「token」の検索結果

Solana token data, PnL, risk scores, 1-second OHLCV, wallet analytics, and self-hosted swap execution via Raptor

日本語の概要は準備中です。原文の説明を表示しています。

DEX orderflow analysis, trade classification, buyer/seller pressure, and microstructure signals for Solana tokens

日本語の概要は準備中です。原文の説明を表示しています。

Free DeFi analytics across all chains — TVL, token prices, DEX volumes, fees/revenue, stablecoins, and bridges

日本語の概要は準備中です。原文の説明を表示しています。

DEX liquidity depth assessment, slippage estimation, and pool composition analysis for Solana tokens

日本語の概要は準備中です。原文の説明を表示しています。

Free, no-auth multi-chain DEX pair data — prices, volume, liquidity, transactions, and token profiles

日本語の概要は準備中です。原文の説明を表示しています。

21 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for maintaining the MassGen model and backend registry. This skill should be used when adding new models, updating model information (release dates, pricing, context windows), or ensuring the registry stays current with provider releases. Covers both the capabilities registry and the pricing/token manager.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose and troubleshoot bitcoin-auth token generation and verification issues. This skill should be used when users encounter authentication failures, signature verification errors, or integration problems with the bitcoin-auth library.

日本語の概要は準備中です。原文の説明を表示しています。

Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.

日本語の概要は準備中です。原文の説明を表示しています。

Use when escalating privileges on a Windows host — SeImpersonate Potato chains (GodPotato/PrintNotifyPotato), service & DLL hijacking, UAC bypass (fodhelper/ICMLuaUtil), kernel EoP + BYOVD (CVE-2025-29824), token-rights abuse, LSASS/SAM/DPAPI credential harvesting

日本語の概要は準備中です。原文の説明を表示しています。

Front door. Reads the current project (framework, tokens, brief, spec, harness) and reports what ui-craft can do right now, then routes you to the right next step. Run this first if you're new or unsure where to begin. No code changes. Invoke when the user asks for start on their UI, or mentions 'start' alongside design / UI / frontend work.

日本語の概要は準備中です。原文の説明を表示しています。

Full spec-driven pipeline — walks brief → tokens → shape (spec) → craft (build) → converge → ship in one guided run. Writes `.ui-craft/spec.md`. Run when starting a net-new surface from scratch. Invoke when the user asks for sddesign on their UI, or mentions 'sddesign' alongside design / UI / frontend work.

日本語の概要は準備中です。原文の説明を表示しています。

Pre-ship gate — runs detector, verifies brief and tokens, applies the 10-pass finish bar, ranks findings by feedback hierarchy. Use when the user wants to ship, merge, or finalize a surface and needs a verdict (READY / NOT READY / BLOCKED) before committing. Invoke when the user asks for finalize on their UI, or mentions 'finalize' alongside design / UI / frontend work.

日本語の概要は準備中です。原文の説明を表示しています。

Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implementing design systems, or standardizing UI patterns.

日本語の概要は準備中です。原文の説明を表示しています。

Build and maintain Core UI with the ReUI registry and authenticated ReUI MCP. Use for new pages/sections/features, ReUI components/examples/Pro blocks, @reui installs, REUI_LICENSE_KEY setup, and ReUI API or documentation research. Preserve exact base-maia/Base UI, packages/ui ownership, existing shared tables, and Core tokens; use live APIs and worked examples.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Build UI with Core's shadcn/Base UI system. Use for selecting, installing, composing, or customizing shadcn/ui, registry items, themes, and wrappers. Always preserve exact style `base-maia`, Zinc-oriented semantic CSS-variable tokens, and Base UI primitives. Do not use to switch styles, run `shadcn init`, or introduce Radix/React Aria. Pair with `packages/ui/AGENTS.md`.

日本語の概要は準備中です。原文の説明を表示しています。

Build accessible, composable UI with @base-ui/react primitives and project styling tokens. Use when creating or refining unstyled primitive components and behavior-heavy UI such as overlays, menus, popovers, dialogs, and form controls, or defining custom component APIs. Prefer the moai-library-shadcn skill when copied-in shadcn components are the primary implementation path.

日本語の概要は準備中です。原文の説明を表示しています。

HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.

日本語の概要は準備中です。原文の説明を表示しています。

Skill for creating and managing a Design System using Tailwind CSS and shadcn/ui. Use when defining design tokens, setting up theming with CSS variables, building a consistent UI component library, initializing a design system configuration, or wrapping shadcn/ui components into design system primitives.

日本語の概要は準備中です。原文の説明を表示しています。

Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

日本語の概要は準備中です。原文の説明を表示しています。

Provides JWT authentication and authorization patterns for Spring Boot 3.5.x covering token generation with JJWT, Bearer/cookie authentication, database/OAuth2 integration, and RBAC/permission-based access control using Spring Security 6.x. Use when implementing authentication or authorization in Spring Boot applications.

日本語の概要は準備中です。原文の説明を表示しています。