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Found 9,277 Skills
Market microstructure analysis via Longbridge Securities — bid-ask spread, order-flow toxicity (large-order pressure), liquidity depth, price impact, and institutional order direction. Covers A-share call-auction analysis and HK block-trade mechanics. Triggers: "盘口分析", "微观结构", "订单流", "大单分析", "买卖价差", "逐笔分析", "买卖盘深度", "挂单墙", "主力动向", "集合竞价", "盤口分析", "微觀結構", "訂單流", "大單分析", "買賣價差", "逐筆分析", "買賣盤深度", "掛單牆", "主力動向", "market microstructure", "order flow", "bid-ask spread", "depth analysis", "large order", "order book imbalance", "price impact", "auction analysis", "institutional order flow".
Chan Theory Pattern Recognition — Automatically detect top/bottom fractals, Bi (upward/downward Bi), Segments, Zhongshu, and generate Buy 1/Buy 2/Buy 3/Sell 1/Sell 2/Sell 3 signals. Depends on the czsc library. Triggers: "缠论", "分型", "笔", "中枢", "线段", "一买", "二买", "三买", "一卖", "二卖", "三卖", "缠中说禅", "缠师", "纏論", "分型", "筆", "中樞", "線段", "一買", "二買", "三買", "一賣", "二賣", "三賣", "chanlun", "chan theory", "bi", "zhongshu", "buy point", "sell point", "fractal top bottom", "Chan theory".
Use when writing, fixing, or editing TypeScript with duplicated logic, magic values, unclear one-liners, mixed responsibilities, clutter, arbitrary code, or inconsistent abstraction levels.
Generate deep research reports on prediction market events using the Octagon Prediction Markets Agent. Combines real-time Kalshi market data with AI-driven analysis to surface price drivers, compare market vs. model probabilities, and identify potential mispricings across 120+ active markets.
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
Generates correct, deployable Salesforce permission set metadata (PermissionSet XML) with object, field, user, and app permissions. Use this skill when creating or editing permission set metadata, object permissions, field-level security (FLS), tab visibility, or deploying permission sets.
Guides SOC operations—alert triage, SIEM/EDR investigation, enrichment, playbook execution, false-positive closure, escalation decisions, and detection tuning feedback. Use when working SOC queues, investigating suspicious alerts, correlating events, documenting analyst notes, or deciding escalate vs close—not for declared incident command, timelines, evidence preservation, or regulatory comms (incident-responder), incident program design (incident-management-engineer), binary/firmware RE (reverse-engineer), red team operations (red-team-specialist), or enterprise security strategy (cybersecurity).
For identifying the main theme of the A-share market, focusing on market structure / theme cycle / capital behavior. This Skill is mainly applicable to scenarios such as answering user questions, writing reports, and creating financial articles. This report generates a large amount of content and is not suitable for simple conversation scenarios. To obtain various information and data, you can use the wind.financial.data tool with appropriate keywords or keyword combinations. After the market opens, at midday, and after the market closes every day, users need to quickly know: what the market is actually trading today, what the real main theme is, where the market sentiment stands, and which areas to focus on tomorrow.
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Implements the Syncfusion ASP.NET Core TimePicker component (EJ2). Use this skill when the user needs to add a time picker, time selection input, time field, or clock picker in an ASP.NET Core application. Trigger this skill for: TimePicker setup, time range configuration, strict mode, time masking, masked time input, globalization, RTL support, localization, accessibility, keyboard navigation, CSS customization, custom styling, form validation with TimePicker, TimePickerFor model binding, time format configuration, time step interval, floating label, clear button, full screen popup, popup behavior, scroll position, server timezone, and any scenario involving ejs-timepicker tag helper or Syncfusion EJ2 TimePicker.
Builds Moran's I spatial autocorrelation workflows in CARTO. Triggers when the user mentions spatial autocorrelation, Moran's I, spatial dependency, spatial correlation, spatial outliers, HH HL LH LL quadrants, high-high clusters, low-low clusters, spatial weight matrix, "is there clustering", "are values spatially correlated", local indicators of spatial association, LISA, spatial randomness test, or wants to determine whether a variable exhibits spatial clustering, dispersion, or randomness across a gridded dataset. Also relevant when the user needs to classify locations into cluster types (HH, HL, LH, LL) rather than just identifying hotspots and coldspots.
Render an ad-hoc interactive map inline in the chat from a deck.gl declarative spec via the CARTO MCP server's view_map tool. Use whenever the user asks to map, visualize, or show the geographic distribution of points, polygons, hexagons, quadbins, clusters, density (heatmaps), or raster — and the map is exploratory or throwaway, not meant to be saved as a permanent CARTO Builder map. Triggers on "show me X on a map", "visualize Y", "make a heatmap of Z", "render the points/clusters/raster of W". Distinct from carto-create-builder-maps (CLI authoring of permanent maps), carto-preview-builder-map (loading an existing saved Builder map), and carto-develop-app (writing a from-scratch deck.gl app in TypeScript / JavaScript).