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Found 578 Skills
MUST activate before editing ANY file under uiBundles/*/src/ for visual or UI changes to an EXISTING app — pages, components, sections, layout, styling, colors, fonts, navigation, animations, or any look-and-feel change. Use this skill when modifying pages, components, layout, styling, or navigation in an existing UI bundle app. Activate when the project contains appLayout.tsx, routes.tsx, src/pages/, src/components/, or global.css. This skill contains critical project-specific conventions (appLayout.tsx shell, shadcn/ui components, Tailwind CSS, Salesforce base-path routing, module restrictions) that override general knowledge. Without this skill, generated code will use wrong imports, break routing, or ignore project structure. Do NOT use when creating a new app from scratch (use building-ui-bundle-app instead).
Genesys Cloud CX platform help — enterprise CCaaS with AI-powered experience orchestration, omnichannel ACD routing (voice + digital), Architect IVR/flow builder, workforce management (WFM forecasting/scheduling/adherence), quality management (evaluations/scoring), predictive routing, agent assist, virtual agents, outbound dialer, Interaction Analytics, AppFoundry marketplace (450+ apps), REST Platform API with OAuth 2.0 and 15 regional endpoints, deep Salesforce integration (CX Cloud joint product + Service Cloud Voice BYOT), 4 tiers CX1 $75/CX2 $115/CX3 $155/CX4 $240 per user/mo + telephony minutes. Use when setting up Genesys Cloud routing or Architect flows, WFM forecasting not matching actual volume, quality management evaluations not triggering coaching, dropped calls or audio quality issues, comparing Genesys pricing tiers, integrating Genesys with Salesforce or ServiceNow, Genesys reporting hard to navigate, MFA management confusing, Genesys API integration, or evaluating enterprise CCaaS platforms. Do NOT use for building a general coaching program (use /sales-coaching) or comparing CCaaS platforms (use /sales-ccaas-selection).
PostHog integration for React applications using TanStack Router with file-based routing
This skill should be used when designing Make scenarios, choosing which modules to use, composing module flows, setting up routing/branching/filtering/iterations/aggregations, building blueprints, deploying scenarios, handling errors, configuring scheduling and triggers, or discussing scenario architecture. Covers WHICH modules to use and WHY — complementary to make-module-configuring which covers HOW to configure each module.
Guide identification, measurement, and management of operational risk in trading and brokerage operations. Use when designing trade error detection and correction procedures, investigating trade breaks and reconciliation failures, classifying loss events under Basel taxonomy, developing key risk indicators (KRIs) and dashboards, responding to system outages or data feed failures or order routing errors, conducting root cause analysis after a trade error or settlement fail, planning business continuity and disaster recovery for trading desks, preparing for FINRA or SEC operational risk examinations, or assessing technology risk in OMS and market data systems. Also covers fat-finger errors, error account P&L, and corrective action tracking.
Connects NemoClaw to a local inference server. Use when setting up Ollama, vLLM, TensorRT-LLM, NIM, or any OpenAI-compatible local model server with NemoClaw. Trigger keywords - nemoclaw local inference, ollama nemoclaw, vllm nemoclaw, local model server, openai compatible endpoint, switch nemoclaw inference model, change inference runtime, nemoclaw additional model, nemoclaw sub-agent model, openclaw sub-agent, agents.list, sessions_spawn, vlm-demo, nemoclaw tool calling, ollama tool calls, vllm tool-call-parser, raw json in tui, nemoclaw inference options, nemoclaw onboarding providers, nemoclaw inference routing.
Use whenever the user mentions LLM prompt/prefix cache misses, cached_tokens=0, cache_read_input_tokens/cache_creation_input_tokens, prompt_cache_key, cache_control/cachePoint placement, stable prefixes, tool/schema stability, TTFT/prefill latency, OpenAI/Claude/Bedrock/OpenRouter routing, vLLM/SGLang KV reuse, or LLM cost/speed regressions on repeated long prompts. Use when reviewing LLM request shape changes: prompt text, message order, request builders, tools, schemas, response_format, provider API surface, model/router settings, agent loop structure, context compaction, or inference deployment. Use for speeding up agents only when prompt-cache stability, TTFT, or cache cost is central. Do not use for generic prompt writing, generic RAG design, token counting, or non-LLM performance.
Opinionated React and TypeScript conventions for JSX, hooks, routing, shared state, and query wiring. Use for style cleanups, refactors, review feedback, or code generation.
API Gateway patterns (Kong, Traefik, AWS API Gateway) — rate limiting, auth, routing, versioning. Use when implementing API gateway, reverse proxy, or API management.
Routes AWS networking requests to the correct service skill for implementation. Covers Route 53 (DNS, health checks, routing policies, Resolver, DNS Firewall), CloudFront (caching, edge, OAC, mTLS, signed URLs), Transit Gateway (multi-VPC hub, segmentation, centralized egress), Direct Connect (hybrid link, DX Gateway, MACsec), Site-to-Site VPN (IPsec tunnels, static or BGP), Network Firewall (stateful L3-L7 inspection, FQDN filtering, Suricata), WAF (web ACLs, AWS Managed Rules, rate-based rules, Bot and Fraud Control), and Shield Advanced (L3/L4 DDoS). Applicable when creating, configuring, troubleshooting, or designing across these services, choosing between them, or diagnosing connectivity or traffic-filtering issues. Not for VPC subnets and route tables, load balancers, VPC endpoints, PrivateLink, API Gateway, IAM policy logic, container or serverless networking, or IaC authoring.
Use when one Python service must send each agent's, tenant's, team's, or request's spans to its correct Arize space and project using application metadata. Covers dynamic OpenTelemetry routing for custom agent builders and multi-tenant applications, including register_with_routing, set_routing_context, multi-space tracing, and custom span routing.
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.