Results for “tokenization”

Reduces a webpage to a structural skeleton by tokenizing content in the browser and applying LLM reasoning to collapse repeated patterns.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.

Define and organize design tokens for color, spacing, typography, elevation, and motion with naming conventions and usage guidance.

Use for UI/UX and design-system work when Codex should convert visual direction or existing UI into reusable design tokens.

Design a theming architecture that supports brand variants, dark mode, and high-contrast modes with token mapping.

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.

Token budget analytics and ROI scoring for AI agent actions. Track token cost per fix, detect wasteful loops, auto-route to fast tier when burn rate is too high. Integrates with token-budget-guard.sh hook. Triggered by /cost-report, "how much did that cost", "token usage", "am I wasting tokens".

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.

Auto-routes Claude Code subtasks to the cheapest capable model (Haiku/Sonnet/Opus), logs token costs, and A/B tests tiers to validate savings against real workloads.

Manage design tokens by listing, adding, and updating them while keeping JSON sources, CSS variables, and dark-mode values in sync.

Measurement-driven model router for Claude Code. Routes Haiku/Sonnet/Opus per task class, logs every routed task with real $ numbers, and A/B tests cheaper tiers before you trust the savings.

Generates at most one token recommendation and one prediction-market pick per run, each with a falsifiable thesis, entry, sizing, and kill criterion. Returns NO_PICK when no candidate meets the bar.

Mine BOTCOIN tokens by solving AI-generated challenges and submitting on-chain proofs on Base.

Audit design token usage across a product for consistency and coverage, identifying hard-coded values and gaps.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Creates and manages design token systems with three-layer architecture, generates CSS variables, validates token usage, and produces brand-compliant slide presentations with Chart.js and contextual decision logic.

Token Standards

Optimize a complete candidate context package by deduplicating, filtering, ordering, and allocating its token budget. Use when retrieved or assembled material is noisy or exceeds the useful context budget; use context-ranking for scoring chunks and context-compression for shrinking selected content.

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.

上下文压缩省 Token。对话变长或开新任务时输出/使用 6 块压缩结构，总长 400~800 tokens。必加载。

Token-efficient code review using Tree-sitter AST graphs and MCP. Reduces AI assistant token usage by 6.8–49x by computing blast radius of changes instead of reading entire codebases. Uses SQLite graph database for structural analysis.

Guides fuzzers with domain-specific tokens to reach deeper code paths in parsers, protocol handlers, and file format processors.

Optimizes long-running agent sessions with structured context compression, summarization, and durable handoff summaries that preserve decisions, files, risks, and next actions.

Establish clear naming conventions for design assets, components, and tokens to make design systems predictable and maintainable.

Expert design system architect specializing in design tokens, component libraries, theming infrastructure, and scalable design operations. Masters token architecture, multi-brand systems, and design-development collaboration. Use PROACTIVELY when building design systems, creating token architectures, implementing theming, or establishing component libraries.

A design system has three token layers, and conflating them is what makes systems

Tracks and optimizes AI API spending across 19 providers with live pricing and 6 optimization modules.

Reads a public API using a declared token.

Intercepts responses to let users choose the depth and token budget before answering, with heuristic token estimation and preset depth levels.