3,273 件 ・ 関連度順
概要と使いどころ
Competitor intelligence system. Research competitors across web, Reddit, Twitter/X, LinkedIn, and blogs. Build deep competitor profiles, monitor content and positioning changes, track what gets traction, and identify competitive gaps. Covers data collection, content tracking, and strategy analysis. Pure research skill — uses web search, web fetch, and optionally Apify for social scraping. No scripts required.
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。
Assemble a stop-motion hand-swatch-cycle product-demo ad from a config — a sequence of still PLATES (one hand swiping a single-barrel product across a test surface, the barrel + swatch changing per plate while the hand, background, crop, and lighting stay locked) is PNG→mp4 loop-encoded at each plate's own stop-motion hold (fast motion frames 150–250ms, per-shade ~380ms, hero beats 1100–1800ms), concat-demuxed with HARD cuts into a silent master, closed on a Playwright HTML-rendered branded end card (serif tagline + sans subtitle + real logo SVG over a hero BG, never AI-rendered text), and muxed with a pre-sourced music track playing under the end card with a fade tail (no VO). This is the FREE deterministic assembly stage (loop-encode + concat-demux + end-card render + music mux); the master-anchor plate, shade plates, and end-card BG come from create-image-gpt-image-fal and the track from create-music-elevenlabs. Use for the stopmotion-hand-swatch-cycle format.
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。
Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.
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。
Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.
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。
Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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 circular genome visualizations using circlize (R), pyCirclize (Python), or Circos (Perl CLI) with ideogram tracks, multi-data tracks (scatter, histogram, heatmap), chord/link arcs for interactions, and explicit circos.clear() between plots. Covers when circular is appropriate vs when Cartesian wins (Cleveland-McGill 1984), karyograms, and chromosome adjacency in chord diagrams. Use when adjacency on the circle conveys meaning — chromosome-level overview, structural variants, Hi-C interactions, cross-genome comparisons.
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。
Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
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。
Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.
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。
Access Core Motion accelerometer, gyroscope, magnetometer, device-motion, pedometer, activity-recognition, altitude, headphone motion, batched high-frequency workout motion, and water-submersion/depth data. Use when reading device sensors, counting steps, detecting walking/running/driving/cycling, tracking altitude, building motion interactions, handling AirPods head tracking, or implementing watchOS dive/depth features.
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 driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.
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。
Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early.
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。
TikTok Shop analytics and performance tracking. Video metrics, live selling data, affiliate performance, advertising analytics, and business intelligence. Use when the user asks about TikTok Shop analytics, performance tracking, metrics analysis, or data-driven 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。
Brand monitoring tool for tracking mentions across social media platforms. Monitor Reddit, Google News, YouTube, and DuckDuckGo for brand mentions. Includes sentiment analysis, trend tracking, crisis detection, and competitor comparison. No API key required for basic monitoring.
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。
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.
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。
Use when adding sound to a video or generating standalone audio with Sonilo models on Scenario via MCP: text-to-sound-effects, text-to-music, video-to-sound-effects or video-to-music from a clip, scoring silent AI video, foley, ambience, muxed video-to-video variants returning the clip with the new track mixed in, keeping speech while replacing music, or per-segment sound control. Keywords: Sonilo V1.1, SFX, soundtrack, score, keepSpeechVocal, segments, royalty-free.
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。
Use when generating or handling audio on Scenario via MCP. Triggers include music tracks, full-length songs with vocals written from lyrics, background scores, soundtracks, game sound effects, SFX, foley, ambience, looping audio, voiceover, narration, speech, TTS, text-to-speech, dialogue, voice cloning, re-voicing a recording, scoring or adding sound to a video, transcription, or requests to create, wait on, play, or download audio files (MP3, WAV) with Scenario tools.
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。
Use when turning a song, track, or audio master into a finished music video with Scenario and Seedance: planning shots against beats and sections, transcribing lyrics, generating clips that cut to music, keeping the shots' own sound while the song stays the only score, assembling a delivery over the untouched master soundtrack, or verifying the final cut. Keywords: music video, beat sync, shot list, lyric transcription, reference frames, ffmpeg assembly, Seedance.
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。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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。
Track and report on progress toward defined goals
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。
Scan codebases for technical debt with AST parsing, prioritize by impact, and generate trend dashboards. Use when tracking tech debt, prioritizing refactoring, calculating cost-of- delay, planning sprint debt, or reporting debt to execs.
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。
Systematic competitor tracking that feeds positioning, battlecards, and roadmap decisions. Use when analyzing competitors, building battlecards, tracking market moves, conducting win/loss analysis, or updating positioning.
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。
Deprecated; use rank-monitor --features for SERP features. "track SERP features"
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。
Track the brand narrative in AI engines against desired positioning, flag misstatements. "is AI misrepresenting our brand"
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。