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概要と使いどころ
Produce and operate Amazon Nova Reel video-generation jobs through Amazon Bedrock. Use for Nova Reel text-to-video, image-conditioned animation, automated or manual multi-shot storyboards, async S3 delivery, cost and approval gates, production continuity, output custody, and AWS-specific safety, privacy, IAM, and provenance decisions.
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。
Plan, prompt, execute, troubleshoot, and quality-control Black Forest Labs FLUX image generation and editing across the BFL direct API and licensed local/open-weight deployments. Use for FLUX.2 model selection, text-to-image, single- or multi-reference editing, typography, exact-color work, mask-based erase/inpainting, outpainting, API integration, reproducibility, deployment licensing, and production rights or safety decisions; also use when migrating or maintaining legacy FLUX.1/FLUX1.1 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。
Provider-independent production workflow for creating Manim-based explainer animations. Use when an agent must plan, code, render, QA, or hand off precise math, science, data, diagram, algorithm, or concept animations with Manim, including storyboard, scene architecture, formulas, coordinate systems, voiceover timing, accessibility, reproducibility, provenance, and delivery decisions.
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。
Provider-independent FFmpeg finishing workflow for AI agents preparing generated or edited media deliverables. Use when finalizing images, image sequences, video, audio, captions, overlays, social/export variants, checksums, manifests, delivery specs, and QA with ffmpeg/ffprobe, including transcode versus stream-copy decisions, scaling, frame-rate handling, color metadata caution, loudness normalization, subtitle burn-in or sidecars, concat/trim, GIFs, batch variants, and reproducible command reporting.
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。
Provider-independent audio mixing and mastering direction for AI agents finishing generated videos, ads, trailers, explainers, podcasts, recuts, avatar clips, music videos, documentaries, and social content. Use when planning, mixing, repairing, mastering, QCing, or delivering dialogue, music, ambience, and sound effects, including loudness/true-peak targets, intelligibility, accessibility, stems, stereo/immersive decisions, platform/client specs, and final audio QA.
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。
Provider-independent asset continuity and version management for generated-media production. Use when an agent must track generated or reference assets, prompts, seeds, model/tool versions, manifests, approvals, edit decisions, derivatives, localization variants, metadata/provenance, and continuity QA across image, video, audio, avatar, product-ad, social, explainer, or film-style pipelines.
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。
Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).
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。
Prepare for sales conversations with comprehensive account intelligence — company news, hiring signals, product launches, social activity, and decision-maker profiles. Combines firmographic data, news monitoring, and social signals for sales-ready briefings.
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。
Analyze influencer performance metrics across TikTok, Instagram, YouTube, Bilibili, and Kuaishou. Compare engagement rates, audience growth, content consistency, and cross-platform presence for data-driven partnership decisions.
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 or review revenue operations deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for revenue operations planning or analysis.
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 or review team composition analysis deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for team composition analysis planning or analysis.
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 or review meeting briefing deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for meeting briefing planning or analysis.
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 or review lean canvas deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for lean canvas planning or analysis.
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 or review legal risk assessment deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for legal risk assessment planning or analysis.
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 concise, actionable meeting minutes for internal meetings. Includes metadata, attendees, agenda, decisions, action items (owner + due date), and follow-up steps.
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 or review journal entry prep deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for journal entry prep planning or analysis.
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 or review brainstorm experiments new deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for brainstorm experiments new planning or analysis.
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 or review close management deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for close management planning or analysis.
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 or review call prep deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for call prep planning or analysis.
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 or review business model deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for business model planning or analysis.
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 or review daily briefing deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for daily briefing planning or analysis.
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 or review audit support deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for audit support planning or analysis.
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 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
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。
Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the p53-dependent G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.
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。