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Found 858 Skills
Guide for selecting and executing the correct pytest suites (unit, integration, redis, R2, routing rules, magic link) with environment setup and coverage expectations.
React Native and Expo patterns for navigation, data fetching lifecycle, infinite scroll lists, form handling, state persistence, authentication routing, gesture-driven animations, bottom sheets, push notifications, and OTA updates. Use when building Expo/React Native apps that need screen-level data prefetching, auth guards with protected routes, infinite scroll feeds, native form input handling, offline-capable state persistence, platform-specific setup (focus/online managers), fluid animations and gesture interactions, modal bottom sheets, push notification flows, or over-the-air update strategies. Do not use for React web apps.
Mailgun (Sinch) platform help — developer-first transactional email API and SMTP relay with inbound routing, webhooks, and Mailgun Optimize deliverability tools. Use when sending transactional email via Mailgun API or SMTP, configuring domains or DNS (DKIM/SPF), setting up inbound email routing, managing webhooks or templates, using Mailgun Optimize for inbox placement testing, or working with the Mailgun REST API. Do NOT use for general email deliverability strategy (use /sales-deliverability), cross-platform email marketing (use /sales-email-marketing), or email open/click tracking strategy (use /sales-email-tracking).
Reference skill for Zoom RTMS. Use after routing to a live-media workflow when processing real-time audio, video, chat, transcripts, screen share, or contact-center voice streams.
Reference skill for Zoom Apps SDK. Use after routing to an in-client app workflow when building web apps that run inside Zoom meetings, webinars, the main client, or Zoom Phone.
Artifact status + multi-phase orchestration. Scan what exists, check freshness, compose and track complex workflows across sessions. Not for skill routing (the agent does that proactively).
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).
Describe what an existing SigNoz alert rule does in plain language — the signal it watches, the threshold and evaluation behavior, the notification routing, and a one-line fire-frequency summary so the user knows whether the alert has been active. Make sure to use this skill whenever the user asks "what does this alert do", "explain alert X", "walk me through this rule", "how does my [Y] alert work", "is this alert configured correctly", or otherwise asks for an interpretation of an existing alert's configuration. Static explanation only — for diagnosing a specific firing incident, use `signoz-investigating-alerts`.
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.
Use when reviewing or rebalancing direct vs. partner-led channel economics — computing fully-loaded cost-to-serve per channel, channel ROI with cash / LTV / marginal lenses, and optimal channel mix subject to constraints. For Head of Commercial, RevOps, and VP Sales doing quarterly channel review when pipeline is mixed (e.g., 60% direct + 40% partner-led) and nobody actually knows which channel makes money after CAC, support load, partner discount, deal-velocity differences, retention differential, and overhead allocation are all loaded in. Outputs cost to serve, channel ROI verdicts (DOUBLE-DOWN / MAINTAIN / DEFUND / EXIT), a sensitivity-tested channel-mix recommendation, and the diminishing-returns inflection. Not channel structure (that's partnerships-architect — tiers, joint GTM, revshare). Not RevOps process (that's business-growth/revenue-operations — lead routing, SDR motion). Not strategic CRO judgment (that's c-level-advisor/cro-advisor — comp plans, when-to-hire-a-VP-Sales). Not historical close-and-report (that's finance/financial-analysis). This skill answers: direct vs partner profitability, channel profitability, channel mix, channel economics.
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.