Academic

ML research for RAN with reinforcement learning, causal inference, and cognitive consciousness integration. Use when researching ML algorithms for RAN optimization, implementing reinforcement learning agents, developing causal models, or enabling AI-driven RAN innovation.

Generate personalized content recommendations based on learner profiles, performance, preferences, and learning analytics. Use for adaptive learning systems, content discovery, and personalized guidance. Activates on "recommend content", "next best", "personalization", or "what should I learn next".

data-collection for evidence-based learning research and evaluation.

Deep technical assistant for projects that combine Neural Quantum States (FFNN-based) with Sample-based Quantum Diagonalization (SQD). Trigger this skill whenever the task involves: (1) designing or analyzing NQS architectures for quantum chemistry, (2) connecting classical samplers to qiskit-addon-sqd, (3) studying sample-efficiency, bias, and variance in few-sample regimes (e.g. 12-bit / 14-bit H2).

Expert academic text editing for scientific manuscripts with focus on clarity, precision, and journal-quality standards

Research topics by verifying actual source content. Use when asked to research or study links and documentation.

Calculate and interpret effect sizes for statistical analyses. Use when: (1) Reporting research results to show practical significance, (2) Meta-analysis to combine study results, (3) Grant writing to justify expected effects, (4) Interpreting published studies beyond p-values, (5) Sample size planning for power analysis.

Personalize textbook chapter content for logged-in users based on their background, preferences, and learning style collected during signup.

Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.

Assess risk of bias in research studies for systematic reviews. Use when: (1) Conducting systematic reviews, (2) Evaluating study quality, (3) GRADE assessments, (4) Meta-analysis planning.

Design peer assessment systems including rubrics, calibration activities, review protocols, and feedback quality guidelines. Use for peer learning. Activates on "peer review", "peer assessment", "peer feedback", or "student grading".

Machine learning integration patterns for rRNA-Phylo covering three use cases - rRNA sequence classification (supervised learning with sklearn/PyTorch), multi-tree consensus (ensemble methods), and generative tree synthesis (GNNs/transformers). Includes feature engineering, model training, hyperparameter tuning, model serving, versioning, and evaluation metrics for bioinformatics ML workflows.

Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation.

Generate course content for HPM 883 (PhD Advanced Quantitative Methods) following establishedtemplates, pedagogical principles, and accessibility requirements. Supports unit planning,session generation, code-along creation, problem sets, and reference curation.USE WHEN: creating HPM 883 content, generating units/sessions, building code-alongs/labs,curating references for Spring 2026 course.

Detect AI/LLM-generated text patterns in research writing. Use when: (1) Reviewing manuscript drafts before submission, (2) Pre-commit validation of documentation, (3) Quality assurance checks on research artifacts, (4) Ensuring natural academic writing style, (5) Tracking writing authenticity over time. Analyzes grammar perfection, sentence uniformity, paragraph structure, word frequency (AI-typical words like 'delve', 'leverage', 'robust'), punctuation patterns, and transition word overuse.

Domain-specific configuration profiles for learning resource creation. Defines search strategies, special fields, terminology policies, and content structures for different academic domains: technology, history, science, arts, and general. Use when researcher or writer agents need domain-adapted behavior.

Use for Phase 1 of Course OS - collecting and cataloging source materials including existing course content, reference books, videos, competitor courses, and expert knowledge. Triggers on "/course-import", "import course materials", "add sources", "collect references", or when starting a new course project.

Use when editing or improving manuscript introductions with unclear research gaps, poor flow, or weak literature integration - provides funnel structure (context → gap → this study), hypothesis clarity patterns, and CNS-level positioning

Use when determining which repositories or files a task affects. Distinguishes between target repos (where changes happen) and reference repos (for learning patterns). Supports both standard mode returning { targets, references } and audit mode detecting specific config files to audit. Returns structured scope object.

Researches implementation approaches using browser automation via /chrome, then implements the best solution. Use when building new features, solving unfamiliar problems, or need to find best practices before implementing. Combines learning with doing.