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Found 598 Skills
Image Generation Skill: Use this skill when users need to generate images, create graphics, or edit/modify/adjust existing images. It supports 10 aspect ratios (1:1, 16:9, 9:16, etc.) and 3 resolutions (1K, 2K, 4K), and supports text-to-image and image-to-image editing.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
AI-powered E2E testing for any app — Flutter, React Native, iOS, Android, Electron, Tauri, KMP, .NET MAUI. Test 8 platforms with natural language through MCP. No test code needed. Just describe what to test and the agent sees screenshots, taps elements, enters text, scrolls, and verifies UI state automatically.
World-class UI design expertise combining the precision of Jony Ive's Apple work, the systems thinking of Figma's design philosophy, and the accessibility obsession of Inclusive Design principles. UI design is the craft of making interfaces that users don't notice - because they just work. Great UI isn't about making things pretty. It's about making the right thing obvious and the wrong thing impossible. Every pixel, every animation, every spacing decision either helps the user or hurts them. The best UI designers are invisible - users accomplish their goals without ever thinking about the interface. Use when "ui design, visual design, interface design, component, design system, figma, sketch, color, typography, spacing, layout, animation, motion, responsive, mobile design, button, form design, card, modal, navigation, icon, ui, design, visual, interface, components, design-system, figma, accessibility" mentioned.
AI-powered systematic codebase analysis. Combines mechanical structure extraction with Claude's semantic understanding to produce documentation that captures not just WHAT code does, but WHY it exists and HOW it fits into the system. Includes pattern recognition, red flag detection, flow tracing, and quality assessment. Use for codebase analysis, documentation generation, architecture understanding, or code review.
Use when you need hard pass fail eval gates for generated projects and skills; pair with addon-decision-justification-ledger and addon-human-pr-review-gate.
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Use historical analogies to inform strategic decisions by identifying structural similarities and differences between past and present situations. Use this skill when the user draws on historical precedent to justify a strategy, needs to evaluate whether a historical comparison is valid, or wants to learn from past events — even if they say 'this is like the dotcom bubble', 'history repeats itself', or 'what can we learn from how X handled this'.
Adopt Prisma Next into a new project, onto an existing database, or as the first move after a bootstrap tool dropped you into a scaffold. Use for "what can I do with Prisma Next", "what can I do next with Prisma", "where do I start", "what should I do first", "just ran createprisma", "createprisma", "npx createprisma", "npx create-prisma", "first steps", "first query", "I have a scaffolded Prisma Next project what now"; for `pnpm dlx prisma-next init` greenfield setup; and for `prisma-next contract infer` + `db sign` against an existing database. Also covers the connect-write-read first-arc orientation, the day-to-day commands (`contract emit`, `db init`, `db update`, `migration plan`, `migrate`, `db schema`, `db verify`), and routing to `prisma-next-contract` / `prisma-next-queries` / `prisma-next-runtime` for the next move. Flags: --target, --authoring, --schema-path, --probe-db, --output.
Gain wisdom from setbacks — Go through the 5-step interactive reflection (Setback → Automatic Output → Old Weights → New Parameters → Alternative Action), move from "emotional review" to "behavioral training", and update the L3 weights of your first reactions. Use when Wang Jianshuo reflects on a personal setback, mistake, or recurring pattern (reflection, post-mortem review, review, draw lessons, learn from a setback, gain wisdom, "I messed up again", "Why does this keep happening?", "Why do I always…?", "I can't just let it go", "I know the principles but can't put them into practice"). For the user as a human, not for Claude's task post-mortems.
Cross-platform attribution health audit covering AdAttributionKit (iOS view-through 24h post-impression, WWDC 2025 configurable windows), GA4 attribution models (data-driven vs last-click), Consent Mode V2 enforcement, server-side attribution stitching, MMP integration health, and cross-device / cross-platform attribution. Use when user says attribution audit, attribution model, AdAttributionKit, AAK, view-through attribution, GA4 attribution, Consent Mode V2, conversion window, attribution window, MMP audit, AppsFlyer audit, Adjust audit, Branch audit, Singular audit, cross-device attribution, or cross-platform attribution.
Estimate Amazon monthly sales and revenue from a Best Seller Rank, and size a niche or competitor from observable signals. Explains the BSR-to-sales relationship, adjusts by category and price, and returns a sales range with a confidence note. Use when a user asks how many units a product sells, to estimate sales from BSR, size a market or niche, gauge a competitor's volume, or judge demand. Trigger phrases: "sales estimate", "how many units", "estimate from BSR", "best seller rank", "market size", "competitor sales", "monthly sales". Works with zero tools. the user provides BSR, category, and price.