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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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Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
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Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
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Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
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Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
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Analyze Chronote product adoption with PostHog and Langfuse, including active users, meetings, feature usage, AI telemetry, and privacy-safe resolution of active Discord guild IDs to live server names.
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Help teams prepare materials for an SCF referral. Use when a team wants to approach a referrer (SCF Pilot, SDF staff, alumni who completed deliverables, ambassador, or ecosystem partner) with a strong package that makes it easy for the referrer to evaluate and endorse them. Covers the referral form fields, what referrers assess, and how to present your project for referral.
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Stellar standards, ecosystem, and reference. Covers SEPs (Stellar Ecosystem Proposals), CAPs (Core Advancement Proposals), and a quick map for picking the right standard for wallets, anchors, payments, deposits/withdrawals, federation, deep links, and KYC. Also bundles ecosystem references (DeFi protocols, dev tools, wallets, infra, community projects), curated documentation links, and MCP servers (live tools such as Raven). Use when you need to know which SEP applies, or want a starting point for ecosystem integrations, official docs, or live MCP tooling.
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Promote QA → production by opening a PR from peterdrier/Humans:main to nobodies-collective/Humans:main with a properly-qualified commit summary. Use when the user says "PR to production", "promote to prod", "PR from origin to upstream", "prod PR", or any variation of pushing batched fork changes upstream. Always use this skill for the two-remote promotion flow even if the user doesn't say "prod" explicitly.
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Hermes Agent (Nous Research の AI CLI エージェント)。インストール、 CLI コマンド (chat, model, gateway, config, cron, webhook, mcp, skills, profile, hermes pm)、 Configuration (Terminal Backends, Memory, TTS / STT, Auxiliary Models, Compression, Display, Local Models llama.cpp, Egress / Secrets)、 Features (Tools, Skills, Hooks, Delegation, Kanban, Cron, Voice Mode, Personality, Context Files)、 Messaging Gateway (Platform Support, Bot Mode, Connectors)、Security、Architecture、 Hermes Desktop、Plugin Catalog、Profiles、Developer Guide、Nous Portal (Tool Gateway)。
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Better Auth (TypeScript 認証フレームワーク) v1.7 リファレンス。 認証・認可、サインアップ・サインイン、セッション管理、OAuth、パスキー、二要素認証 (2FA / TOTP)、SSO / SAML、SCIM、 OAuth Provider (OAuth 2.1 / OIDC)、MCP 認可 (@better-auth/mcp)、Infrastructure (dash / sentinel)、 プラグイン (organization, admin, magic-link, anonymous 等)。betterAuth, createAuthClient, signUp, signIn, getSession。 Next.js, Nuxt, SvelteKit, Hono, Express, Expo, TanStack, Effect 統合。
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Claude Code (code.claude.com) の拡張機能リファレンス。 Agent Skills (SKILL.md), slash commands, output styles, subagents (cross-session messaging), agent teams, agent view, workflows, routines, scheduled tasks, hooks (PreToolUse / PostToolUse), mods (Claude Code mods), plugins, plugin eval, plugin marketplace (marketplace.json), MCP 設定 (.mcp.json / claude mcp add), managed MCP, channels (--channels / claude/channel push events, webhook / telegram / discord / imessage), tools-reference, advisor, ultrareview。
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日本語と英語の文章を ukwhatn 本人の文体で書く・下書きする・推敲する。PR 概要欄、レビュー指摘・返信、Slack・Discord のメッセージ、チーム内ドキュメント、対外ビジネスメール、コミュニティのフォーラム告知とその合意形成の投稿、記事・レポート・議事録・提案書等の長文を日本語で書く時、本人の代わりに文章を書く時、国際的なコミュニティの運営間チャットに英語で投稿する時に使用。AI 特有の冗長さ・難語・翻訳調を排し、相手と媒体に応じて敬体/常体と敬語の重さを切り替える。雑談・times の投稿は対象外。境界: 実装修正を伴う PR コメント対応は pr-comment（本スキルは文章作成のみ）、コミットメッセージは commit、画面の文言は writing-ui-text、コードコメントは writing-code。
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Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.
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開発修正・機能実装後の動作確認→スクショ撮影→テストチェックリスト→修正報告書（md+PDF）作成の一連のワークフロー。 Use when: 「動作確認して」「スクショ撮って」「テスト結果まとめて」「修正報告書作って」「確認してPDFで送って」等の依頼。 開発作業後のブラウザ確認、スクリーンショット収集、テスト結果の文書化、PDF報告書生成に使用。 Don't use when: コードの実装のみ（動作確認・報告書が不要な場合）。デザインレビュー（実装の動作確認ではない場合）。
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Discord の Webhook を使ってメッセージを送信する。
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GitHub issue から計画・実装・テスト・レビュー・PR テキスト・理解確認まで一気通貫で行う。難易度 tier に応じてステップのモデル・effort・スキップを切り替える。
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State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
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