Senior Prompt Engineer

This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RA...

This skill appears to be a coherent prompt-engineering toolkit that runs local Python scripts to analyze prompts and agent configs. Before installing or running: - Inspect rag_evaluator.py (the full file) for any network calls, hardcoded endpoints, or attempts to use API keys; the SKILL.md does not declare any required credentials. - Run the scripts on non-sensitive sample files first and in a sandboxed environment (or virtualenv) so you can observe any unexpected outbound network traffic. - Review any agent_config.yaml you give to agent_orchestrator.py—the tool validates and parses configs but uses a simple YAML parser; avoid feeding untrusted configs that could cause unexpected behavior. If rag_evaluator.py uses external LLM/embedding services, ensure you supply credentials explicitly and understand where data is sent before using with private data.

Type: OpenClaw Skill Name: senior-prompt-engineer Version: 2.1.1 The 'senior-prompt-engineer' skill bundle is a comprehensive toolkit for prompt optimization and LLM evaluation. The included Python scripts (agent_orchestrator.py, prompt_optimizer.py, and rag_evaluator.py) perform static analysis, token estimation, and heuristic scoring of RAG systems without using dangerous functions like eval(), subprocess, or making network calls. The documentation and SKILL.md instructions are strictly technical and aligned with the stated purpose, showing no signs of malicious intent or prompt injection attacks.