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Build text tokenizers that split multilingual text into small units for AI models. Learn how to train SentencePiece on raw text, configure BPE or Unigram splitting methods, and convert text to token IDs and back.
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Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.
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Prepare large text collections for AI models with fast tokenizers, which split text into model inputs. Train custom vocabularies, trace tokens to original text positions, adjust input lengths, and integrate the results with transformers.
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Build text tokenizers, which split text into units for AI models, directly from raw multilingual text. Learn custom vocabularies with BPE or Unigram, convert text to pieces or numeric IDs, and decode the results.
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Prepare text for language models by splitting it into tokens, small units represented by numbers. The skill covers batch processing, training custom vocabularies, tracking positions in the original text, and adjusting input lengths.
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Intelligent media optimization with automated workflows for images, videos, and SVGs. Includes decision trees that detect LCP images (triggers format/lazy-loading/priority analysis), identify layout shift risks (missing dimensions), and flag lazy loading issues (above-fold lazy or below-fold eager). Features workflows for complete media audit, LCP image investigation, video performance (poster optimization), and SVG embedded bitmap detection. Cross-skill integration with Core Web Vitals (LCP/CLS impact) and Loading (priority hints, resource preloading). Provides performance budgets and format recommendations based on content type. Use when the user asks about image optimization, LCP is an image/video, layout shifts from media, or media loading strategy. Compatible with Chrome DevTools MCP.
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Intelligent loading performance analysis with automated workflows for TTFB investigation (DNS/connection/server breakdown), render-blocking detection, script performance deep dive (first vs third-party attribution), font optimization, and resource hints validation. Includes decision trees that automatically analyze TTFB sub-parts when slow, detect script loading anti-patterns (async/defer/preload conflicts), identify render-blocking resources, and validate resource hints usage. Features workflows for complete loading audit (6 phases), backend performance investigation, and priority optimization. Cross-skill integration with Core Web Vitals (LCP resource loading), Interaction (script execution blocking), and Media (lazy loading strategy). Use when the user asks about TTFB, FCP, render-blocking, slow loading, font performance, script optimization, or resource hints. Compatible with Chrome DevTools MCP.
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Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs interaction), INP debugging (latency breakdown + attribution), and cross-skill integration with loading, interaction, and media skills. Use when the user asks about Core Web Vitals, LCP optimization, layout shifts, or interaction responsiveness. Compatible with Chrome DevTools MCP.
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Web performance measurement and debugging toolkit. Use when the user asks about web performance, wants to audit a page, or says "analyze performance", "debug lcp", "check ttfb", "measure core web vitals", "audit images", or similar.
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Intelligent interaction performance analysis with automated workflows for INP debugging, scroll jank investigation, and main thread blocking. Includes decision trees that automatically run script attribution when long frames detected, break down input latency phases, and correlate layout shifts with interactions. Features workflows for complete interaction audit, third-party script impact analysis, and animation performance debugging. Cross-skill integration with Core Web Vitals (INP/CLS correlation) and Loading (script execution analysis). Use when the user asks about slow interactions, janky scrolling, unresponsive pages, or INP optimization. Compatible with Chrome DevTools MCP.
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Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images), and Core Web Vitals (connection impact on LCP/INP). Use when the user asks about slow connections, mobile optimization, save-data support, or adaptive loading strategies. Compatible with Chrome DevTools MCP.
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Build direct Bluetooth Low Energy workflows with Core Bluetooth. Use when implementing BLE central or peripheral GATT communication, scanning or connecting with CBCentralManager, discovering services and characteristics, reading/writing/subscribing with CBPeripheral, publishing local services with CBPeripheralManager, handling Bluetooth authorization, background BLE modes, state restoration, write flow control, or CBUUID-based workflows. For privacy-preserving accessory setup/picker flows, use accessorysetupkit first and return here for post-setup GATT communication.
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Tokenizadores rápidos otimizados para pesquisa e produção. Implementação em Rust que tokeniza 1GB em menos de 20 segundos. Suporta algoritmos BPE, WordPiece e Unigram. Treine vocabulários customizados, rastreie alinhamentos, gerencie padding/truncagem. Integração perfeita com transformers. Use quando precisar de tokenização de alto desempenho ou treinamento de tokenizadores customizados.
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Tokenizador agnóstico de linguagem que trata texto como Unicode bruto. Suporta algoritmos BPE e Unigram. Rápido (50k sentenças/seg), leve (6MB de memória), vocabulário determinístico. Usado por T5, ALBERT, XLNet, mBART. Treina em texto bruto sem pré-tokenização. Use quando você precisar de suporte multilíngue, linguagens CJK ou tokenização reproduzível.
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Scan, connect, and communicate with Bluetooth Low Energy peripherals and publish local peripheral services using Core Bluetooth. Use when implementing BLE central or peripheral roles, discovering services and characteristics, reading and writing characteristic values, subscribing to notifications, configuring background BLE modes, restoring state after app relaunch, or working with CBCentralManager, CBPeripheral, CBPeripheralManager, CBService, CBCharacteristic, CBUUID, or Bluetooth Low Energy workflows.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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Review AI agent instructions for unnecessary rules, contradictions, repetition, and vague wording. Get recommendations on what to remove, merge, clarify, or load only when needed, plus an estimate of savings in tokens, the units models process.
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Screens biomedical / life-science papers for signs of data fabrication, image manipulation, and statistical anomalies, using the detection techniques distilled from the field's canonical exposure platforms (PubPeer, Data Colada, Science Integrity Digest, For Better Science) and tools (ImageTwin/Proofig, statcheck, GRIM/GRIMMER, Problematic Paper Screener, Seek & Blastn). Use when asked to check a paper/figure for image duplication, blot splicing, impossible statistics, paper-mill or tortured-phrase signals, research integrity, or "is this data faked"; or when a user shares a figure, Western blot, supplementary dataset, or DOI and asks whether it looks manipulated. Reports observable anomalies as questions for clarification — it never accuses anyone of fraud.
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Builds a GPT and BPE tokenizer from scratch. Use when implementing autograd, attention, a nanoGPT loop, muP hyperparameter transfer, WSD annealing, loss spikes, or mid-training.
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Use this skill to configure a Lightning Service Console app in Salesforce — Console Navigation (split view), workspace tabs and subtabs, the utility bar with Omni-Channel, Macros and History, Quick Text, keyboard shortcuts, and navigation rules per object. Trigger keywords: Service Console, console app, workspace tabs, subtabs, utility bar macros, Omni-Channel utility, split view, Quick Text, console navigation rules, keyboard shortcuts service console, navType Console, workspaceConfig mappings, WorkspaceMapping fieldName, UtilityBar FlexiPage, tabLimitConfig, listPlacement, MacroInstruction, QuickText Channel multipicklist, isNavTabPersistenceDisabled. NOT for opening or refreshing console tabs from code — use lwc/lwc-console-workspace-api. NOT for Omni-Channel routing setup — use admin/omni-channel-routing-setup.
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Fast BPE/WordPiece tokenization and custom vocab training.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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