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Found 245 Skills
Upgrade Cairo smart contracts using OpenZeppelin's UpgradeableComponent on Starknet. Use when users need to: (1) make Cairo contracts upgradeable via replace_class_syscall, (2) integrate the OpenZeppelin UpgradeableComponent, (3) understand Starknet's class-based upgrade model vs EVM proxy patterns, (4) ensure storage compatibility across upgrades, (5) guard upgrade functions with access control, or (6) test upgrade paths for Cairo contracts.
Cloudflare Workers Runtime APIs including Fetch, Streams, Crypto, Cache, WebSockets, and Encoding. Use for HTTP requests, streaming, encryption, caching, real-time connections, or encountering API compatibility, response handling, stream processing errors.
Compress PNG and JPEG screenshots in place using pngquant and jpegoptim, keeping the original format for maximum compatibility.
Apply CUDA Graphs to PyTorch workloads — API selection (torch.compile, PyTorch make_graphed_callables, TE make_graphed_callables, MCore CudaGraphManager, FullCudaGraphWrapper, manual torch.cuda.graph), code compatibility, capture workflows, dynamic pattern handling, and troubleshooting. Triggers: CUDA graph, torch.cuda.graph, make_graphed_callables, reduce-overhead, graph capture, graph replay, kernel launch overhead, CudaGraphManager, FullCudaGraphWrapper, full-iteration graph, stream capture.
Audits SQL migration files for destructive actions, potential table locks, and compatibility issues. Use before applying migrations to production databases to prevent downtime and ensure data integrity.
Think like a product manager before changing React Doctor's public surface — CLI commands/flags, the 0–100 score, config (doctor.config.*), the JSON report schema, package APIs (inspect()/diagnose()), the GitHub Action, the website, and the canonical prompts. A step-by-step runbook for a user-facing change — locate the surface, search for a reuse candidate, wire one telemetry metric, add the compatibility artifacts (changeset / schemaVersion / action tag), update docs, and record a kill metric. Not for lint rules, which have their own pipeline. Also runs when the user types `/product-thinking`.
Clarify and compile project technical solutions. Use this when users need to determine the overall implementation approach, key technology trade-offs, module and file responsibilities, compatibility and verification boundaries for a problem, bug, function change, or existing requirement in combination with the current project, or when they need to create a technical document that can be understood by product, development, and testing teams and used for subsequent work. No prior requirement document is required, and it does not cover task breakdown, coding implementation, or project acceptance.
Rework a change as if the intended UX and architecture existed from day one, deleting compatibility cruft and accidental complexity.
Pre-deployment validation for Webflow Code Components. Checks bundle size, dependencies, prop configurations, SSR compatibility, styling setup, and common issues before running webflow library share.
Audit Webflow Code Components for architecture decisions - prop exposure, state management, slot opportunities, and Shadow DOM compatibility. Focused on Webflow-specific patterns, not generic React best practices.
Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.