'functions' 검색 결과

Query STRING for protein interaction networks, interaction partners, and enrichment results showing functions common in a gene set. Receive concise summaries with limited result counts, with options for machine-readable JSON or saved full results.

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• Checking a protein’s interaction network
• Finding interaction partners for a protein
• Reviewing functions enriched in a gene set

Test whether an AI agent reveals another user's data or performs actions beyond the caller's permissions. Dialogue probes cover access across users or organizations, admin functions, and attempts to gain higher privileges without authorization.

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• Checking access to other users' or organizations' data
• Testing permission checks on admin-only actions
• Checking whether dialogue claims grant higher privileges

Review Apache Pinot changes for correctness in null handling, numeric types, type-based processing, and resource lifecycles. Use it to examine changes to calculations, aggregations, window functions, and segment cleanup or persistence.

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• Reviewing changes to null handling
• Checking branches based on data or index types
• Reviewing numeric processing in aggregations and window functions

Check code for complex logic, duplication, naming issues, and oversized functions. Get a quality report with file locations and severity levels, plus guidance on splitting complex code and extracting shared logic during reviews or after refactoring.

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• Checking code quality before a commit
• Reviewing code after refactoring
• Finding complex functions and duplicated code

Generate README.md and DESIGN.md outlines by inspecting a module's files, language, and dependencies. Python analysis also extracts classes, functions, and docstrings, providing a starting point for usage documentation and design records that you complete afterward.

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• Create documentation outlines for a new module
• Add missing README.md and DESIGN.md files
• Summarize Python classes and functions

Provides coding conventions for TypeScript, JavaScript, React, and Node.js. It guides naming, type safety, error handling, API structure, and tests, with examples for simplifying complex functions and keeping code consistent and maintainable.

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• Aligning coding conventions across a project
• Simplifying long functions and deeply nested logic
• Standardizing API responses and input validation

Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.

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• Analyzing experimental CSV data and saving plots
• Solving matrix equations and differential equations
• Analyzing signal frequencies and applying filters

Interpret statistical models in R and Python using predictions, comparisons, and marginal effects, which describe how outcomes change with predictors. Get guidance on choosing quantities, evaluating scenarios, summarizing groups, measuring uncertainty, and testing hypotheses.

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• Calculate predicted probabilities for chosen conditions
• Estimate how outcomes change with a predictor
• Compare treatment effects across groups

Work through continuity problems in real analysis by checking function values and limits. The skill outlines separate checks for piecewise functions and provides commands for limit calculations with SymPy and proof checks with Z3.

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• Checking whether a function is continuous at a point
• Comparing left and right limits of piecewise functions
• Comparing a computed limit with the function value

Work through integration problems in measure theory by matching the problem's conditions to suitable theorems. The skill covers simple functions, exchanging limits and integrals, lower bounds, and changing integration order, with commands for symbolic calculations and proof tools.

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• Choosing a theorem for exchanging limits and integrals
• Checking conditions for changing integration order
• Finding lower bounds with Fatou's lemma

Investigate whether a complex function is analytic, meaning it has a local power series representation. The skill guides derivative checks, Cauchy–Riemann equations, power series and convergence radii, plus ways to explore extension beyond the original domain.

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• Checking where a complex function is analytic
• Verifying the Cauchy–Riemann equations
• Finding a power series and its convergence radius

Search a codebase with WarpGrep to find functions, variables, imports, and other text. Use regular expressions to match text patterns, limit searches to a directory, or replace the contents of a specified file.

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• Finding authentication code
• Locating functions with regular expressions
• Checking imports in a source directory

Find the right tool for equations, calculus, numerical calculations, proof checks, or math practice. The guide connects tasks to commands for computing results, verifying steps, requesting hints, and plotting functions.

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• Solving equations and checking the answers
• Learning with practice problems and progressive hints
• Choosing tools for proofs and constraint problems

Guides authorized source code security reviews across dangerous functions, data flow, authentication, logic flaws, and dependencies. Helps agents prioritize review paths and organize code locations, reproduction evidence, and remediation advice before drawing conclusions.

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• Prioritizing entry points in a large codebase
• Tracing user input to dangerous functions
• Reviewing authentication, logic, and dependency risks

Study the mathematics behind computer science through a structured learning path and practice problems. Covers logic, proofs, sets, functions, counting, and number theory, connecting these foundations to algorithm correctness and computational thinking.

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• Learning the mathematics behind computer science
• Practicing induction and proof by contradiction
• Reviewing sets, functions, and counting

Look up how genetic differences affect medication response using ClinPGx. Retrieve gene–drug relationships, clinical guidelines, genetic variant functions, and drug labels to gather evidence for research and reviews of genotype-based dosing recommendations.

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• Investigate gene–drug relationships
• Review guidelines for genotype-based dosing
• Compare genetic variant frequencies across populations

Wrap existing tool functions and language model calls with NeMo Relay to record execution events, process requests through middleware, and apply guardrails. Guides integration while preserving the original functions’ arguments, results, and business logic.

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• Adding execution events to existing tool calls
• Wrapping model provider calls with NeMo Relay
• Integrating request processing and execution guardrails

Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.

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LoongCollector E2E Testing Framework Development Guide. Use this rule when you need to understand E2E testing framework architecture, develop new test behavior functions, or extend testing capabilities. Contains detailed BDD testing framework usage, test behavior function development and registration workflows, and comprehensive reference of existing testing capabilities. Example: When adding custom test behaviors for new plugins, use this rule to understand how to write and register new test functions. Example: When developing E2E tests, use this rule to understand the e2e framework.

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Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

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Assess and migrate cross-cloud workloads to Azure with reports and code conversion. Supports Lambda→Functions, Beanstalk/Heroku/App Engine→App Service, Fargate/Kubernetes/Cloud Run/Spring Boot→Container Apps. WHEN: migrate Lambda to Functions, AWS to Azure, migrate Beanstalk, migrate Heroku, migrate App Engine, Cloud Run migration, Fargate to ACA, ECS/Kubernetes/GKE/EKS to Container Apps, Spring Boot to Container Apps, cross-cloud migration.

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Convex backend development guidelines. Use when writing Convex functions, schemas, queries, mutations, actions, or any backend code in a Convex project. Triggers on tasks involving Convex database operations, real-time subscriptions, file storage, or serverless functions.

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Modernize removed or discouraged MATLAB functions and patterns. Use when the check_matlab_code MCP tool reports that a function "is not recommended", "will be removed in a future release", or "has been removed" — at any severity (info, warning, or error) — when migrating legacy code, or when replacing removed or discouraged APIs with current equivalents. Also applies to discouraged patterns Code Analyzer does not flag (containers.Map, bsxfun, str2num, eval, clear all) and to removed or discouraged functions such as trainNetwork, csvread, xlsread, datenum, guide, svmtrain, ltln2val, webmap.

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Generate correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form, extracting PDE coefficients (pdeCoefficients), symbolic-to-C code generation (matlabFunction + codegen), symbolic matrix variables (symmatrix), physical units and constants (symunit, newUnit), or rewriting/combining algebraic expressions (rewrite, combine).

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