- name
- prompt-optimizer-toolkit
- description
- LLM Prompt Engineering Toolkit - 提示词分析、优化、模板化与质量评分 | Analyze, optimize, template, and score your LLM prompts with built-in engineering strategies
- metadata
- openclaw
- requires
- bins
- ["python3"]
- install
- kind
- python
- requirements
- requirements.txt
Prompt Optimizer Toolkit
功能
- Analyze — 多维度分析提示词质量（清晰度、具体性、上下文、约束、示例、角色）
- Optimize — 自动检测最佳优化策略并应用
- Template — 内置5种经典提示词模板（CoT、Few-shot、Role-play、Structured、Constraints）
- Library — 本地提示词库管理，支持标签检索
- Score — 1-10分综合质量评分
使用
from scripts.prompt_optimizer import PromptOptimizer, PromptLibrary
optimizer = PromptOptimizer()
# 分析提示词
analysis = optimizer.analyze("Write a story about a robot")
# → {'clarity_score': 7, 'specificity_score': 5, 'overall_score': 4, ...}
# 自动优化
better = optimizer.optimize("Write a story about a robot")
# → 添加角色、结构、约束
# 使用模板
prompt = optimizer.apply_template("role_playing", domain="creative writing", task="Write a sci-fi story")
# 提示词库
lib = PromptLibrary()
lib.save("coding-helper", "You are a senior Python developer...", tags=["coding", "python"])
CLI
python3 scripts/prompt_optimizer.py
