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Found 2,691 Skills
Apple Human Interface Guidelines (HIG) reference for designing iOS, iPadOS, macOS, tvOS, visionOS, watchOS apps. Covers UI components, layout, accessibility, typography, navigation, inputs, and platform technologies. Use when designing Apple platform UIs, reviewing SwiftUI/UIKit patterns, or applying HIG design principles to any app.
Capture, organize, and retrieve notes efficiently using structured formats, tagging, and file management for meetings, ideas, research, and daily logs.
Run Codex to take a screenshot for a specific page.
Apply `wbfy` to the current repository.
Run Claude Code to take a screenshot for a specific page.
Compares two Xcode build runs to identify duration regressions, cache changes, and new issues. Can be invoked with build IDs, dashboard URLs, or branch names (e.g. `tuist compare-builds --base main --head feature-branch`).
Use when evaluating whether a skill belongs in a library. Preview content, check frontmatter, validate structure, and decide whether to keep, curate, or remove.
Performs security audits and vulnerability assessments on Ruby on Rails application code. Use when reviewing Rails code for security risks, assessing authentication or authorization, auditing parameter handling, redirects, file uploads, secrets management, or checking for XSS, CSRF, SSRF, SQL injection, and other common vulnerabilities.
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio, Linkme, Amazon Shop). Use when the user asks to scrape, fetch, extract, search, or look up social media profiles, posts, videos, reels, comments, transcripts, followers, ads, hashtags, trending content, or engagement metrics from any social platform. Also use when user mentions ScrapeCreators, social media API, ad library, or creator data.
Optimize the performance of Triton operators optimized for Ascend NPU. This guide is for users who need to optimize the performance of Triton operators on Ascend NPU, resolve UB overflow, improve Cube unit utilization, and design Tiling strategies.
Maintain JSONL-only profiler performance test cases under csrc/ops/<op>/test in ascend-kernel. Collect data using torch_npu.profiler (with fixed warmup=5 and active=5), aggregate the Total Time(us) from ASCEND_PROFILER_OUTPUT/op_statistic.csv, and output a unified Markdown comparison report (custom operator vs baseline) that includes a DType column. Do not generate perf_cases.json or *_profiler_results.json. Refer to examples/layer_norm_profiler_reference/ for the reference implementation.
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation. Workflow 1 auto-discovers input shapes and parameters from user source code. Workflow 2 exports PyTorch models to ONNX. Workflow 3 converts ONNX to .om via ATC with multi-CANN version support. Workflow 4 adapts the user's full inference pipeline (preprocessing + model + postprocessing) to run end-to-end on NPU. Workflow 5 verifies precision between ONNX and OM outputs. Workflow 6 generates a reproducible README. Supports any standard PyTorch/ONNX model. Use when converting, testing, or deploying models on Ascend AI processors.