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Found 550 Skills
Autonomous co-pilot — agent formulates goal from natural language, enables lock mode with SessionCopilot reasoning, works until goal is achieved.
Perses variable lifecycle management: create Text and List variables at global, project, or dashboard scope. Handle variable chains with dependencies (A depends on B depends on C). Supports 14+ interpolation formats. Uses MCP tools when available, percli CLI as fallback. Use for "perses variable", "dashboard variable", "perses filter", "add variable". Do NOT use for datasource management (use perses-datasource-manage).
Fetch the latest financial signals and transmission-chain analyses from DeepEar Lite. Use when the user needs immediate insights into financial market trends, stock performance factors, and reasoning from the DeepEar Lite dashboard.
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to Google Sheets (via Rube) or CSV. Supports calibration mode for prompt refinement.
[QwenCloud] Understand images and videos with Qwen vision models. TRIGGER when: user wants to analyze, describe, or extract information from images or videos, OCR text extraction, chart/table reading, visual reasoning, multi-image comparison, screenshot understanding, video comprehension, or explicitly invokes this skill by name (e.g. use qwencloud-vision). DO NOT TRIGGER when: user wants to generate/create images (use qwencloud-image-generation), generate videos (use qwencloud-video-generation), text-only tasks without visual input, or non-Qwen vision tasks.
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.
Use when scheduling Xiaohongshu posts, maintaining consistent posting frequency, planning content around events or seasons, or organizing content production workflow
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
Draft or update requirement documents under `easysdd/requirements/` for the project — describe a capability's "reason for existence, solution approach, and boundaries" using **user stories + plain language**, so non-technical readers can quickly grasp the key highlights of the system. Layered with architecture: requirement is the "problem space" (why this capability is needed), while architecture is the "solution space" (what structure is used to implement it). Two modes: new (draft a new requirement doc from scratch), update (refresh an existing doc based on new materials or implementation changes). Single-target rule — only modify one document at a time. Trigger scenarios: when the user says "fill in a requirement doc", "write down the requirements for this capability", "update the requirements directory", or when it is found during the feature-design phase that there is no corresponding requirement for the capability to be implemented this time.