prompt-engineer
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
skill install https://www.promptspace.in/skills/prompt-engineerMaster the Art of Prompt Engineering
Building high-performance LLM applications requires more than just basic instructions. This skill equips your AI agent with a sophisticated framework for designing, debugging, and optimizing prompts across any major model provider. It solves the common problems of model drift, parsing failures, and hallucination by implementing industry-standard engineering patterns.
What it does
- Architectural Design: Implements advanced system prompt structures, including role anchoring, constraint blocks, and persona tuning.
- Precision Control: Utilizes few-shot prompting and chain-of-thought (CoT) reasoning to ensure logical consistency and format compliance.
- Agentic Workflows: Supports complex patterns like ReAct (Reasoning + Acting), Plan-and-Execute, and reflection loops for autonomous task completion.
- Reliable Outputs: Enforces structured data (JSON/XML) and implements robust defense mechanisms against prompt injection and jailbreaking.
- Context Management: Provides strategies for RAG (Retrieval-Augmented Generation), token budgeting, and conversation summarization.
Technical Compatibility
This skill is framework-agnostic and designed for developers working with OpenClaw, Python, and Go. It is optimized for high-reasoning models (GPT-4, Claude 3, Gemini Pro) and provides specific guidance for multimodal (image) prompting and tool-use orchestration.
High-Quality Outputs
Expect deterministic results: valid JSON objects ready for backend consumption, structured Markdown reports, and explainable reasoning chains that make debugging AI behavior straightforward for your development team.
Use cases
- Construct robust few-shot templates to ensure consistent output formatting
- Implement chain-of-thought patterns to improve complex reasoning accuracy
- Apply defensive prompting techniques to mitigate jailbreaks and injections
- Optimize context window usage to reduce latency and token consumption
- Standardize JSON schemas for reliable automated data extraction
Example
Prompt
Output
{
"role": "Go Developer",
"reasoning_chain": "1. Analyze HTTP handler... 2. Identify missing error check... 3. Propose fix.",
"status": "success",
"payload": "func(w http.ResponseWriter, r *http.Request) { ... }"
}Known limitations
- Performance varies by model version (best on GPT-4/Claude 3.5). - Does not automatically guarantee 100% JSON validity without parser retries. - Token overhead increases with few-shot examples.