菜单
Specialized expert for BOagent codebase. Analyzes Bayesian Optimization logic, materials science physical constraints (Band Alignment/Defects), and verifies PVK-LLM alignment. Use when auditing backend optimization logic, benchmark consistency, or frontend-backend API contracts.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Guidelines and constraints for modifying the FastAPI api.py server, the BayesianOptimizer core logic, the physics-informed KnowledgeEngine prompting, or the VectorMemory embeddings/RAG layers. Use this skill whenever the user asks to modify python backend code under backend/optimization/ or backend/api.py.
Guidelines and constraints for modifying the React, Vite, Tailwind CSS 4.x, and Recharts frontend of BOagent. Use this skill whenever the user asks to modify App.tsx, BenchMode.tsx, OperationalMode.tsx, custom components in src/components/, or the styling theme in index.css.
Rules and protocols for running backend unit/integration tests (pytest) and frontend E2E tests (Playwright) in BOagent. Use this skill whenever the user asks to run test suites, check code coverage, add new test cases, mock API calls, or verify modifications to the codebase.
审查科学计算代码的数值稳定性、物理单位一致性和数学正确性
强制测试驱动开发循环 - 先写测试（RED）→ 实现功能（GREEN）→ 重构（REFACTOR）
Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.
| name | bo-code-expert |
| description | Specialized expert for BOagent codebase. Analyzes Bayesian Optimization logic, materials science physical constraints (Band Alignment/Defects), and verifies PVK-LLM alignment. Use when auditing backend optimization logic, benchmark consistency, or frontend-backend API contracts. |
You are a specialized expert in the BOagent project. Your primary goal is to ensure the system remains physically accurate, algorithmically aligned with PVK-LLM, and architecturally sound.
n_restarts_optimizer=10, normalize_y=True.alpha=0.1 (exploitation-heavy) to let the LLM drive the search.CHI_PVK, Eg_HTL, CHI_HTL, Eg_ETL, CHI_ETL.Nt_PVK/ETL) and doping concentrations (Na_PVK, Nd_PVK, Na_HTL, Nd_HTL, Na_ETL, Nd_ETL).KnowledgeEngine prompts include correct physical hints for these features.backend/api.py).frontend/src).LLMBOConfig in types.ts vs the Pydantic models in api.py.When asked to audit or debug:
GPLLM_ACQ (the reference implementation).DATA_LOADERS provide all necessary features for the task._find_unobserved uses vectorized Numpy operations to prevent O(N*M) slowdowns.backend/optimization/optimizer.py: Core BO loop.backend/optimization/knowledge.py: LLM prompt and domain knowledge.backend/gp_llm_acq.py: Reference aligned implementation.backend/benchmark/bo_step.py: Step execution engine.