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Found 1,662 Skills
Grounding an assistant in your app with assistant-ui copilots (@assistant-ui/react). Use when steering assistant behavior with useAssistantInstructions, feeding lazy app-state context via useAssistantContext({ getContext }), exposing rendered components with makeAssistantVisible(Component, { clickable, editable }), building two-way interactable state with useAssistantInteractable and Interactables(), or registering instructions and tools imperatively through useAui().modelContext().register({ getModelContext }). Reach for this when the assistant should read the current page, click or edit UI, or read and update component state through auto-generated update_{name} tools. For LLM tools and tool-call UI use the tools skill; for runtime and thread state use the runtime skill.
Use when creating or revising model PR optimization history documents for SGLang, vLLM, or another serving framework that cite GitHub PRs. Requires manual, per-PR source-diff review and documentation of motivation, key implementation approach, most important code excerpts, reviewed files, and validation implications instead of generated or one-line summaries.
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) strategies for AI-powered search visibility in ChatGPT, Perplexity, Google AI Overviews, and other AI search platforms. Use when working with aeo, geo, ai search, chatgpt search, perplexity, ai overviews, generative search, llm visibility.
Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".
The essential mental models for building onchain — focused on what LLMs get wrong and what humans need explained. "Nothing is automatic" and "incentives are everything" are the core messages. Use when your human is new to onchain development, when they're designing a system, or when they ask "how does this actually work?" Also use when YOU are designing a system — the state machine + incentive framework catches design mistakes before they become dead code.
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For parasite SEO strategy, use parasite-seo. For GitHub, use github-seo.
Use this when you need to evaluate the risks and benefits of accepting, negotiating before accepting, pausing, or rejecting outsourcing projects, internal projects, or requirements. It is particularly suitable for scenarios with ambiguity in scope, acceptance criteria, payment terms, compliance, project timelines, or dependencies, as well as high-uncertainty situations such as emergency task insertion, contract renewal/modification, multi-requirement prioritization, or AI/LLM-related initiatives.
Apply Actor-Network Theory (Latour, Callon) to trace how human and non-human actors (actants) form networks through translation processes. Use this skill when the user needs to map sociotechnical assemblages, analyze how innovations stabilize or fail through network-building, trace the four moments of translation (problematization, interessement, enrollment, mobilization), or when they ask 'how did this technology become accepted', 'who and what holds this network together', or 'why did this innovation fail to gain traction'.
Use when managing Xiaohongshu shop daily operations, handling inventory and orders, processing customer purchases, coordinating shipping and fulfillment, or planning shop promotions and activities
NestJS reference skill: modules, controllers, providers, DTOs with class-validator, TypeORM/Prisma, guards, interceptors, pipes, queues (BullMQ), WebSockets, microservices, testing, OpenAPI, and CLI scaffolding. Use when the task touches NestJS application code and should follow the project's module-based architecture.
Enthu.AI platform help — contact center conversation intelligence with auto QA scorecards, agent coaching, compliance monitoring, and speech analytics. Use when setting up Enthu.AI QA scorecards for call center agents, calls not being scored or transcribed correctly, agents not seeing coaching insights from their calls, Enthu.AI integration with Aircall or RingCentral not syncing, comparing Enthu.AI vs Gong or CallMiner for contact center QA, or configuring sentiment analysis and keyword tracking. Do NOT use for building a general coaching program (use /sales-coaching) or reviewing a specific call transcript (use /sales-call-review).
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.