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Found 540 Skills
Assess investment suitability obligations under FINRA Rules 2111 and 2090 across all three suitability prongs. Use when the user asks about reasonable-basis, customer-specific, or quantitative suitability, product-specific concerns for complex products, leveraged ETFs, variable annuities, or alternatives, household-level suitability, hold recommendations, or the institutional suitability exemption. Also trigger when users mention 'is this investment suitable', 'turnover ratio is too high', 'cost-to-equity ratio', 'churning metrics', 'suitability questionnaire design', 'complex product due diligence', 'customer refused to provide their risk tolerance', or ask whether a recommendation fits a customer's profile.
Expertise in F2P economics, virtual currencies, and ethical monetization strategiesUse when "game monetization, F2P economy, in-app purchase, IAP strategy, battle pass design, loot box, gacha system, virtual currency, player LTV, whale monetization, game economy balance, premium currency, season pass, daily rewards, pay to win, ethical monetization, monetization, f2p, free-to-play, iap, in-app-purchase, battle-pass, season-pass, gacha, loot-box, virtual-economy, game-economy, ltv, arpu, retention, whales, pricing, microtransactions" mentioned.
Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.
Apply first principles thinking to break problems down to fundamental truths and reason up from there. Use this skill when the user is stuck in conventional thinking, needs to challenge assumptions, find breakthrough solutions, or evaluate whether something is truly impossible vs just assumed to be — even if they say 'everyone does it this way', 'is there a fundamentally better approach', 'why does it have to cost this much', or 'challenge my assumptions'.
Use this skill when reasoning about the PixiJS v8 scene graph as a whole: how containers, leaves, transforms, and render order fit together. Covers leaf vs container distinction, local/world coordinates, culling, render groups, sortable children, masking, RenderLayer, constructor options shared by every scene node, and which leaf skill covers which display object. Triggers on: scene graph, display list, Container, Sprite, Graphics, Text, Mesh, ParticleContainer, DOMContainer, GifSprite, masking, render group, RenderLayer, world transform, constructor options, ContainerOptions.
Lindy platform help — no-code AI agent builder for email triage, meeting notes, calendar management, custom workflow automation, chatbots, and AI phone agents. Use when setting up Lindy agents for inbox management, meeting recording not working or transcripts missing, credits burning too fast and need to optimize usage, building custom AI workflows with triggers and actions, choosing between Lindy and a dedicated note-taker or automation tool, or debugging agent errors in multi-step workflows. Do NOT use for picking a dedicated AI note-taker across vendors (use /sales-note-taker) or general workflow automation without AI reasoning (use /sales-integration).
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Meta skill for the EvanFlow system. Loads the shared vocabulary (deep modules, deletion test, vertical slice, grill, mockup quick-mode, no-auto-commit) and describes when to invoke each evanflow-* skill. Use when starting a new task and unsure which evanflow skill applies, or when you need to ground reasoning in the shared vocabulary.
Record human-in-the-loop quality judgments for generated images, voice takes, and videos in short-form production. Use this when a person has reviewed an asset and you need structured verdicts, reasons, issue categories, and rerun guidance without turning subjective approval into untracked chat history.
Honestly evaluate AI work quality using a two-axis scoring system. Use after completing a task, code review, or work session to get an unbiased assessment. Detects score inflation, forces devil's advocate reasoning, and persists scores across sessions.
Solve quantitative problems in biophysics, pharmacokinetics, epidemiology, toxicology, population genetics, and statistical mechanics. Provides reasoning strategies and Python templates for calculations alongside ToolUniverse data lookups. Use when users ask about drug dosing, half-life decay, radioactive tracers, R0, herd immunity, diffusion, Hardy-Weinberg, binding equilibria, or any computation-heavy biology/chemistry question.
Find and evaluate research datasets for any scientific question. Teaches how to reason about data needs, search across public repositories, evaluate dataset fitness, and identify access requirements. Use whenever users ask to find data, search for datasets, identify cohort studies, or need data for analysis. Also use when users ask about a specific survey or cohort (NHANES, HRS, UK Biobank, TCGA, etc.), when they want to know what data exists for a research question, or when they need to compare available data sources. If the user mentions "where can I get data" or "is there a dataset for X", this is the right skill.