232 件 ・ 関連度順
概要と使いどころ
Use when detecting and running project build systems automatically. Supports npm/yarn/pnpm/pip/poetry/gradle/maven/cargo/go/make. Triggers on build, test run, project setup, package manager detection.
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 starting Claude Code projects, writing CLAUDE.md/spec.md, dispatching subagents, or requesting Agent Teams parallel development. Covers Spec-Driven Development, Context Engineering, and post-dev 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。
Iteratively refine a product spec by debating with multiple LLMs (GPT, Gemini, Grok, etc.) until all models agree. Use when user wants to write or refine a specification document using adversarial development.
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。
Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.
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。
Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.
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。
Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.
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。
Still-to-video conversion guide: model selection, motion prompting, and camera movement. Covers Wan 2.5 i2v, Seedance, Fabric, Grok Video with when to use each. Use for: animating images, creating video from stills, adding motion, product animations. Triggers: image to video, i2v, animate image, still to video, add motion to image, image animation, photo to video, animate still, wan i2v, image2video, bring image to life, animate photo, motion from image
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。
Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.
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。
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.
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。
Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project context.
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。
检测一个 IP 或住宅/机房代理节点的质量——注册库、地理库一致性、ASN/org、风控信誉、黑名单、住宅真实性、BGP 宣告、目标服务(Grok/Claude/ChatGPT)解锁与延迟三角测量。判定该 IP 能否安全用于 AI 服务(避免被地理库误判到别国导致区域锁)。当用户说"查这个 IP"、"这个节点在哪"、"这个代理能用吗"、"IP 质量检测"、"验收住宅 IP"、"为什么被判定在 X 国"、"check ip"、给出 socks5 代理凭证问归属或解锁时使用。
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。
Фейбл руки-агенты — ручной режим оркестрации, где Fable не пишет код, а пишет спеки в тела GH issues и раздаёт готовые задачи рукам: кодинг, код-ревью, gh-операции, CLI-механика, DoD-верификация и укладка файлов — Codex; разведка, чтение и скауты — Grok-воркеры через Orca. Вызывается пользователем явно, не моделью.
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。
Think and work like an expert Deep Learning Scientist. Use when a task calls for Deep Learning Scientist judgment. Reasons from CNN/Transformer inductive bias, Li et al. loss landscapes, grokking/mode connectivity, and Kaplan/Chinchilla scaling (~20 tokens/param); designs ResNet/ViT/DiT/MoE/FlashAttention stacks with FLOPs-matched ablations; trains AdamW+cosine/WSD via Megatron-FSDP/DeepSpeed; evaluates FID/MMLU-Pro/MMLU-CF with lm-eval decontamination and Pineau/NeurIPS reproducibility checklists.
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 installing, bootstrapping, configuring, starting, verifying, operating, or troubleshooting Looper, looperd, the looper CLI, ~/.looper config, or runtime paths; when setting up Looper with opencode, claude-code, codex, cursor-cli, Grok Build, Pi, or Oh My Pi (omp); when registering repos or configuring planner/reviewer/fixer/worker loops; or when diagnosing status, logs, osascript, git, gh, LOOPER_TOKEN, writable path, or daemon startup issues.
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。
Compact reference for the Hyperliquid API as the desk uses it - endpoints and envelopes, every /info request type, every /exchange action with its signing scheme, order and status vocabularies, asset ids, tick and lot rules, rate limits, WebSocket subscription list, error strings, and where the official docs are. Use to look up an exact field, request type or limit before writing a call, and to map an error string to its cause.
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。
Less common Hyperliquid actions and their rules - dead-man's switch (scheduleCancel), TWAP orders, spot orders, expiresAfter and nonces, API wallet approval from code, sub-account and vault addressing, HIP-3 dexs, and what the desk deliberately does not do (transfers, withdrawals, builder fees, staking). Write actions are Execution Trader only, on an approved ticket. Use when a ticket asks for one of these or when a user asks whether the desk can.
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。
Read a Hyperliquid account from the desk computer - positions and margin, spot balances, open orders including trigger details, fills, funding paid, ledger updates, order status by oid or cloid, historical orders, portfolio history, fee tier and rate-limit budget - with curl and Python SDK examples. Read-only, needs only the account address. Use for sizing inputs, book checks, reconciliation and reviews.
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。
Place, cancel and modify Hyperliquid orders correctly from the desk computer - limit and IOC (market-style) orders, take-profit and stop-loss trigger orders with grouping, client order ids, reduce-only, batch actions, price and size rounding, and how to read every response status. Write path - Execution Trader only, on an approved ticket. Use for any order action and for reconciling by cloid.
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。
Manage Hyperliquid perp positions and margin from the desk computer - read positions and margin, set leverage and cross/isolated mode, add isolated margin, understand margin tiers and liquidation price, close a position with a reduce-only IOC, and clean up orphaned orders. Write actions are Execution Trader only, on an approved ticket. Use for leverage changes, closes, protection checks and margin questions.
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。
Prepare the desk computer to work with Hyperliquid - install the SDKs, pick testnet or mainnet, verify connectivity, and (only when the user asks) provision a trade-only API wallet through the secure secret store and verify it is approved. Use during desk setup, when moving between research, testnet and mainnet levels, when a key is rotated, or when any Hyperliquid call fails with an environment problem.
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。
Subscribe to live Hyperliquid data over WebSocket from the desk computer - mids, order book, trades, candles, best bid/offer, and per-account fills, order updates and events - with raw JSON, Python SDK and TypeScript examples, plus how to run a supervised watch that logs to a file and alerts. Read-only. Use for monitoring, fill notifications and any watch that polling would make expensive.
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。
How the Risk Manager writes the desk's risk limits with the user, sizes every proposed trade from live account state and Hyperliquid's real constraints, checks the book, and issues a PASS or REJECT with exact ticket fields. Use for setting up or changing limits, sizing any trade, and answering "how's the book".
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。
How the Strategist works with the user to turn their own trading idea into explicit rules, backtest it honestly on Hyperliquid candle and funding history, and paper-trade it on testnet through the desk lifecycle. Method only - the desk ships no strategies and makes no return claims. Use when the user wants to design, test, compare or paper-trade an idea.
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。
Read live Hyperliquid market data from the desk computer with curl or the Python SDK - mid, mark and oracle prices, order book depth, funding (current, predicted, historical), open interest, volume, candles, perp and spot metadata, margin tiers, and how to save datasets for the strategy lab. Read-only, no key. Use for any market brief, depth read, funding question or data pull.
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。