「attention」の検索結果

メールやウェブサイト、ローカルファイルから進捗を集め、定期更新するダッシュボードを作成。要対応事項や変更履歴をまとめ、重要な変化があったときに通知します。

• メールとサイトで申請状況を追いたいとき
• 商談や配送の進捗を一か所で確認
• 重要な状況変化の通知を受けたいとき

Review a connected Paperclip team's agents, tasks, blockers and pending approvals when the user asks for a team summary or what needs attention.

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。

Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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。

Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.

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。

Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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。

Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.

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。

Replace the manual commit-by-commit classification step in `breeze release-management prepare-provider-documentation` with AI-driven classification. For each provider with pending changes, analyze every PR (batched into one sub-agent per provider, not one per PR), pay special attention to potentially breaking changes by inspecting the actual diff, scope multi-provider PRs to the current provider's slice, ask the release manager when uncertain, and apply version bumps + changelog entries. Use during the regular provider release cycle as an alternative to the interactive breeze prompts.

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。

Triage open herdr GitHub issues into a concise decision-first Markdown table. Use when the user says "triage", asks to triage open issues, asks which issues need attention, or wants issue priority/recommendation lights for herdr.

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 and maintain a calendar of upcoming catalysts across a coverage universe — earnings dates, conferences, product launches, regulatory decisions, and macro events. Helps prioritize attention and position ahead of events. Triggers on "catalyst calendar", "upcoming events", "what's coming up", "earnings calendar", "event calendar", or "catalyst tracker".

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。

Naming conventions for SGLang speculative decoding identifiers. Use when adding, renaming, or reviewing identifiers in speculative decoding code — anything under `python/sglang/srt/speculative/`, related attention backends, scheduler accumulators, IPC fields, observability metrics, or CLI flags.

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。

Identify focused, evidence-backed skills CLI bug fixes worth quuu's attention.

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。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

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。

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

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 Google Tasks that are past due and need attention.

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。

Run `gbrain skillpack-check` to produce an agent-readable JSON health report for the gbrain install. Wraps `gbrain doctor` + `gbrain apply-migrations --list` so a host agent (your OpenClaw's morning-briefing, any OpenClaw cron) can see at a glance whether the skillpack needs attention. Use when the user asks "is gbrain healthy?", when a cron fires a morning check, or proactively when something seems off (jobs not running, brain not updating, autopilot silent).

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。

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

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。

Reconciles the accounting ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) against PayPal, Shopify, Square, and Stripe settlements, flags transactions that need attention, suspicious duplicates, and missing receipts, then writes a plain-English P&L narrative and exports a close packet (xlsx + one-page PDF). This is the first link of the /close-month command; a request to close the month or the books routes there, and the command runs this skill before refreshing the forecast and distributing the packet. Use this skill directly only when the owner wants the reconciliation alone, with no forecast refresh and no distribution: "just reconcile, no packet," "what's missing from the books," "flag the duplicates and missing receipts," or "write the P&L narrative for this month."

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。

Produces a one-page cross-functional business snapshot for SMB owners — cash position (the ledger: MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), sales trend (PayPal/Square/Stripe), pipeline movement (HubSpot), this week's commitments (Calendar), urgent watch-list items (Gmail or M365, Slack), and the single most important thing needing attention today. Proactively tries every available connector and gracefully scopes to whatever is connected — one connector gives a partial pulse; the full stack gives the full picture. Trigger when the user asks how the business is doing, wants a snapshot, a daily brief, a Friday recap, or says anything like "what am I missing" or "catch me up on the business." A start-of-week briefing routes to /monday-brief, which runs this skill as its first link.

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。

Generate a status report with KPIs, risks, and action items. Use when writing a weekly or monthly update for leadership, summarizing project health with green/yellow/red status, surfacing risks and decisions that need stakeholder attention, or turning a pile of project tracker activity into a readable narrative.

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。

Package an escalation for engineering, product, or leadership with full context. Use when a bug needs engineering attention beyond normal support, multiple customers report the same issue, a customer is threatening to churn, or an issue has sat unresolved past its SLA.

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。

Define a North Star Metric and 3-5 supporting input metrics that form a metrics constellation. Classify the business game (Attention, Transaction, Productivity) and validate against 7 criteria for an effective North Star. Use when choosing a North Star Metric, setting up a metrics framework, learning about the North Star Framework, or deciding what to measure.

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。

This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting 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。

This skill should be used when the user asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical agents.

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。

MNN Vulkan 后端 kernel/算子性能优化与新特性集成。覆盖 benchmark 基线、kernel 优化迭代、集成验证全流程，以及 GLSL .comp + makeshader 双轨、conv1x1/attention dispatcher 多路径（coopMat/subgroup/nosubgroup）、cooperative matrix、packed weight 设计、pipeline cache、CPU 侧调度瓶颈、Adreno/Mali/Apple 多 vendor 真机验证等参考知识。

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。