peer-review
Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
pennylane
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
perplexity-search
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's 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.
plotly
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
polars
Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.
protocolsio-integration
Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
pubchem-database
Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics.
pubmed-database
Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.
pufferlib
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.
pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
pydicom
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
pyhealth
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 implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
ml-expert
Implement machine learning solutions including model architectures, training pipelines, optimization strategies, and performance improvements. This skill spawns a specialist ML implementation agent...
pylabrobot
Laboratory automation toolkit for controlling liquid handlers, plate readers, pumps, heater shakers, incubators, centrifuges, and analytical equipment. Use this skill when automating laboratory workflows, programming liquid handling robots (Hamilton STAR, Opentrons OT-2, Tecan EVO), integrating lab equipment, managing deck layouts and resources (plates, tips, containers), reading plates, or creating reproducible laboratory protocols. Applicable for both simulated protocols and physical hardware control.
pymatgen
Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
pymc-bayesian-modeling
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
pymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
pyopenms
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.
pysam
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.
pytdc
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
mcp-prompts-guide
Create powerful MCP prompts that guide AI interactions with templates, arguments, and context injection
implementation-review
Automatically trigger review agents after task completion. Use when strategic-planner finishes planning tasks (calls plan-consultant) or when main agent completes coding tasks in /implement workflow (calls code-reviewer). Triggers on phrases like "plan complete", "implementation done", "coding finished", "ready for review".
hooks-management
Use PROACTIVELY when you need to create, update, configure, or validate Claude hooks for various events and integrations
hook
Use PROACTIVELY when you need to create, update, configure, or validate Claude hooks for various events and integrations
generating-output-styles
Creates custom output styles for Claude Code that modify system prompts and behavior. Use when the user asks to create output styles, customize Claude's response format, generate output-style files, or mentions output style configuration.
executing-epic-workflow
Execute systematic feature development using EPIC methodology (Explore, Research, Plan, Validate, Implement, Review, Iterate). Use when building features, implementing complex tasks, or following structured development workflows. Delegates exploration, research, planning, validation, and review to specialized agents.
engineering-nba-data
Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.
designing-components
Use this skill when you need to design a component
command-management
Use PROACTIVELY this skill when you need to create or update custom commands following best practices
coding-with-tailiwnd
Use this skill when you need to code with tailwind css
code-instructor
Educational code development skill that teaches programming concepts while building applications. Use when the user wants to learn how code works, understand programming concepts, or build an app with detailed explanations. Provides line-by-line breakdowns, explains the 'why' behind code patterns, uses pedagogical teaching methods, and builds apps incrementally with educational commentary at each step.
brainstorming-features
Facilitates creative ideation sessions for mobile and web app features, generating structured ideas with user stories, technical considerations, and implementation suggestions. Use when planning new features, exploring product direction, generating app ideas, feature discovery, product brainstorming, or when user mentions 'brainstorm', 'ideate', 'app ideas', or 'feature suggestions'.
agile-planning
Generate agile release plans with sprints and roadmaps using unique sprint codes. Use when creating sprint schedules, product roadmaps, release planning, or when user mentions agile planning, sprints, roadmap, or release plans.
agent-management
Use PROACTIVELY this agent when you need to design and create optimal Claude Code subagents, update existing agents with new capabilities, revise agent configurations, analyze project requirements to identify specialized roles, or craft precise agent configurations with appropriate tool permissions and model tiers. When the user specify "Create or Update subagent [name]", this skill must be triggered.
type-driven-design-rust
Type-driven design patterns in Rust - typestate, newtype, builder pattern, and compile-time guarantees
thiserror-expert
Provides guidance on creating custom error types with thiserror, including proper derive macros, error messages, and source error chaining. Activates when users define error enums or work with thiserror.
test-coverage-advisor
Reviews test coverage and suggests missing test cases for error paths, edge cases, and business logic. Activates when users write tests or implement new features.
rust-2024-migration
Guides users through migrating to Rust 2024 edition features including let chains, async closures, and improved match ergonomics. Activates when users work with Rust 2024 features or nested control flow.
rmcp-quickstart
Quick start guide for creating MCP servers with the rmcp crate - installation, concepts, and first server
property-testing-guide
Introduces property-based testing with proptest, helping users find edge cases automatically by testing invariants and properties. Activates when users test algorithms or data structures.
port-adapter-designer
Helps design port traits and adapter implementations for external dependencies. Activates when users need to abstract away databases, APIs, or other external systems.
parquet-optimization
Proactively analyzes Parquet file operations and suggests optimization improvements for compression, encoding, row group sizing, and statistics. Activates when users are reading or writing Parquet files or discussing Parquet performance.
object-store-best-practices
Ensures proper cloud storage operations with retry logic, error handling, streaming, and efficient I/O patterns. Activates when users work with object_store for S3, Azure, or GCS operations.
mock-strategy-guide
Guides users on creating mock implementations for testing with traits, providing test doubles, and avoiding tight coupling to test infrastructure. Activates when users need to test code with external dependencies.
mcp-transport-guide
Understand MCP transport mechanisms - stdio, SSE, HTTP streaming, and custom transports
mcp-tool-creation
Master creating MCP tools with type-safe parameters, automatic schema generation, and best practices
mcp-resources-guide
Implement MCP resources that provide data and files to AI assistants - URIs, caching, and streaming
mcp-server-best-practices
Production-ready patterns and best practices for MCP servers - architecture, security, performance, and maintenance
let-chains-advisor
Identifies deeply nested if-let expressions and suggests let chains for cleaner control flow. Activates when users write nested conditionals with pattern matching.
lambda-optimization-advisor
Reviews AWS Lambda functions for performance, memory configuration, and cost optimization. Activates when users write Lambda handlers or discuss Lambda performance.
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