Data & AI skills

Integrate PostHog analytics into Expo apps. Use for posthog-react-native setup, providers, screen tracking, env config, identify calls, and error tracking.

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Optimize NowStack Mobile across Convex, Expo, and TanStack Start. Use for performance, state, subscriptions, rendering, routing, forms, mobile UI, bundle, or data-fetching improvements.

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Summarize any block of text using the `summarize` CLI (chained-in from steipete/summarize — npm package `@steipete/summarize`). Use whenever the user pastes a long piece of content and asks for a TL;DR, a summary, a digest, a gist, a condensed version, or key points — even if they don't explicitly say "use summarize". Also chains into `mktg-transcribe --summarize` for audio/video TL;DRs and source-digestion workflows to condense long articles. Requires the `summarize` CLI to be installed (`mktg doctor` will tell you if it's missing). Do NOT trigger for structured data (JSON/CSV/code), for text already shorter than ~500 words, or when the user wants the full content preserved.

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Use when the user wants professional brand-quality product images via Higgsfield's mode-specific prompt enhancement pipeline. Entry point for any product visual with a specific format or platform target. Use whenever: "product photoshoot", "lifestyle product shots", "Pinterest pin", "hero banner", "ad pack", "virtual try-on", "studio shot", "carousel images", "Meta ads creative", "model wearing product", "levitating product", "splash shot", "CGI style product", "restyle product image", Shopify image, brand campaign visual. 10 modes: product_shot, lifestyle_scene, closeup_product_with_person, moodboard_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the prompt — never call gpt_image_2 directly for product shots. NOT for: one-off images without a product (use image-gen), branded video with avatars (use higgsfield-generate), Soul training (use higgsfield-soul-id). Requires Higgsfield CLI and authed account.

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Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".

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Enhance an existing marketing plan with parallel research. Use when the agent has a draft plan (brand strategy, campaign brief, content calendar, launch plan) that needs strengthening with real data. Triggers on 'deepen this plan', 'strengthen this strategy', 'research gaps in my plan', 'make this plan better', 'this plan is too surface level', 'add research to this', 'validate this plan', 'back this up with data', or when a plan exists but lacks audience data, competitive positioning, or keyword strategy. Make sure to use this whenever someone has an EXISTING plan that feels thin or unresearched — even if they just say 'is this plan good enough?' or 'what's missing here?', they likely need deepening. This skill ENHANCES existing plans — it does not create new ones. If no plan exists, route to /brainstorm or /launch-strategy first.

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Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the agent already has URLs. Prefer Exa MCP web_fetch_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for open-ended discovery (use exa-search) or auth-walled X/Twitter (use mktg-x). For full-site crawl of a known domain, firecrawl may be better.

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Build ML pipeline orchestration systems. TRIGGERS - Use when user needs help with ai-pipeline-orchestrator related tasks.

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Build marketplaces for AI models and prompts. TRIGGERS - Use when user needs help with ai-marketplace-builder related tasks.

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Design feature stores for ML systems. TRIGGERS - Use when user needs help with ai-feature-store related tasks.

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Guide AI model fine-tuning with data preparation. TRIGGERS - Use when user needs help with ai-fine-tuning-guide related tasks.

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Build data privacy systems for AI. TRIGGERS - Use when user needs help with ai-data-privacy-system related tasks.

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Build data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.

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Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan related tasks.

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Curate and prepare datasets for AI training. TRIGGERS - Use when user needs help with ai-dataset-curator related tasks.

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Build entity extraction systems for structured data from text. TRIGGERS - Use when user needs help with ai-entity-extractor related tasks.

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Build AI data extraction pipelines. TRIGGERS - Use when user needs help with ai-extraction-pipeline related tasks.

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Build AI data deduplication systems. TRIGGERS - Use when user needs help with ai-deduplication related tasks.

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Optimize embedding strategies for AI retrieval. TRIGGERS - Use when user needs help with ai-embedding-optimizer related tasks.

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Build AI prompt chains and multi-step workflows for complex tasks. TRIGGERS - Use when user needs help with ai-chain-builder related tasks.

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Design analytics for AI chatbot performance. TRIGGERS - Use when user needs help with ai-chatbot-analytics related tasks.

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Build AI data cleaning systems. TRIGGERS - Use when user needs help with ai-data-cleaner related tasks.

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Build text and data classification systems with training and evaluation. TRIGGERS - Use when user needs help with ai-classification-system related tasks.

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Build AI conversation analytics systems. TRIGGERS - Use when user needs help with ai-conversation-analyzer related tasks.

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