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Deep competitor analysis. Tear apart a specific competitor or compare multiple competitors. Use when the user names a competitor or asks "who else is doing this?"

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Create and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing.

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This skill covers two related use cases: (1) Creating high-converting webinar registration and event landing pages, and (2) Repurposing webinar recordings into multiple content assets including blog posts, social media, email sequences, and sales materials. Use when users need to build webinar pages or convert webinar content into multi-channel marketing materials.

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Aurora Smart Home orchestrator — routing layer for all smart home skills. Use this skill when the user asks ANY smart home question and you need to decide which skill to invoke, or when a task spans multiple skills (e.g., "build a sensor that shows on a dashboard and triggers automations"). Invoke aurora FIRST before reaching for a specific skill — it will route to the right specialist(s) and recommend the correct Claude model to keep token usage efficient. Trigger on: smart home, Home Assistant, ESPHome, automation, IoT, dashboard, ESP32, Node-RED, or any request about controlling or monitoring devices at home.

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The methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.

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Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to customer opportunities, possible solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when the team is unclear what to build next, when multiple opportunities compete, or before writing a PRD for a complex feature space.

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When multiple solutions exist, pick the best one. Explain why in one sentence.

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Help user work from multiple devices. Sync project via git.

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Tests whether data supports a claim: A/B significance, peeking, sample size, eval error bars, multiple comparisons. Use when deciding if results are significant.

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Deep research on a topic — use when investigating unfamiliar domains, answering complex questions requiring multiple sources, or when an evaluation flags something for deeper analysis

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Fetches YouTube video metadata and transcripts (genesis web_fetch MCP tool first, yt-dlp via Bash as the fallback). Activate when the user shares a YouTube URL (youtube.com, youtu.be), asks to 'fetch this video', 'get the transcript', 'what does this video say', 'summarize this YouTube video', or references video content that needs to be retrieved. Also activate when processing multiple YouTube URLs in batch. Do NOT use for non-YouTube video platforms, local video files, or audio-only podcast URLs.

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Evidence-driven web and open-source research for questions that require multiple sources, factual verification, comparisons, or an adopt/adapt/build decision. Use for substantial research in foreground sessions, the genesis-researcher subagent, and research-profile background sessions. Skip for a single stable fact or a known URL that only needs fetching.

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Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

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Use this skill when the user's prompt requires (1) researching a topic across multiple sources, comparing options or alternatives, analyzing trends or history, understanding markets or industries, or reviewing literature or studies and (2) synthesizing that research into a comprehensive, narrative report. If you're planning to search the web or internal knowledge bases, consider using this skill. This skill coordinates research subagents, so use it only when you have a tool for spawning subagents (the Agent or Task tool); otherwise, research the question directly.

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Harmonize multiple Blender skills into a coherent pipeline with clear activation precedence, handoff artifacts, dependency gates, and conflict-resolution rules. Use when a Blender task spans several skills, when reference-locked work conflicts with generic production workflow, after adding/updating skills, or when repeated failures indicate skill interference or missing inter/intra-play.

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Design and validate texture-state transitions for Blender animations without ugly whole-image crossfades, texture popping, misregistered morphs, or target-engine-incompatible Python-only swaps. Use when animating between multiple source textures, mascot/logo states, style states, lightmaps, decals, or UI skins.

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End-to-end production workflow guidance for Blender — the order to assemble scenes (block-out → camera → light → forms → materials → detail → render → composite → export), critique protocols, time budgets, and recovery patterns. Use whenever the user asks to "make a complete scene / hero shot / production-quality render", "what's the right order to do this", "set up a full pipeline", or has a multi-step request crossing modeling + lighting + materials + rendering. Make sure to use this skill for any request that spans multiple phases of 3D work, even if the user does not say "workflow" — also covers "make a final image of X", "produce a hero render", "professional-looking result".

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Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.

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Pre-workflow rapid prototyping for exploring multiple game ideas before committing to a development workflow. Produces lightweight reports with no workflow commitment.

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Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.

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AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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Filter Temu stores by multiple dimensions (store name/ID, country site, backend category, fully-managed/semi-managed, total/weekly/monthly sales and revenue, rating, reviews, followers, product count, store opening time, etc.). Trigger when users mention Temu store, Temu store analysis, Temu seller, Temu store ranking, Temu semi-managed store, Temu revenue, temu stores, Temu store query. Even if the user does not mention the tool name, trigger this skill whenever they are looking for stores on Temu, filtering store data, or analyzing store performance.

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Filter Shopify standalone store products by multiple dimensions (keyword/URL, price, weekly sales, listing date, Facebook ads, competitiveness, supplier availability, shipping country, etc.). Triggered when users mention Shopify products, Shopify product selection, standalone store product selection, Shopify bestsellers, Shopify dropshipping, standalone store sourcing, Facebook ad products, Shopify product query, or shopify items. Even if the user does not mention the tool name, this skill should be triggered whenever searching for products, viewing weekly sales/revenue/competitiveness, or filtering products on Shopify standalone stores.

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Filter Shopify standalone stores by multiple dimensions (store name/domain, country, years since creation, product count, ad count, monthly visits, monthly orders, social media followers, etc.). Triggered when users mention Shopify stores, Shopify store analysis, standalone stores, Shopify sellers, competitor standalone stores, Shopify monthly visits, standalone store ad library, shopify stores, or Shopify store query. Even if the user does not mention the tool name, this skill should be triggered whenever searching for stores, filtering store data, or analyzing store performance on Shopify standalone stores.

日本語の概要は準備中です。原文の説明を表示しています。