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概要と使いどころ
Long short portfolio construction, market neutral positioning, factor-balanced book, gross and net exposure management, pair selection, beta hedging, portfolio construction methodology
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。
Select, integrate, and operate multi-model video-generation gateways — hosted inference aggregators such as fal.ai, Replicate, and WaveSpeed that expose many third-party video (and video-adjacent audio) models behind one account, one API surface, and one bill. Use when deciding whether to route video generation through a gateway versus a direct model-provider API; when comparing gateways on catalog, async/queue job design, webhooks, pricing, schema discovery, and version pinning; and when operating gateway workloads in production — spend controls, retries, cross-gateway failover, content- safety differences, data retention, and commercial-rights passthrough of the underlying model. Not for direct model-provider APIs, local/self-hosted inference, model training/fine-tuning, or image-only gateway use.
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 Microsoft Azure Speech in Foundry Tools for media-production speech workflows: speech-to-text, fast and batch transcription, diarization, captions/subtitles, real-time transcription, text-to-speech neural and HD voices, SSML, custom/personal voices, text-to-speech avatars, speech/video translation, localization, quotas, regions, privacy, consent, rights, and production 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。
Produce speech and clone voices with Fish Audio — its hosted TTS API (S2.1-Pro / S2-Pro / S1 model lineup, REST + WebSocket streaming, instant and persistent voice cloning) and its open-weight OpenAudio S1-mini / Fish-Speech models for self-hosting. Use this skill when an agent must generate narration or dialogue through Fish Audio, choose between Fish Audio's hosted models and open weights, clone a voice from reference audio, author emotion/tone/special markers for expressive delivery, estimate cost from UTF-8 bytes, wire real-time streaming for a voice agent, decide whether self-hosting beats the API, or review Fish Audio TTS output for production. Do not use it to pick a different provider — it covers Fish Audio specifically.
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 Amazon Polly for production text-to-speech work: selecting Standard, Neural, Long-form, or Generative engines and compatible voices; authoring SSML; creating speech marks for captions, word highlighting, or lip-sync; managing pronunciation lexicons; running synchronous, streaming, or asynchronous S3-backed synthesis; planning quotas, pricing, IAM, privacy, and QA for narration, audiobooks, accessibility audio, avatars, and multilingual media.
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 3D assets with Tencent's Hunyuan3D family — open-weight self-hosted models (Hunyuan3D-2.0/2.1 and HunyuanWorld / HY-World for scenes) and the hosted, closed API tiers (2.5, PolyGen, 3.0/3.1 via Tencent Cloud and third-party hosts). Use when a task involves image-to-3D or text-to-3D mesh generation, the shape-then-texture (DiT + Paint / PBR) workflow, choosing between self-hosting and a hosted API, checking the Community License's Territory (EU/UK/South Korea) and 1M-MAU commercial gate, sizing GPU/VRAM for local inference, ComfyUI integration, or reviewing and post-processing (retopology, UVs, decimation) the meshes these models produce. Not for image, video, or general LLM 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。
Produce anime-style and stylized 2D animation with generative image and video tools as a craft discipline, not a filter. Use when a request asks for anime, manga-style, cel-shaded, sakuga, shonen/shojo/seinen/slice-of-life, mecha, or retro-90s-OVA animation; when planning shots, motion, and cutting rhythm to anime convention; when holding a character on-model and preventing style drift across shots or episodes; when choosing prompting vocabulary that actually controls anime look; when pairing voice, music, and impact SFX to stylized action; and when navigating the cultural, IP, and platform-policy sensitivities of AI anime (studio/artist style imitation, fan-art and IP boundaries, disclosure and monetization). This is provider-neutral; models are named only as illustrative options.
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。
Production guidance for reusable layered 2D character rigs in browser-rendered and video media. Use for SVG hierarchies, pivots and constraints, FK/IK planning, pose and facial libraries, acting, cycles, deterministic handoff, crop variants, and rig QA; not for character concept continuity, 3D armatures, or motion-capture solving.
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 character design and continuity direction for generated media. Use when an agent must create, preserve, revise, or QA a recurring character across AI-generated images, video clips, avatars, animation, comics, ads, product mascots, serialized social posts, or multi-shot campaigns; especially when building character bibles, reference packs, prompts, expression/pose ranges, costume/hair/makeup/prop continuity, identity and likeness safeguards, iteration plans, handoff notes, or continuity reviews.
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 this skill for provider-independent reality capture that turns photographed or video-derived real places, objects, aerial sites, interiors, or people into photogrammetric meshes, NeRFs, or 3D Gaussian splats. It guides representation choice, capture planning, calibration and scale, reconstruction or training, cleanup, compression and LOD, coordinate and interchange handoff, visual/geometric QA, archival records, and property, location, people, and cultural rights checks. Do not use it for text-to-3D generation, synthetic asset invention, guaranteed metrology, or broad downstream finishing after handoff.
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。
Facilitate a structured conversation to define language-specific idioms and patterns for a repository. Produces a language-idioms.md document consumed by multiple atoms to adapt pseudocode defaults to the project's language. Use when setting up a new project, switching languages, or when the user says 'setup language', 'define language idioms', 'configure language', 'language patterns', or 'adapt for Go/Rust/Python'.
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。
Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery.
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。
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.
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。
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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。
Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for running PCA, computing neighbors, clustering with Leiden/Louvain algorithms, generating UMAP/tSNE embeddings, and visualizing clusters. Use when performing dimensionality reduction and clustering on single-cell 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。
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
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 accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential 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。
Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN. Remove technical variation while preserving biological differences. Use when integrating multiple scRNA-seq batches or datasets.
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。
Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.
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。
Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.
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。
Workflow for pseudotime, lineage branching, and state-transition analysis in single-cell 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。
ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
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。
ESM2 protein language model for sequence scoring, embeddings, and plausibility checks. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai1-structure-prediction or boltz-structure-prediction. For QC thresholds, use protein-design-qc.
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 sentiment in any text — reviews, social posts, feedback, or articles. Returns sentiment polarity (positive, negative, neutral), confidence score, and key phrases driving the sentiment. Useful for brand monitoring, customer feedback analysis, and content 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。