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Found 1,476 Skills
Interacts with Google Cloud services using the gcloud CLI safely and efficiently. Covers command validation, data reduction, safety guardrails with a denylist, and workflows for discovery and investigation. You MUST read this skill before invoking any gcloud command. Use when managing cloud resources, querying configurations, or troubleshooting issues via gcloud. Don't use when writing or debugging Google Cloud client library code or raw REST/gRPC API interactions.
Construcción y optimización cuantitativa de portafolios: Markowitz (scipy.optimize + Monte Carlo), Black-Litterman (prior CAPM, views absolutas/relativas, posterior bayesiano), HRP/HERC/NCO (clustering jerárquico, risk parity, NCO con restricciones). Todo flat numpy + scipy, sin Riskfolio-Lib ni PyPortfolioOpt.
Recover a suppressed, blocked, or deactivated Amazon listing. Maps the suppression reason code to the specific fix path and produces the reinstatement message. Use when a user asks about a suppressed listing, a blocked listing, listing deactivated, pricing error suppression, image policy, restricted phrase, or missing required field. Trigger phrases: "suppressed listing", "blocked listing", "listing deactivated", "pricing error", "image suppression", "restricted phrase". Works with zero tools. the user pastes the suppression notice.
Check an Amazon product for compliance and safety requirements before and after listing. Covers required certifications, labeling and warning requirements, restricted-substance rules, documentation Amazon may demand, and category-specific safety rules. Use when a user asks about product compliance, safety requirements, certifications, required labels or warnings, a compliance document request from Amazon, or whether a product is allowed. Trigger phrases: "product compliance", "safety requirements", "certifications", "compliance documents", "required labels", "is this product allowed". Works with zero tools.
This skill should be used when the user wants to manage Railway deployments, view logs, or debug issues. Covers deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes, why deploy failed). NOT for deleting services - use environment skill with isDeleted for that.
Build Shopify applications, extensions, and themes using GraphQL/REST APIs, Shopify CLI, Polaris UI components, and Liquid templating. Capabilities include app development with OAuth authentication, checkout UI extensions for customizing checkout flow, admin UI extensions for dashboard integration, POS extensions for retail, theme development with Liquid, webhook management, billing API integration, product/order/customer management. Use when building Shopify apps, implementing checkout customizations, creating admin interfaces, developing themes, integrating payment processing, managing store data via APIs, or extending Shopify functionality.
Manage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes). NOT for deleting services - use railway-environment skill with isDeleted for that.
Use when building Django web applications or REST APIs with Django REST Framework. Invoke for Django models, ORM optimization, DRF serializers, viewsets, authentication with JWT.
Identify and debug performance regressions from code changes. Use comparison and profiling to locate what degraded performance and restore baseline metrics.
This skill provides comprehensive instructions for interacting with the Raindrop.io bookmarks service via its REST API using curl and jq. It covers authentication, CRUD operations for collections, raindrops (bookmarks), tags, highlights, filters, import/export, and backups. Use this skill whenever the user asks to work with their bookmarks from Raindrop.io, including reading, creating, updating, deleting, searching, or organising bookmarks and collections.
Guidance for setting up HuggingFace model inference services with Flask APIs. This skill applies when downloading HuggingFace models, creating inference endpoints, or building ML model serving APIs. Use for tasks involving transformers library, model caching, and REST API creation for ML models.
Use when investigating or improving WordPress performance (backend-only agent): profiling and measurement (WP-CLI profile/doctor, Server-Timing, Query Monitor via REST headers), database/query optimization, autoloaded options, object caching, cron, HTTP API calls, and safe verification.