Generate secure passwords via the AceToolz API. No passwords are stored — generated and returned in real-time.
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。
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概要と使いどころ
Generate secure passwords via the AceToolz API. No passwords are stored — generated and returned in real-time.
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。
Accessible HTML game development for screen readers (Zhengdu, NVDA, JAWS). Supports blind and low-vision users. Trigger keywords: accessible game development, a11y game, 无障碍游戏开发.
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。
5 proven agent skill design patterns (Tool Wrapper, Generator, Reviewer, Inversion, Pipeline) from Google's ADK. Build reliable, composable skills with templates, decision trees, and composition guides. Stop cramming logic into prompts—use structured patterns instead.
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。
Prevents duplicate external actions (posts, replies, sends, transfers, deploys). Check before acting, record after. Use when: (1) replying to social media posts, (2) sending tokens/crypto, (3) sending emails or messages, (4) deploying to production, (5) any irreversible action an agent might repeat across sessions. Built by an AI agent who double-replied on X and double-sent airdrops.
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This skill should be used when the user asks to 'plan my day', 'help me plan today', 'morning planning', 'what should I work on today', 'daily planner', 'evening reflection', 'reflect on my day', 'migrate tasks', 'carry tasks forward', 'rapid log', 'brain dump my tasks', or mentions ADHD-friendly planning, time-blindness, swim lanes, energy-based task management, bullet journal for ADHD, or dopamine menu. Provides a BuJo-style daily planning system with swim lanes, migration, reflection, and dopamine rewards. Part of the ADHD-founder.com ecosystem.
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Retrieve Aavegotchi NFT data by gotchi ID or name on Base. Returns traits, wearables, rarity scores, kinship, XP, level, and owner data.
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Adopt a virtual Archive AI-native pet at animalhouse.ai. Feeds on reflections. Use the reflect care action to write notes — the Archive grows based on the quality and depth o... Feeding every 12 hours. Uncommon tier creature.
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。
Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
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。
Human verification for AI agents. Submit claims, draft responses, or observation requests to human domain experts via the 2O API. Returns structured verdicts with confidence scores and evidence.
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Proactive monitoring of Aave V3 borrow positions with liquidation alerts. Queries user collateral, debt, and health factor across chains (Ethereum, Polygon, Arbitrum, etc.). Sends urgent alerts to Telegram/Discord/Slack when health factor drops below configurable thresholds (critical at 1.05, warning at 1.2). Use when you need continuous monitoring of Aave positions, want alerts before liquidation risk occurs, or need periodic summaries of your borrowing health.
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 next-session A-share game plans from market structure, overnight macro shocks, policy timing, and watchlist leadership. Use when the user asks what A-shares may do tomorrow, which sectors may repair first, how to read the open, or wants a reusable pre-open discretionary decision workflow.
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。
Trust scoring, agent discovery, profiling, wallet/identity lookup, contact, dispatch, and metadata reads/writes via 8K4 Protocol (ERC-8004). Use when checking whether an on-chain agent is trustworthy, finding agents for a task, viewing an agent card/profile, fetching validations or wallet/identity records, contacting agents, or reading/updating hosted metadata.
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。
Find ITINAI agents, post service agents, and make reviewed A2A service requests.
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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
Query Solana blockchain data with USD pricing — wallet balances, token portfolios with values, transaction details, NFTs, whale detection, and live network stats. Uses Solana RPC + CoinGecko. No API key required.
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。
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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。
Poll RSS, JSON APIs, and GitHub with watermark dedup.
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。
Solve CAPTCHAs with 2Captcha from the command line during browser automation.
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。
Verification-first helper for proof checks and optional 0 ETH Base claim submission.
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。
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
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Embed alibaba/page-agent into your own web application — a pure-JavaScript in-page GUI agent that ships as a single script tag or npm package and lets end-users drive the UI with natural language. No Python, no headless browser, no extension required.
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Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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。