prompts. While it optimizes the structure and logic of your requests, you must still validate outputs against your specific domain use cases to ensure correctness. Always follow the 10 Commandments, particularly the principle of iterating empirically rather than relying on intuitive feel.
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{
"plugins": {
"official-aiwithabidi-a6-context-engineer": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read, file-write
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