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概要と使いどころ
Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.
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Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
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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.
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Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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Catalog of the AO (Agent Orchestrator) `ao` CLI: spawning workers, managing sessions and projects, creating reusable Cues, sending messages, controlling the shared browser, previewing pages, and daemon control. Use when using the ao CLI, creating Cues, spawning workers, or managing AO sessions in an AO workspace.
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Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider configuration, multi-environment workflows, and infrastructure testing.
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Builds Vue 3 components with Composition API patterns, configures Nuxt 3 SSR/SSG projects, sets up Pinia stores, scaffolds Quasar/Capacitor mobile apps, implements PWA features, and optimises Vite builds. Use when creating Vue 3 applications with Composition API, writing reusable composables, managing state with Pinia, building hybrid mobile apps with Quasar or Capacitor, configuring service workers, or tuning Vite configuration and TypeScript integration.
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Scan open issues and discussions, deduplicate, prioritize, and propose labels. Provides a clean actionable queue.
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当用户询问《纳瓦尔宝典》这本书本身(书名/作者/章节/整书概览/术语)，或涉及本书未独立成 Skill 的主题时调用: 幸福训练/欲望管理、接受与内耗、冥想与心猴、地位游戏与攀比、选朋友与圈子、打工vs创业(委托代理)、 诚实沟通、身份与自我形象、期望与愤怒、生命意义、该不该信玄学(理性验证)、长期复利与声誉、判断力训练。 以下意图优先使用同包独立 Skill: wealth-structure(致富/股权期权)、productize-yourself(职业定位/副业方向)、 decision-heuristics(重大选择纠结)、hourly-rate-time(时薪/外包/时间分配)、reading-metaskill(怎么读书学习)、screen-detox(刷手机上瘾)。 Triggers: 纳瓦尔/Naval/纳瓦尔宝典/幸福/冥想/内耗/攀比/圈子/复利/人生意义
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Distill a book, long-video transcript, podcast, course, or interview into a coherent set of executable skills. Use when the user asks to "拆书" / "蒸馏一本书" / "把 XX 书做成 skill" / "把这个视频/播客/课程蒸馏成 skill" / "turn a book or video into skills" — i.e. wants the frameworks, principles, and methodologies in long-form content extracted into atomic, reusable Claude skills that an agent can invoke in real-world situations. NOT for simple summarization, book reviews, or role-playing as the author (that is nuwa-skill's job).
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Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
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Turn vulnerability notes, disclosure reports, PoCs, source code, or Codex Security findings into self-contained, sceptically validated, natural-sounding vulnerability reports. Use for one vulnerability or a disclosure campaign; a Codex Security scan is optional.
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Read before automating LinkedIn with browser_* tools. LinkedIn combines shadow DOM (#interop-outlet), strict Trusted Types CSP that silently drops innerHTML, Lexical composer, native beforeunload dialogs that hang the bridge, and aggressive spam filters — each has bitten us at least once. Verified flows for profile messaging, connection-request acceptance, feed composition, and search. Requires hive.browser-automation. Verified against logged-in production 2026-04-11.
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Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept replies. Use when the user asks to "set up Slack notifications", "get pinged on Slack", "connect Slack for alerts", "set up Sentinel on Slack", or clicks the in-app "Set this up with the agent" button on the Slack channel step. Requires hive.browser-automation.
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Claim tasks, record step progress, and verify SOP gates in the colony SQLite queue. Applies when your spawn message includes a db_path field.
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Mine bug patterns from any git repository. Discovers bug-fix commits via git log heuristics, analyzes each in parallel with subagents, writes individual analysis files, and synthesizes a generalized PATTERNS.md with repo-specific details stripped. Invoke explicitly with /bug-archaeology.
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Professor's one-time clinic setup — practice areas, jurisdiction, supervision style (formal review queue / configurable flags / lighter-touch), and handbook/rules upload. Writes CLAUDE.md so every other skill and every student who runs /ramp reads from the same clinic context. Use on fresh install, when CLAUDE.md has placeholders, when re-doing setup with --redo, or when re-checking integrations with --check-integrations.
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Draft a unanimous written consent of the board or a committee in house format, with precedent search from the consents repository. Handles multi-resolution consents, director conflict flags, state-law notice requirements, and signatory tracking, with a built-in scope warning for major one-off actions. Use when user says "written consent", "unanimous consent", "board consent", "consent in lieu", "UWC", or describes an action needing board approval without a meeting.
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Diff a new AI regulation or guidance against your current governance posture — surfaces gaps, priorities, and a remediation plan with owners and deadlines. Use when an AI regulation moves (or you learn about one you missed), or when user says "new reg just dropped", "does [regulation] affect us", "gap analysis for EU AI Act", "compliance check against [AI law or guidance]", or pastes regulatory text.
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Drafts board or committee meeting minutes in your house format. Auto-detects upcoming board and committee meetings from your calendar, asks for the agenda and any slides or pre-read materials, and produces a complete draft in the format learned from your seed minutes. Also handles written consents in lieu of meetings. Trigger: "board minutes", "draft minutes", "upcoming board meeting", "committee minutes", "written consent", or calendar detection of an upcoming board or committee event.
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自媒体创作者与电商运营在推广美妆护肤产品时，当需要创作或优化小红书笔记、生成AI配图提示词时，使用此技能可一键生成高转化爆款笔记、吸睛标题与精准标签，自动产出专业文生图提示词，轻松打造小红书爆款内容。
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Generate properly-formatted SKILL.md files from extracted architectural patterns. Turns raw pattern descriptions into reusable skills that OpenSpace can discover, select, and evolve.
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。