「track」の検索結果

計画や仕様、会話を、単独で動作を確認できる小さな開発チケットに分けます。着手順を決める依存関係を明示し、確認後にローカルファイルや課題管理ツールへ登録します。

• 仕様書を開発チケットに分けたいとき
• チケットの依存関係と着手順を整理
• 広範囲の変更を段階的に進めたいとき

Claude Codeの利用ログからトークン使用量と推定費用を集計し、モデル・セッション・日付別に確認します。予算との比較やCSV出力にも使えます。

• 今日と昨日の支出を比較したいとき
• モデル・セッション別の費用確認
• 予算と支出を比較したいとき

LLM APIの費用を抑えるため、作業の複雑さに応じたモデル選択、予算の記録、一時的なエラーの再試行、共通プロンプトのキャッシュを組み合わせる設計例を示します。

• LLMアプリのAPI費用を見直したいとき
• 一括処理の難易度でモデルを選びたいとき
• 予算に対する累積費用の確認

Track multiple live deals with milestones, deadlines, action items, and status updates. Maintains a deal pipeline view and surfaces upcoming deadlines and overdue items. Use when managing a book of business, tracking process milestones, or preparing for weekly deal reviews. Triggers on "deal tracker", "deal status", "where are we on", "process update", "deal pipeline", or "weekly deal review".

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 and track comprehensive due diligence checklists tailored to the target company's sector, deal type, and complexity. Covers all major workstreams with request lists, status tracking, and red flag escalation. Use when kicking off diligence, organizing a data room review, or tracking outstanding items. Triggers on "dd checklist", "due diligence tracker", "diligence request list", "what do we still need", or "data room review".

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。

Dolt-powered issue tracker for multi-session work with dependencies and persistent memory across conversation compaction. Use when work spans sessions, has blockers, or needs context recovery after compaction. Trigger with "create task", "what's ready", "track this work", "resume after compaction". Make sure to use this skill whenever managing multi-session work, tracking dependencies, or recovering 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。

Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.

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。

Automates the complete Xiaohongshu (XHS / Little Red Book) content operation workflow: pain-point topic collection → style case collection → topic selection → content writing → publishing → performance tracking. Use when user mentions xiaohongshu auto posting, xhs auto post, little red book posting, xiaohongshu post, xiaohongshu auto posting, xiaohongshu content operations, xhs content marketing, post to xiaohongshu, publish on xhs, post on xiaohongshu, xiaohongshu promotion, xiaohongshu operations, xiaohongshu automation, xhs automation, track xhs performance, track xiaohongshu performance, xiaohongshu data tracking, xiaohongshu analytics, switch xhs account, update xhs keywords.

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。

Rank tracking without a tracker subscription — position movers between any two periods from real Search Console data. Use when asked "how are my rankings", "what moved this week/month", "track my keywords", or "did the update hit me".

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。

项目管理里程碑跟踪器（Project Milestone Tracker）——当用户要求代理在代码库/项目会话中规划、跟踪里程碑、记录承诺或事件、生成进度报告时使用。核心：三层里程碑规则（文件依据/客户提供/待确认）+ 原话+时间戳事件记录。Project milestone tracker: plan and track project milestones, record events with original text + timestamp, generate status reports, using a three-tier milestone rule (contract-sourced / stakeholder-stated / pending-confirmation).

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。

Set up and keep a job application tracker: one master CV, every tailored version, where each was sent, its status, next action and follow-up date, plus a weekly review of what is working. Use when asked to track my job applications, organise my job search, which CV did I send to which company, or what should I follow up on this week. Produces a tracker table (markdown or CSV) with one row per application, a version log linking each to its tailored CV, this week's actions, and a short weekly review of response rates by source.

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, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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。

Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.

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。

Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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。

Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract) with SES scaling, k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.

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。

When the user wants to track AI search traffic in GA4 or GSC. Also use when the user mentions "AI traffic," "ChatGPT referral," "Perplexity traffic," "AI Overviews," "GA4 AI sources," "AI search analytics," "track AI referrals," "AI search traffic," "Claude traffic," or "how to track AI traffic." For AI SEO strategy, use generative-engine-optimization.

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。

Design measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use this skill whenever the user wants to plan analytics, design dashboards, build event taxonomies, define KPIs, set up tracking, or audit existing measurement. Triggers on analytics strategy, measurement plan, event taxonomy, tracking plan, KPI framework, dashboard design, north star metric, attribution model, conversion tracking, GA4 setup, Mixpanel setup, analytics audit. Also triggers when the user has data but no clear way to use it, or wants to make decisions but doesn't know what to track.

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 patterns for AnimationPlayer including track types (Value, Method, Audio, Bezier), root motion extraction, animation callbacks, procedural animation generation, call mode optimization, and RESET tracks. Use for timeline-based animations, cutscenes, or UI transitions. Trigger keywords: AnimationPlayer, Animation, track_insert_key, root_motion, animation_finished, RESET_track, call_mode, animation_set_next, queue, blend_times.

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。

Generates a streak tracking system with timezone-aware day boundaries, streak freeze protection, and streak-at-risk push notifications. Use when user wants daily/weekly engagement streaks, consecutive day tracking, or habit tracking.

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 knowledge for Azure Business Process Tracking development including deployment. Use when creating CI/CD pipelines, automating builds, running tests, and deploying tracking solutions via DevOps tools, and other Azure Business Process Tracking related development tasks. Not for Azure Monitor (use azure-monitor), Azure Logic Apps (use azure-logic-apps), Azure Data Factory (use azure-data-factory), Azure Machine Learning (use azure-machine-learning).

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。

Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.

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。

Manages the organisational DPIA register tracking all Data Protection Impact Assessments across the enterprise. Covers DPIA lifecycle management, status tracking, review scheduling, Art. 35(11) periodic reassessment, and supervisory authority reporting. Implements a centralised register linking DPIAs to RoPA entries, risk registers, and mitigation plans. Keywords: DPIA register, DPIA tracking, Art. 35(11), review schedule, DPIA lifecycle, centralised register, DPIA portfolio management.

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。

Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract, SES), k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.

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。

Create and read bigWig browser tracks for visualizing continuous genomic data. Convert bedGraph to bigWig, extract signal values, and generate coverage tracks using UCSC tools and pyBigWig. Use when preparing coverage tracks for genome browsers or extracting signal at specific regions.

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。