Setup & Installation
openclaw skills install @alirezarezvani/senior-prompt-engineer
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
npx clawhub install senior-prompt-engineer
Version History
v2.1.1Mar 9, 2026 - Version note by skill developer:
v2.1.1: optimization, reference splits
What This Skill Does
Tools and workflows for analyzing LLM prompts, evaluating RAG pipelines, and validating agentic systems. Includes Python scripts for token counting, clarity scoring, few-shot example management, and agent workflow visualization.
Combines prompt analysis, RAG evaluation, and agent validation into a single workflow rather than requiring separate tools for each concern.
When to use it
- Cutting token costs in a production GPT-4 prompt
- Measuring retrieval accuracy in a RAG-based Q&A system
- Validating agent tool configs before a deployment
- Building consistent few-shot examples for entity extraction
- Enforcing structured JSON output from an LLM
Example Workflow
Here's how your AI assistant might use this skill in practice.
INPUT
User asks: Optimize this customer support prompt to reduce token usage
AGENT
- 1Runs prompt_optimizer.py --analyze on the prompt file to get token count, clarity score, and identified issues
- 2Reviews issues: redundant context, missing output format specification, ambiguous instruction verbs
- 3Applies patterns: removes redundant lines, adds explicit JSON schema, rewrites vague verbs to concrete actions
- 4Runs --optimize to generate the revised prompt file
- 5Compares optimized vs baseline with --compare to show token reduction and clarity improvement
OUTPUT
Optimized prompt file with a comparison report showing token reduction percentage and resolved issues list