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Found 1,327 Skills
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Vercel security and access controls including RBAC, SSO, deployment protection, firewall, bot defense, audit logs, and 2FA. Use when securing Vercel projects or managing access.
Provides foundational knowledge about GuaraCloud PaaS platform — projects, services, deployments, tiers, build methods, and CLI installation and authentication. Use when the user mentions GuaraCloud, asks about platform concepts, or needs to set up the CLI.
Use this skill when users need to create, generate, modify, or validate Salesforce Lightning pages (FlexiPages). Trigger when users mention RecordPage, AppPage, HomePage, Lightning pages, page layouts, adding components to pages, or page customization. Also use when users say things like 'create a Lightning page', 'add a component to a page', 'customize the record page', 'generate a FlexiPage', or when they're working with FlexiPage XML files and need help with components, regions, or deployment errors. Always use this skill for any FlexiPage-related work, even if they just mention 'page' in the context of Salesforce.
Official Salesforce documentation retrieval skill. Use when you need authoritative Salesforce docs from developer.salesforce.com, help.salesforce.com, architect.salesforce.com, admin.salesforce.com, or lightningdesignsystem.com, especially when pages are JS-heavy, shell-rendered, or hard to extract with naive fetching. Use to ground answers in official Salesforce sources instead of third-party blogs or summaries. TRIGGER when: user asks for official Salesforce documentation, Apex or API reference, LWC docs, Agentforce docs, setup or help articles, or any doc from a Salesforce-owned domain. DO NOT TRIGGER when: user is asking for a code change, deployment task, or anything not requiring documentation retrieval — use the appropriate sf-* skill instead.
Start Here. Use when the user asks about Narev Cloud, the Pricing API, model pricing (API reference skill vs applied workflows on top of that API), live LLM pricing, token costs, cost calculation, pinning or snapshotting model rates, Narev SDK, @ai-billing/core, provider middleware packages, Vercel AI SDK billing, Next.js App Router route handlers, framework-specific billing patterns, usage-based billing, billing integrations (Polar, Stripe, Lago, OpenMeter), FOCUS format, Narev Self-Hosted (ThinOps), deployment, COGS, customer tagging, FinOps for AI, or this documentation site. Guides you to the right skill or documentation path based on their task.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.
Deploy and manage web apps using Azure App Service with auto-scaling, deployment slots, SSL/TLS, and monitoring. Use for hosting web applications on Azure.
Infrastructure as Code using Terraform with modular components, state management, and multi-cloud deployments. Use for provisioning and managing cloud resources.
Create a Next.js app running on Bun, configure the development environment, and deploy to Vercel with automatic deployments on push.
Expert in building scalable ML systems, from data pipelines and model training to production deployment and monitoring.