Build and train reinforcement learning agents, which learn actions through rewards. The skill guides custom environment creation, parallel simulation tuning, integration with existing environments, and policy development for image or sequential observations.
Turn existing logs, measurements, or user reports into a prioritized performance improvement plan. It ranks frontend, backend, database, and infrastructure changes by expected impact and effort, and suggests validation steps for checking whether improvements worked.
Analyze propositional formulas and arguments using truth tables, deduction rules, and branching proof trees. Classify formulas as always true, always false, or dependent on their inputs, and check whether an argument follows from its premises.
Build tests with fast-check for TypeScript and JavaScript or Hypothesis for Python. Generate varied inputs to check rules code should always satisfy, discover edge cases, and simplify failing inputs into small examples for debugging.
Find and retrieve AI prompt templates through prompts.chat, filtering by keywords, categories, tags, or media type. Improve existing prompts for text, image, video, or sound tasks, with an explanation of the changes.
Implement filtering, aggregation, joins, and file processing with Polars for tabular data that fits in memory. Use lazy evaluation, which optimizes operations before running them, and parallel execution to improve pipelines and migrate pandas code.
Create interactive charts in Python for exploring scientific, statistical, geographic, and financial data. Choose quick plotting or detailed customization, add hover details and zoom controls, and export results as interactive HTML or static images.
Search current web information and recent scientific literature with Perplexity through OpenRouter and LiteLLM. Get answers with source citations, choose models for different research needs, and save results as JSON for further use.
Estimate intervention effects from existing data using causal analysis methods. Set up models, compare observed outcomes with estimates of what would have happened without intervention, check robustness, and interpret fitted results.
Handle equations, calculus, matrices, unit conversions, and mathematical explanations through one entry point. The skill selects a suitable calculation tool or topic guide, including symbolic math tools that work directly with expressions.
Calculate defined business, statistical, or quality metrics from available data and test results. It provides formulas and reproducible calculation steps, with checks on units, denominators, and which records are included in each calculation.
Keeps existing requests for OpenAI documentation guidance working by directing them to the local openai-docs skill. It reuses the canonical instructions, references, and scripts while preserving compatibility with older callers.
Deploy web projects to Netlify with its command-line tool. The skill checks authentication and project settings, links or creates a site, and publishes a preview or production version with deployment URLs and log links.
Choose a mathematical approach for a problem you do not yet know how to model. Guided questions clarify change, uncertainty, optimization goals, and required precision, leading to a recommended framework, starting equations, tools, and related skills.
Work through optimization problems using gradients, which indicate how a function changes. The skill guides method selection, step size choices, and convergence checks, with SciPy examples for conjugate gradient and BFGS methods.
Handle review and discussion comments on the open GitHub pull request for the current branch. Collect comments, number and summarize the requested changes, then apply fixes for the comments the user chooses using the GitHub CLI.
Work with geographic points, lines, and areas in Python. Read spatial files, combine datasets by location, calculate areas and distances, transform coordinate systems, and export results as maps or converted files.
Use LaminDB to organize biological datasets, search their metadata, and track how analyses produce results. Standardize annotations with biological vocabularies, validate data structures, and record versions and computational history for reproducible research.
Retrieves aggregated hedge fund time series from the public OFR API for research on industry size, leverage, counterparty concentration, and liquidity. It supports finding datasets, comparing series, and downloading data without an API key.
Retrieve company filings from the SEC, the US securities regulator, using Python. Extract financial statements, insider transactions, and institutional holdings to research companies and compare financial data across reporting periods.
Find the right code for Roo Code evaluations, which test AI coding tasks. This skill explains the execution system, management interface, and public results page, helping agents identify where changes and debugging work belong.
Implement, customize, and debug legends in G2 charts with guidance on rendering, layout calculations, and user interactions. Understand category and continuous legends, adjust their appearance, and investigate sizing or filtering problems.
Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.
Search the European Nucleotide Archive for DNA and RNA sequences, raw sequencing reads, and assembled genomes. Retrieve records by accession, a unique record identifier, or filter by research metadata, then download data for analysis workflows.
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