「segment」の検索結果

Outbound email-marketing desk (Busabase App-in-Skill) for building segments, drafting campaigns, newsletters, and sequences, running pre-send deliverability and subject-line QA, and approving every send before it is scheduled. Structured around the SEND discipline — Setup, Engage, Nurture, Deliver — with an email-quality-auditor gate (EQS score + SHIP/FIX/BLOCK verdict). Use when the user invokes $kelly-campaigns or /kelly-campaigns, or mentions email marketing, campaigns, newsletters, drip/welcome/win-back sequences, broadcasts, segments, subject-line A/B tests, deliverability (SPF/DKIM/DMARC/spam score/inbox placement), or wants to review and approve marketing email before it is sent. This is OUTBOUND marketing to a subscriber list, distinct from kelly-email inbox triage (incoming mail). 出站邮件营销：策划分群、起草营销活动 / 新闻邮件 / 邮件序列，发送前做可送达性与主题行质检，人工审批后再排期发送。

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Configures pfSense firewall rules, NAT policies, VPN tunnels, and traffic shaping to enforce network segmentation, control traffic flow, and protect internal network zones in enterprise and small-to-medium business environments.

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。

Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.

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。

Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-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。

Pick the right business-model canvas (Lean Canvas, Business Model Canvas, or Value Proposition Canvas) for the stage and fill it with specifics — one segment, one primary canvas, top-3 assumptions, no fluff in the moat or channel boxes. Load when the user asks to fill a business model canvas, lean canvas, value proposition canvas, model this business, map the business model, says "fill the BMC", "make a Lean Canvas", "Value Proposition Canvas for this", "model this idea", "what's the business model", "design the business model". Sub-skill of `venture-exploration`. Hard-bans "everyone" segments, generic channels ("SEO/social/content/ads"), and "unfair advantage = AI/data/network effects" with no concrete asset. Does NOT score viability — for that use `idea-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。

Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.

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。

Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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。

Research markets with sizing, segmentation, competitor mapping, pricing checks, and demand validation that turn fuzzy ideas into decision-ready evidence. Use when (1) you need TAM, SAM, SOM, whitespace, or category sizing; (2) you must compare competitors, pricing, positioning, or customer segments before acting; (3) the user asks whether a niche, launch, expansion, or go-to-market bet is actually worth pursuing.

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。

Configures pfSense firewall rules, NAT policies, VPN tunnels, and traffic shaping to enforce network segmentation, control traffic flow, and protect internal network zones in enterprise and small-to-medium business environments.

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 and refine ICP segments with persona cards and validation

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。

家庭や小規模ラボのネットワーク変更前に、機器構成と復旧手順を整理するスキル。VLANによる分離、DNSフィルタリング、VPN導入を段階的に計画・点検します。

• 普段使う端末とIoT・来客端末の分離
• DNSフィルタリングへの移行計画
• VPNによる遠隔アクセスの見直し

複数店舗の販売データや商品の特性をもとに、需要予測、安全在庫、発注量を検討するスキル。販促後の需要減少や季節商品の値下げ時期まで計画を支援します。

• 商品ごとの需要予測方法の選定
• 仕入れ先の納期変更に伴う安全在庫の見直し
• 販促中と終了後の需要を見積もりたいとき

撮影済みの動画や画面収録から見どころを選び、カット・構成整理・字幕や音声の追加を進めます。長い録画の短編化からSNS向けの画角調整、編集ソフトでの仕上げまで扱うスキルです。

• 長い録画から見どころを切り出したいとき
• 撮影素材からVlogを作りたいとき
• 製品デモに文字やナレーションを加えたいとき

Analyze images — segment objects, detect, run OCR, describe, and answer visual questions via fal.ai vision models.

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。

Segmented logo assembly, glow bloom, and tagline reveal for video outros or brand closing frames.

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。

Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Composio

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 and manage the user's Klaviyo email and SMS marketing campaigns, newsletters, flows, subscriber lists, and audience segments. Use to compare campaign performance and revenue, find recent customers, plan audience targeting, and manage marketing content and subscriptions through Klaviyo's official MCP server.

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Edit videos locally using ffmpeg. Trim, concat, resize, speed, overlay, extract audio, compress, and convert. Use when: (1) Trimming or cutting video segments, (2) Concatenating multiple clips, (3) Resizing video for social platforms, (4) Extracting or replacing audio, (5) Compressing video, (6) Converting video formats, (7) Getting video info.

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House-style oversized macOS cursor technique for HyperFrames launch videos. Load whenever a scene involves cursors or a pointer-led action, when kicking off a UI scene, when igniting a morph/transition/typing run with a click, or when a scene reads as static, dead, or stale and needs a cheap high-yield source of motion to carry the viewer's eye and segment them out of the stale state. Covers cursor size/look (incl. brand-motif cursors), the off-screen entry law, tip-targeting and the click tap, click-ignites-the-next-beat, and exit / cross-scene handoff.

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For choosing attribution models, comparing multi-touch/MMM/incrementality, or reconciling conflicting numbers across tools, see attribution. For A/B test measurement, see ab-testing. Rate metrics divide matching closed windows — never full-period spend by partial-period results. Rate metrics divide matching closed windows, never full-period spend by partial-period results.

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Command-line interface for CloudCompare — Agent-friendly harness for CloudCompare, the open-source 3D point cloud and mesh processing software. Supports 41 commands across 9 groups: project management, session control, point cloud operations (subsample, filter, segment, analyze), mesh operations, distance computation (C2C, C2M), transformations (ICP, matrix), export (LAS/LAZ/PLY/PCD/OBJ/STL/E57), and interactive REPL.

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。

Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.

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 time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

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。