Results for “domains”

Design and implement Monte Carlo methods for uncertainty quantification, risk analysis, and probabilistic simulations across scientific and financial domains. Use when "monte carlo, random sampling, uncertainty quantification, risk analysis, stochastic simulation, MCMC, variance reduction, probabilistic, " mentioned.

Deploys applications to a self-hosted Platform-as-a-Service using Dokku, supporting buildpacks, Dockerfiles, custom domains, and SSL via Let's Encrypt.

Plan backend domains, services, jobs, integrations, and failure handling for a product or major feature. Use when UX and product scope are defined and the team needs a production-minded backend design without defaulting to premature microservices.

Manage Oracle Cloud Infrastructure Internet of Things Platform resources: domains, digital twin models, adapters, instances, relationships, and device publish flows. Includes discovery, inspection, safe lifecycle operations, and troubleshooting.

Adds an iOS App Clip target to an Expo project, including configuration of bundle identifiers, associated domains, AASA file hosting, and TestFlight submission.

Wix business solution management recipes — REST API operations for configuring and managing Wix business solutions. Routes to: stores, bookings, get-paid, CMS, contacts, forms, media, app-installation, pricing-plans, restaurants, rich-content, sites, blog, calendar, domains, site-properties, ecommerce.

Discovers and uses scientific datasets, models, blog posts, and interactive demos from a curated catalog for AI/ML work in domains like biology, chemistry, physics, and genomics.

Use when symbolic mathematics must cross into NumPy vector evaluation or SciPy numerical algorithms: lambdify contracts, domains, dtypes, parameters, residuals, tolerances, and symbolic-versus-numeric verification. Do not use for work confined entirely to one of those libraries.

Analyze proxy logs, DNS query logs, and netflow data to detect unauthorized SaaS and cloud service usage, classify domains, and flag high-risk services.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Enumerate DNS records, attempt zone transfers, brute-force subdomains, and map DNS infrastructure during authorized reconnaissance to identify attack surface, misconfigurations, and information disclosure in target domains.

Detect brand impersonation attacks across domains, social media, mobile apps, and dark web channels to identify phishing campaigns, fake sites, and unauthorized brand usage.

Automates multi-source enrichment of IPs, domains, URLs, and file hashes using VirusTotal, AbuseIPDB, Shodan, GreyNoise, URLScan.io, and MISP to provide contextual risk scoring and disposition recommendations for SOC analysts.

Discover and exploit broken link hijacking vulnerabilities by identifying references to expired domains, decommissioned cloud resources, and dead external services that can be claimed by an attacker.

Monitor and map adversary-controlled assets including C2 servers, phishing domains, and exploit kit hosts using passive DNS, certificate transparency logs, Shodan/Censys scanning, WHOIS analysis, and network fingerprinting.

Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.

Triages and enriches indicators of compromise (IPs, domains, file hashes, URLs, email artifacts) from phishing emails, security alerts, or threat feeds, assigning confidence scores and dispositions using VirusTotal, AbuseIPDB, MalwareBazaar, and MISP.

Queries Certificate Transparency logs via crt.sh and pycrtsh to detect phishing domains, unauthorized certificate issuance, and shadow IT. Monitors newly issued certificates for typosquatting and brand impersonation using Levenshtein distance.

Use when the user asks how to build with OpenAI products or APIs and needs current official documentation with citations, including Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities, limits, or migrations; prioritize an available official OpenAI documentation connector and restrict fallback browsing to official OpenAI domains.

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

Domain-Driven Design patterns. Bounded contexts, aggregates, entities, value objects, domain events, repositories, and application services. Strategic and tactical DDD for complex business domains. USE WHEN: user mentions "DDD", "Domain-Driven Design", "bounded context", "aggregate", "value object", "domain event", "ubiquitous language", "aggregate root", "domain service" DO NOT USE FOR: database schema design - use database skills; CQRS/Event Sourcing specifics - use `event-sourcing-cqrs`

Layer 1b of the keyword research pipeline. Finds keyword opportunities by comparing the brand's blog against competitors AND by expanding seeds + modifiers via Semrush (phrase_fullsearch / phrase_related). Auto-discovers competitors via domain_organic_organic when none are provided, derives the keyword gap via domain_domains, tags every row with `gap_mode`, and outputs a candidate-keyword CSV ready for downstream BID/AIO vetting.

Comprehensive post-build verification of Shannon Framework's application outputs using three-layer methodology: Flow Verification (execution trace analysis), Artifact Verification (physical output inspection), and Functional Verification (runtime testing). Verifies Shannon built production-ready applications across all domains (Frontend, Backend, Database, Mobile, DevOps). Ensures NO MOCKS compliance, cross-platform functionality, and complete integration. Use after: Shannon builds any application via /shannon:wave, need to verify build quality, production readiness assessment.

Install, configure, and operate Strix for AI-driven application security testing. Use when you need to run authorized vulnerability scans against local codebases, GitHub repositories, staging URLs, domains, or CI pipelines; configure Docker and LLM providers; choose quick, standard, or deep scan depth; or pass authenticated testing instructions to Strix. Triggers on: strix, ai pentest, vulnerability scan cli, appsec scan, bug bounty automation, strix ci, strix docker, strix scan mode, strix instruction file, headless security scan.

Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

Expert knowledge for Azure App Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing App Service plans/ASE, configuring VNet/custom domains/certs, CI/CD slots, or managed identity access, and other Azure App Service related development tasks. Not for Azure Functions (use azure-functions), Azure Spring Apps (use azure-spring-apps), Azure Static Web Apps (use azure-static-web-apps), Azure Service Fabric (use azure-service-fabric).

Interactive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.

Interactive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.

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.

Provide direct REST API access to UniProt (Swiss-Prot/TrEMBL) for protein sequence searches, FASTA retrieval, functional annotations (GO terms, domains), and cross-database ID mapping. Use when looking up a protein entry, fetching a protein FASTA sequence, or mapping accessions between databases over raw HTTP/REST; for EXPERIMENTAL 3D structures prefer alterlab-pdb, for AI-PREDICTED 3D structures prefer alterlab-alphafold-db, for protein-protein interaction networks prefer alterlab-string-db, and for Python workflows spanning many databases prefer bioservices instead. Part of the AlterLab Academic Skills suite.

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.

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.