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Found 3,042 Skills
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.
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
[user] Perform security inspection and monitoring for Alibaba Cloud DDoS security products, covering DDoS Basic Protection, DDoS Native Protection, and DDoS Anti-DDoS Pro/Premium. Supports querying blackhole/scrubbing events, QPS spikes/drops, L4 traffic anomalies, HTTP status code (4xx/5xx) period-over-period surges, origin status code anomalies, and instance asset inventory. Use this Skill when users need security inspection, DDoS protection status checks, attack event queries, traffic anomaly investigation, or to confirm whether DDoS security products are provisioned. Triggers: "DDoS inspection", "security check", "DDoS protection check", "attack event query", "traffic anomaly"
Anthropic Claude Agent SDK for autonomous agents and multi-step workflows. Use for subagents, tool orchestration, MCP servers, or encountering CLI not found, context length exceeded errors.
Implement the Syncfusion ASP.NET Core SpeechToText control for converting spoken words to text using Web Speech API. Use this skill when implementing speech recognition with Razor Tag Helpers, converting voice to text in ASP.NET Core applications, handling microphone input, processing speech events, customizing button appearance, managing listening states, or building accessible voice-enabled forms. Covers setup, speech recognition features, Razor Tag Helper syntax, events, methods, globalization, and security.
Use when you need to design, review, or improve validation in Quarkus applications — including Bean Validation on JAX-RS resources, @Valid on parameters and CDI beans, constraint groups, @ConfigMapping validation, custom constraints, nested DTO validation, and ExceptionMapper-based error mapping. This should trigger for requests such as Add validation support in Quarkus; Review Quarkus validation rules; Improve request validation in Quarkus REST APIs; Add custom validation constraints in Quarkus; Validate Quarkus @ConfigMapping properties. Part of cursor-rules-java project
Authors MSW `.behaviourtree` files end-to-end and maintains the project-specific authoring spec (`.behaviourDocs/bt-spec.md`). Scans every `.codeblock` whose paired `.mlua` extends `ActionNode`/`DecoratorNode` to build a compact catalog of custom action/decorator UUIDs, propertyKey names, and version-stamped MODNativeType strings. Then generates the full tree: RootNode → Nodes graph, Blackboard variables, nodeProperties wiring, and self-validates parent/child consistency. Triggers: 'create behaviourtree', 'new BT', 'add a behaviour tree', 'BT node graph', '비헤이비어 트리 만들어', '.behaviourtree 생성', 'SequenceNode SelectorNode', 'Blackboard variable', 'definitionId codeblock', 'startNodeId', 'build BT spec', 'refresh bt-spec', 'generate behaviourtree catalog', 'BT 스펙 생성', 'bt-spec.md 만들어', 'rescan BT nodes'.
Fast, zero-friction capture of technical findings from the current conversation to the wiki's _raw/ staging area. Use this skill when the user says "/wiki-quick-chat-capture", "quick capture", "capture this finding", "save this bug fix", "capture this gotcha", "drop this to raw", "quick save to wiki", or wants to capture a non-obvious discovery mid-session without a full wiki-ingest run. Writes one _raw/ file per topic cluster in under 60 seconds — no subagents, no QMD updates, no manifest writes. Run /wiki-ingest or /data-ingest later to promote raw files to proper wiki pages.
Generate 5–6 App Store screenshots in a given brand's aesthetic from a `brand.md`, raw product screenshots, or a public App Store listing fetched through Pika MCP. Story-driven (hook → value → features → proof → close), splashy, on-brand. Outputs 1290×2796 PNGs ready to drop into App Store Connect. Use when someone wants App Store / store listing assets — including: "make me app store screenshots", "design app store screens for [brand]", "I have a brand.md and screenshots, generate store assets", "screenshot set for app launch", "iOS store screens", "app store creative", "store listing visuals", "splashy app store screens", "app-store-screens".
Create and manipulate images using GIMP, Inkscape, ImageMagick, FFmpeg, ExifTool, OptiPNG, jpegoptim, and pdftoppm via CLI. Use for: resizing, cropping, compositing, text overlays, watermarks, color correction, format conversion, WebP export, SVG creation, SVG-to-PNG export, OG images, social banners, logo manipulation, batch processing, lossless compression, metadata read/strip, GIF creation, animation, PDF-to-image. Triggers: gimp, inkscape, imagemagick, resize image, crop image, convert image, svg to png, add text to image, watermark, composite, image editing, banner, social card, compress image, strip metadata, gif, pdf to image, webp.
Pricing completo de opciones europeas y americanas. 9 metodos: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (vol estocastica, sonrisa via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM) y P(Profit). Disenado para backtesting: cada funcion es flat Python vectorizado con numpy (sin abstracciones), usa math.erfc (no scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us, Binomial N=500 5.6 ms. CLI con 15 modos mas validate y bench. Time complexity O(1) para todos los closed-form.