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    Prompt Engineer

    21

    Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.

    Free

    571 uses4.7 (7 reviews)
    prompt-engineer

    prompt-engineer

    Example session with this skill installed

    Design a system prompt for a Go developer persona that uses a reasoning chain to fix an HTTP handler, then return the response as a JSON object with role, reasoning_chain, and payload.

    • Read your context and instructions
    • Compiled the prompt-engineer
    {
  "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) { ... }"
}

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Master 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.

    • 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.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

    Reviews

    4.7
    7 reviews
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    Trust & safety

    Security scan

    Security scan passed 7 months ago

    • Free with an Agensi account

    Permissions

    Terminal / Shell
    Listed7 months ago

    What's inside

    Frequently Asked Questions

    This skill provides a comprehensive framework of engineering patterns designed to eliminate non-deterministic behavior, prevent JSON parsing errors, and secure your agent against prompt injection attacks.

    Yes, this skill is framework-agnostic and optimized for industry-leading models including GPT-4, Claude 3, and Gemini Pro, with specific implementation support for OpenClaw, Python, and Go environments.

    You will receive a production-ready library of advanced templates and architectural patterns, including system prompt structures, JSON enforcement schemas, and security-hardened defense logic.

    While this skill includes advanced logic for RAG and token budgeting, it is designed to work with your existing data; it does not include a standalone vector database but provides the structures to optimize one.

    Setup is straightforward and involves integrating the provided engineering blocks into your agent's system instructions or configuration files using the included implementation guide.

    Buyers receive lifetime access to the skill, which includes periodic updates to maintain compatibility as model providers release new versions or change prompt handling behaviors.

    New in LLM Ops

    Free