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Comprehensive STEM academic modeling and documentation skill using CCB eight-model distributed architecture (Claude/Gemini/Codex/OpenCode/iFlow/Kimi/Qwen/DeepSeek). Use when creating academic STEM research notes, mathematical derivations, engineering documentation, or scientific papers requiring rigorous logic, mathematical proofs, code implementations, and cross-disciplinary synthesis. Triggers o

Process Gemini chat exports into structured academic history notes following the Four-Step Deep Reading methodology (🟢 Foundation → 🔵 Critique → 🟣 Context → 🔴 Synthesis). Use when (1) Processing Gemini chat history exports, (2) Expanding existing history notes, (3) Creating comprehensive academic notes from historical readings, (4) Structuring research notes with YAML frontmatter, Mermaid visu

Redmine project management assistant. Use when user mentions Redmine, asks to create/update/list tickets, manage issues, track time, log timesheets, fill timesheet, assign tasks, change issue status, add notes, or check project progress on Redmine.

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable cod

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implem

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Tints the VS Code window chrome (title bar, activity bar, status bar, tabs, user chat bubbles) in a chosen color, allowing users to distinguish parallel Claude Code windows in Alt+Tab and the taskbar.

Use when the user wants to turn an audio meeting recording into a project-aware transcript and notes, or invokes /listen.

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Za