A professional toolkit for optimizing LLM prompts, evaluating RAG performance, and designing complex agentic workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install senior-prompt-engineer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install senior-prompt-engineer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Senior Prompt Engineer?
The Senior Prompt Engineer skill is a comprehensive resource designed for developers building sophisticated AI applications. It provides automated tools to analyze prompts for token efficiency, clarity, and structure, ensuring high-quality outputs while minimizing costs. As part of the Openclaw Skills ecosystem, this tool simplifies the transition from simple chat interfaces to complex, production-ready agentic systems.
By leveraging advanced patterns like Chain-of-Thought and Role Prompting, this skill empowers users to create robust few-shot examples and enforced structured outputs. Whether you are building a research assistant or an automated data extractor, this skill provides the frameworks necessary to maintain high standards of groundedness and relevance in your LLM integrations.
Senior Prompt Engineer Use Cases
- Optimizing existing prompts to reduce token usage and operational costs.
- Evaluating the relevance and faithfulness of RAG systems using automated metrics.
- Designing and visualizing complex multi-step agent workflows using ReAct or Plan-Execute patterns.
- Creating consistent structured outputs like JSON or XML for downstream application logic.
- Generating and validating few-shot examples to improve model performance on niche tasks.
How Senior Prompt Engineer Works
- Analyze: Run the Prompt Optimizer to identify redundant instructions, token waste, or ambiguous verbs in your existing prompt files.
- Optimize: Apply suggested prompt engineering patterns such as role framing or explicit output constraints to generate a refined version.
- Evaluate: Use the RAG Evaluator to test your retrieval quality by measuring context relevance against a set of baseline questions.
- Orchestrate: Define your agent configuration and toolsets, then use the Orchestrator to validate security settings and visualize the execution logic.
- Refine: Compare the optimized results against baseline metrics to ensure continuous improvement in your Openclaw Skills implementations.
Senior Prompt Engineer Setup
To begin using the Senior Prompt Engineer features within Openclaw Skills, ensure you have the necessary scripts environment configured. Use the following commands to interact with the core tools:
# Analyze and optimize a prompt file
python scripts/prompt_optimizer.py prompts/my_prompt.txt --analyze
# Evaluate RAG retrieval quality
python scripts/rag_evaluator.py --contexts contexts.json --questions questions.json
# Visualize agent workflow from definition
python scripts/agent_orchestrator.py agent_config.yaml --visualize
Senior Prompt Engineer Data Schema & Taxonomy
This skill manages various technical assets including prompt templates, evaluation datasets, and agent configuration files to streamline the development lifecycle within Openclaw Skills.
| Asset Type | Format | Description |
|---|---|---|
| Prompt Templates | .txt / .md | The raw text used for model instructions and few-shot examples |
| Agent Definitions | .yaml / .json | Structured files defining tools, patterns (e.g., ReAct), and flow logic |
| Evaluation Sets | .json | Paired questions and ground-truth contexts for RAG testing |
| Analysis Reports | .json | Output metadata including token counts, cost estimates, and clarity scores |
Senior Prompt Engineer Advanced Features
- Token & Cost Analytics: Provides granular token counts and estimated costs for specific models like GPT-4 during the optimization phase.
- Workflow Visualization: Generates ASCII or Mermaid diagrams to represent complex agent tool-calling sequences and decision nodes.
- RAG Quality Metrics: Automated calculation of Context Relevance, Answer Faithfulness, and Groundedness scores.
- Schema Enforcement: Tools to design and validate prompts that require strict JSON or XML structured outputs.
- Example Management: Automatically extract and manage few-shot examples to maintain consistency across different model versions.
SKILL.md
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