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Found 686 Skills
Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. Part of the skills-for-java project
Build VoIP calling apps on Android using Telnyx WebRTC SDK. Covers authentication, making/receiving calls, push notifications (FCM), call quality metrics, and AI Agent integration. Use when implementing real-time voice communication on Android.
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Produce a one-page product intent specification with problem statement, users, metrics, risks, and acceptance criteria in Given/When/Then format. Use before any technical design or implementation work begins.
Transform vague product ideas into concrete, executable strategies with clear metrics, user impact, and technical feasibility.
Iterative testing, verification, and improvement supervisor. Triggers when: User requests iterative testing and improvement, code quality review and assurance is needed, automated testing and feedback loops are required, or multiple rounds of refinement are specified. Commands: - /iterate <n> - Run n iterations of test-improve cycle - /iterate stop - Stop current iteration loop - /iterate status - Show current iteration status - /iterate report - Generate iteration report Capabilities: Automated test execution and result analysis, quality metrics tracking across iterations, improvement suggestion generation, convergence detection, and detailed iteration reports.
Designs structured benchmarks for comparing algorithms, models, or implementations. Selects appropriate metrics (latency, throughput, memory, accuracy), designs representative test cases, captures hardware/software context, produces comparison tables with tradeoff analysis, and includes reproduction instructions. Triggers on: "benchmark", "compare performance", "which is faster", "latency comparison", "memory comparison", "run benchmark", "design benchmark", "compare implementations", "evaluate algorithms", "performance comparison", "throughput test", "speed test". Use this skill when comparing two or more implementations, algorithms, or models.
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.
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.