Prompt Engineer
技能 已验证 活跃Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
To serve as an expert resource for users looking to create, refine, and test prompts for large language models, ensuring optimal performance, accuracy, and efficiency.
功能
- Optimizing prompts for accuracy and token efficiency
- Generating structured output schemas (JSON, function calling)
- Implementing advanced prompting patterns (CoT, Few-shot, ReAct)
- Developing evaluation frameworks and test suites
- Providing guidance on system prompts and context management
使用场景
- Designing prompts for new LLM applications
- Refactoring existing prompts for better performance
- Building reliable and consistent LLM interactions
- Creating robust prompt evaluation frameworks
非目标
- Directly executing prompts against an LLM
- Managing LLM model deployment or infrastructure
- Replacing the need for user-defined task logic
安装
请先添加 Marketplace
/plugin marketplace add jeffallan/claude-skills/plugin install claude-skills@fullstack-dev-skills质量评分
已验证类似扩展
SHAP Model Interpretability
100Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Arize Prompt Optimization
100Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
Prompt Optimization
100应用提示重复以提高非推理 LLM 的准确性
Molfeat
99Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
OraClaw Forecast
100AI 代理的时间序列预测。ARIMA 和 Holt-Winters 预测(含置信区间)。预测收入、流量、价格或任何序列数据。推理延迟低于 5 毫秒。
Arize Evaluator
100Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.