Haibarakiku

Expert culinary professional with advanced skills in food preparation, kitchen operations management, menu engineering, and culinary team leadership. Covers recipe development, technique guidance, flavor troubleshooting, food cost optimization, and HACCP food safety compliance. Use when: cooking, recipe development, menu planning, kitchen management, food safety questions, or culinary team

Elite AI Product Manager skill with expertise in AI product strategy, LLM product development, ML feature prioritization, AI ethics and fairness. Transforms AI into a principal AI PM capable of shipping successful AI-powered products. Use when: ai-product, product-management, llm-products, ai-strategy, ml-roadmap, ai-ethics. Works with Claude Code, OpenAI Codex, Kimi Code, OpenCode, Cursor,

Elite Computer Vision Engineer skill with expertise in deep learning for images and video (CNNs, Transformers), object detection (YOLO, DETR), segmentation, OCR, and production CV deployment (TensorRT, ONNX, OpenVINO). Transforms AI into a principal CV engineer capable of building real-time vision systems. Use when: computer-vision, image-processing, object-detection, deep-learning, cnn,

Elite Data Scientist skill with expertise in statistical analysis, predictive modeling, experimental design (A/B testing), feature engineering, and data visualization. Transforms AI into a principal data scientist capable of extracting actionable insights from complex datasets and building production-grade ML models. Use when: data-science, statistics, machine-learning, predictive-modeling,

Expert-level Prompt Engineer skill. Transforms AI into a specialist who designs, evaluates, and optimizes prompts for LLMs, RAG pipelines, and agent workflows. Covers prompt patterns (zero-shot, few-shot, CoT, ReAct, Tree-of-Thought), RAG context injection and chunking strategies, agent tool-calling and multi-agent coordination, LLM-as-judge evaluation pipelines, and prompt injection

Senior brand strategist with 15+ years experience advising Fortune 500 companies and high-growth startups. Specializes in brand positioning, market segmentation, brand architecture, identity systems, and go-to-market strategy. Delivers executive-level frameworks for competitive differentiation, portfolio brand structure, and repositioning initiatives. Use when: developing new brand strategy,

Licensed Professional Electrical Engineer (PE) specializing in power systems, lighting design, fire alarm systems, and renewable energy. Expert in NEC, IEEE standards, SKM/ETAP power analysis, and Revit MEP. 10+ years designing commercial, industrial, and institutional electrical systems. Use when: electrical engineering, power systems, lighting design, fire alarm, renewable energy,

Chartered Quantity Surveyor (MRICS) with 15+ years in construction cost management, contract administration, and value engineering. Expert in cost planning, tender documentation, post-contract administration, and dispute resolution. Managed $2B+ in construction value across commercial, infrastructure, and residential projects. Use when: cost estimating, quantity surveying, contract

Generate an Abaqus FEA training dataset for surrogate / ML models. Latin Hypercube Sampling (or sparse-pattern sampling) over a parameterized design vector, one case folder per sample, batch-submit Abaqus jobs via subprocess, recover from crashes, and write a unified dataset index. Use when the user wants to "build a training set for a surrogate model", "sweep design parameters in Abaqus", "run N FEA simulations", or "sample a design space".

Closed-loop inverse-design validation. Given a target deformation field, solve the inverse problem on a trained surrogate (Ridge / linear), then run an Abaqus FEA verification and compare surrogate-predicted vs. true displacement field. Reports MSE / MAE / max-abs-error / NRMSE side-by-side, plus saturated-channel count, so you can quantify the surrogate-FEA gap. Use when the user wants to evaluate "is my surrogate good enough for inverse design?", "how big is the surrogate-FEA gap on this target?", "did the optimizer find a real solution or just a surrogate hallucination?"

Convert per-case Abaqus FEA outputs into ML-ready (X, Y) wide-table CSVs. Pivots irregular FEA mesh node displacements onto a regular N×N grid via direct binning (structured mesh) or bilinear resampling, picks the final frame as the deformation target, and aggregates across many cases into X_amplitude.csv (design vectors) + Y_grid_uz.csv (flattened grid displacement). Use when the user has a folder of completed FEA cases and wants to train a Ridge / MLP / Gaussian Process surrogate on the (input → displacement field) mapping.