'attention' 검색 결과

Draft customer replies using order details, payment status, support history, and refund policies, matching the owner's writing style. With connected tools, prepare approved refunds and identify Shopify orders that need attention before customers contact support.

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• Drafting replies to order status questions
• Preparing policy-based responses to refund requests
• Checking previous support exchanges before replying

Turn project or team updates into a structured report for leaders and stakeholders. The skill summarizes key metrics, accomplishments, ongoing work, risks, decisions needed, and next-period priorities, with an overall assessment of whether work is on track.

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• Write weekly, monthly, or quarterly progress reports
• Summarize progress against key metrics and targets
• Highlight risks and decisions needing stakeholder attention

Choose one North Star Metric, a central measure of customer value, and three to five supporting measures that influence it. Classify the business by how customers use it and assess the proposed metric against seven criteria.

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• Choosing a central measure of customer value
• Organizing the metrics a team tracks
• Comparing candidates for a North Star Metric

Sort a Superhuman inbox into messages needing a reply, waiting for others, useful information, and noise. Receive a clear attention list, then apply requested actions such as archiving, labeling, reminders, or unsubscribing.

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• Finding messages that need a reply
• Reviewing requests awaiting a response
• Sorting newsletters and notifications

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

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• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Reduce LLM generation delays with speculative decoding, which proposes tokens ahead of verification. This skill compares small draft models, Medusa, and Lookahead Decoding, with implementation examples, tuning guidance, and deployment using vLLM.

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• Reducing response delays in chatbots
• Speeding up code generation
• Choosing a small model to draft candidate tokens

Classify agent work as ready to continue, needing human judgment, or intentionally paused. Structure decision requests with evidence, a recommended choice, and clear outcomes, then connect the recorded decision to the next responsible agent.

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• Deciding whether agent work needs human review
• Preparing decision requests with evidence and recommended choices
• Designing a dashboard for pending human decisions

Estimate how model architecture, precision, and context length affect weight storage, generation state, computation, and memory traffic. Compare models and attention designs with separate resource estimates for input processing and token generation.

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• Compare memory needs across model architectures
• Estimate resource costs of longer contexts
• Compare attention designs and expert-based models

Refactor claude-flow into an extension of agentic-flow@alpha to reduce duplicate implementations. Plan adapter layers, migrate agent and task management, and connect learning, shared memory, and tools while checking compatibility with existing functionality.

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• Consolidate duplicate agent and task management code
• Design an adapter that preserves existing APIs
• Integrate learning, shared memory, and MCP tools

Configure and validate CUDA graphs in Megatron Bridge training. Choose whole-iteration or module-level capture, which records GPU operations for replay, then check compatibility constraints and compare runtime and memory use against ordinary execution.

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• Choose whole-iteration or module-level CUDA graph capture
• Configure graph scopes for dense or MoE training
• Investigate crashes after CUDA graph configuration changes

A soft expanding ring that draws the eye to ONE element — a primary CTA, an unread badge, a new dot. Honors prefers-reduced-motion. One per screen; never use more than one at once or as pure decoration (attention splits and nothing wins).

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Redesign a learning space using biophilic design principles to improve focus, calm, and wellbeing. Use when classroom environment contributes to restlessness, poor attention, or stress.

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Operate Flash Linear Attention package workflows: setup, kernels, layers/models, KDA/context parallel, and benchmarking.

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Use jadore801120 attention-is-all-you-need-pytorch for Transformer architecture inspection, preprocessing, training, and checkpoint translation workflows.

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Analyze GitHub and Discord signals to prioritize maintainer attention. Use when asked about priorities, what's hot, what needs attention, or project status.

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Use @aleabitoreddit ("Serenity")'s full mention archive (built by the follow-aleabito skill) to anticipate where her attention is moving and generate candidate ideas in her style. Two modes — (1) RADAR reads the live mention data for attention momentum (which tickers she is heating up on, new entrants, conviction core, theme rotation) via scripts/radar.js; (2) GENERATOR applies her empirically-mined patterns (theme-rotation logic, selection signature, catalyst playbook) to propose her likely next focus. Every candidate is gated through the serenity-method checklist. This is a CANDIDATE GENERATOR + CHECKLIST, never an oracle or buy/sell signal. Trigger on "what is Serenity ramping on / her next pick / aleabito radar / predict her next move / generate ideas like her / 她下一个可能看什么".

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Redesign a learning space using biophilic design principles to improve focus, calm, and wellbeing. Use when classroom environment contributes to restlessness, poor attention, or stress.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.

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Geração estruturada rápida e serving para LLMs com RadixAttention para cache de prefixos. Use para saídas JSON/regex, decodificação restrita, workflows com agentes que fazem chamadas de ferramentas, ou quando você precisa de 5× mais rápido que vLLM com compartilhamento de prefixos. Alimenta 300.000+ GPUs na xAI, AMD, NVIDIA e LinkedIn.

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Serve LLMs com alta throughput usando PagedAttention do vLLM e continuous batching. Use ao fazer deploy de APIs LLM em produção, otimizar latência/throughput de inferência, ou servir modelos com memória GPU limitada. Suporta endpoints compatíveis com OpenAI, quantização (GPTQ/AWQ/FP8) e tensor parallelism.

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claude-org-runtime のリリース (release-* タスク / vX.Y.Z タグ発行) を、 同 PyPI 発行を受ける claude-org-ja 側の expectation 同期と ペアで設計・委譲・完了させるためのワークフロー。 DEFAULT_NOTIFY 値・classifier vocabulary・role_configs_schema (ja 側ミラー: org_extension_schema)・attention.example.json テンプレ等が 変わるリリースで発動する。 CI cascade を予測・予防し、ja-side の red を回避することを目的とする。

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組織の生きているペイン（secretary / dispatcher / worker）へ tmux で**直接入る** ための **コマンドを出力するだけ** の read-only スキル。`mcp__org-broker__list_panes`（論理ペイン） と `/usr/bin/tmux -L claude-org-broker list-panes -a`（pane_id ↔ session マッピング）を pane_id（%N）で突き合わせ、各ペインに role + name(task_id) ラベル付きの attach コマンドを 生成する（既定は読取書込 = `-r` 無し / 読取専用 `-r` はオプション / デタッチは `Ctrl-b d`）。 外側が tmux のときに今の tmux を分割してワーカー（と dispatcher）を並べる `split-window` コマンドも出す。 特定のペインの画面を直接見たい・入りたい・並べて見たい、attach コマンドを知りたいという依頼で発動。 自分で attach / split はせず・ペインも一切変更しない（コマンド文字列とペイン一覧表を表示するのみ）。 作業委譲（org-delegate）・ダッシュボードで状況一覧（org-dashboard）・watcher 停止（org-attention-stop） には発動しない。

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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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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.