リサーチ・学習のスキル

Deep web research for VCO: multi-hop search+browse+extract with an auditable action trace and a structured report (WebThinker-style).

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Use when preparing manuscripts, conference papers, or academic posters that need venue-specific templates, LaTeX style files, author guidelines, page limits, anonymity rules, camera-ready formatting, or submission compliance checks.

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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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Use when writing or revising scientific manuscript prose: IMRAD sections, abstracts, figure/table captions, citation-integrated paragraphs, reporting-guideline language, terminology, clarity, and journal-submission text.

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Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model based on query complexity. Search academic papers, recent studies, technical documentation, and general research information with citations.

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Open-ended scientific ideation partner. Use for research gaps, mechanism exploration, interdisciplinary connections, assumptions, possible research directions, and lightweight literature matrix or A+B paper-combination idea mapping. For structured testable hypotheses and validation plans, use hypothesis-generation instead.

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Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Agency-specific formatting, review criteria, budget preparation, broader impacts, significance statements, innovation narratives, and compliance with submission requirements.

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Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

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Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.

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Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

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This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

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Use when converting academic papers into dissemination assets such as Paper2Web websites, Paper2Video video abstracts, or Paper2Poster conference posters from LaTeX or PDF sources.

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Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

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Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

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Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

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Generate comprehensive market research reports and industry/competitive analysis in the style of top consulting firms. Use for market sizing, competitive landscape, market entry, investment thesis, strategic recommendations, and consulting-style business reports. Do not use for scientific reports, paper writing, LaTeX submission/PDF builds, EDGAR/FRED/Treasury/Data Commons data retrieval, or biomedical literature evidence work.

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Evaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.

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LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature engineering, hyperparameter tuning, overfitting, underfitting, baseline, ablation study, critique my approach, review my model, is this a good idea, should I use, what's wrong with, evaluate my solution, challenge my assumptions, discuss my approach Engages in critical discussion with minimum 3 rounds of iterative refinement. Challenges both user proposals and own suggestions with fact-based critique. Demands evidence and baselines before accepting solutions.

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Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

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Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls

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Personalized coding tutorials that build on your existing knowledge and use your actual codebase for examples. Creates a persistent learning trail that compounds over time using the power of AI, spaced repetition and quizes.

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Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

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Comprehensive citation management for academic research. Resolve bibliographic identifiers, extract accurate metadata, validate citations, deduplicate references, and generate properly formatted BibTeX entries. This skill should be used when you need to verify citation information, convert DOIs/PMIDs/arXiv IDs to BibTeX, or ensure reference accuracy in scientific writing.

日本語の概要は準備中です。原文の説明を表示しています。