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Organize shared build settings, tasks, and package versions across .NET projects. Review Directory.Build file placement and import order, centralize repeated configuration, and retain the settings that belong to individual projects or folders.
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Design new interfaces or reshape existing ones with colors, typography, and layouts grounded in the subject and audience. Develop a visual plan, review it, and build a coherent interface with clear wording and purposeful motion.
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Guide authorized red-team exercises that simulate attackers seeking access and demonstrating impact. The skill organizes test priorities, evidence preservation, related application checks, and minimal data collection, with checklists for planning, ongoing work, and final validation.
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Prepare Bugcrowd submissions with accurate vulnerability categories, evidence-based severity requests, and explanations of program scope. Structure references between related reports, document QA testing conditions, and draft responses when a finding is downgraded or considered out of scope.
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Review API configuration and input handling for unauthorized field updates, prototype pollution that alters shared object defaults, and manipulation of backend URLs. The skill also covers exposed API specifications and access control weaknesses in OData queries.
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Create statistical charts from tabular data to explore distributions, relationships, and group differences. This skill guides plot selection, multi-panel layouts, and styling, including box plots, heatmaps, and charts with estimates and uncertainty bands.
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Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.
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Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.
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Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.
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Verify assumptions before implementing large or risky changes to APIs, provider data, billing, pricing, quotas, or defaults. Use when a mistake could affect customers or the user asks if a change is safe to ship. Checks real-world impact beyond passing tests.
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Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.
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Run and validate an end-to-end Mission Control showcase with a locally installed Isaac Sim launched in its GUI window, driven through the isaac-sim-remote Python server, with Nova Carter SIL. Use for demos, showcase replays, Mission Control driving a simulated robot, or diagnosing the integrated small-warehouse scenario. Detect existing Isaac Sim installations without modifying them, automatically select a usable runtime without prompting whenever compatibility can be confirmed, and delegate requested installation or version changes to isaac-sim-installation. Defaults to a canonical Isaac 6.1 warehouse and deterministic circular route when the user does not specify another scenario.
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Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.
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Gather phase context through adaptive questioning before planning. Use --all to skip area selection and discuss all gray areas interactively. Use --auto to skip interactive questions (Claude picks recommended defaults). Use --chain for interactive discuss followed by automatic plan+execute. Use --power for bulk question generation into a file-based UI (answer at your own pace).
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Render an interactive options payoff chart — expiry P&L plus Black-Scholes theoretical value — with sliders for strikes, premium, IV, DTE, and spot. Use this skill whenever the user describes or shares an options position or strategy and wants to see how it makes or loses money: vertical, calendar, diagonal, or ratio spreads, butterflies, condors and iron condors, straddles, strangles, covered calls, protective or naked puts, or any multi-leg structure. This includes pasted strikes, premiums, and expiries, requests like "show me the payoff" or "draw the P&L curve", and broker screenshots (IBKR, TastyTrade, Robinhood). Use it even when details are partial — defaults fill the gaps.
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Generates images and game sprites through GPT or Grok with guided provider choices, separate saved defaults, automatic cleanup and optional curation. Handles sprite requests, ordinary image generation/editing, typography logos (로고, 타이포그래피, 타이틀 글자, 타이틀 로고, wordmark, logo, title lettering: a title screen drawn with the title in it, its lettering lifted out transparent), standalone image-to-video clips (i2v, animate this still, 그록 영상, 이매진 비디오, 스틸 움직여줘, first/last frame, reference-to-video, 영상 이어붙이기, 영상 편집, extend/edit a clip), clips from a video MCP on the agent (ZCRE, 지크 MCP), chroma removal, animation atlases, video loops, 큐레이션뷰, image candidates, 팔레트 스왑, palette swap, recolor, rig layers, engine exports, repeating backgrounds, projected shadows, motion/contact inspection and optional scene composition from existing assets.
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Launches an EC2 instance with secure, cost-efficient defaults including AMI selection, burstable instance sizing, least-privilege IAM roles, hardened security groups, encrypted EBS volumes, and comprehensive tagging. Use when deploying new EC2 instances following AWS best practices for security and cost optimization.
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Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I/O-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.
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Authors, validates, and troubleshoots AWS CloudFormation templates. Covers template authoring with secure defaults, local validation with either cfn-lint or cloudformation-validate, cfn-guard security and compliance checks as a recommended default, account-aware CloudFormation service pre-deployment validation, CloudFormation Express mode for faster deployments, and root-cause diagnosis of failed stacks using CloudFormation events and CloudTrail correlation. Also covers author-time template intelligence with the CloudFormation Language Server and published cloudformation-validate libraries.
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Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.
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Configure which models pstack uses per role and at what reasoning budget. Detects your available models and writes an always-applied rule that overrides the skill defaults. Use for /setup-pstack, "configure pstack models", "pstack budget", or changing pstack's model choices.
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Applies a deliberate visual direction to an interface — minimalist editorial, industrial utilitarian, or high-polish commercial — each with its own type scale, palette behavior, surface treatment, and motion. Use this when a product needs a point of view rather than defaults, when choosing between visual directions, when an interface reads as generic, or when restyling something without changing its structure.
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Applies decision science and cognitive bias research to marketing and product decisions — how people actually choose under uncertainty, and how framing, defaults, sequencing, and social context change behavior. Use this to diagnose why a well-argued offer is not converting, to structure choices and pricing presentation, to design an experience around how attention and memory work, or to pressure-test whether a persuasion tactic is legitimate or manipulative.
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GSAP animation reference for HyperFrames. Covers gsap.to(), from(), fromTo(), easing, stagger, defaults, timelines (gsap.timeline(), position parameter, labels, nesting, playback), and performance (transforms, will-change, quickTo). Use when writing GSAP animations in HyperFrames compositions.
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