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概要と使いどころ
Use when the user wants to prepare or verify a Twenty app for npm or marketplace publication, including README/About copy, package metadata, defineApplication marketplace metadata, logos, screenshots, and public assets.
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Use when the user wants to add or modify Twenty app entities, including objects, layouts, logic functions, and front components inside an existing Twenty app.
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 plan, build, or optimize SMS, MMS, or WhatsApp marketing — including welcome flows, abandoned cart texts, post-purchase, win-back, promotional sends, or transactional/auth SMS. Also use when the user mentions "SMS marketing," "text message campaigns," "SMS sequence," "SMS automation," "abandoned cart text," "post-purchase SMS," "Klaviyo SMS," "Postscript," "Attentive," "Twilio," "A2P 10DLC," "TCPA," "SMS compliance," "short code," "toll-free SMS," "MMS campaign," "should I do SMS," "SMS vs email," "WhatsApp marketing," "WhatsApp Business API," "WhatsApp templates," or "click-to-WhatsApp." For email sequences, see emails. For SMS copy framing, see copywriting. For opt-in popups that capture phone numbers, see popups.
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 write, rewrite, or improve marketing copy for any page, including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for," "improve this copy," "rewrite this page," "marketing copy," "headline help," "CTA copy," "value proposition," "tagline," "hero section copy," "this copy is weak," "this sounds like AI," "AI slop," "make it sound human," or "help me describe my product." Use this whenever someone is working on website text that needs to persuade or convert. Drafts never use AI tells like "it's not X, it's Y" reveals, "no X, no Y, no Z" lists, or sentences that trail into extra comma clauses. For email copy, see emails. For popup copy, see popups. For editing existing copy, see copy-editing. For the offer underneath the copy (bonuses, guarantees, value framing), see offers. Claims must be supportable, and a declined claim names the evidence that would unlock it.
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 optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups.
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 design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
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。
Runs one or more installed review lenses — adversarial critique, edge cases, verification gaps, structure, prose — and reports triaged findings. Use when, and only when, the user asks you to review a diff, a pull request, or an artifact — code or documents, one or many — and actually says "review"; an explicit skill:bmad-review directive from another skill counts as that ask. A request to act on feedback from an earlier review is a change, not a review. Never invoke this uninvited, including on edits you just made.
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 applies consistent corporate branding and styling to all generated documents including colors, fonts, layouts, and messaging
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。
Capability-based multi-tool matrix for video production. Agents pick providers (CLI-Anything harnesses, public CLIs, Python libs, native binaries, cloud APIs) per capability rather than marching through fixed stages, including storyboard planning, story/audio direction, source triage, internet video/music search/download, capture/generation, analysis, sound design, high-end caption design, NLE/render doctor investigation, review, and packaging.
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。
Keep a durable record of work passed between Bots in the shared Hindsight memory bank. Use when handing work to another Bot, when picking up work another Bot started, and when a finding would help other Bots, including Bots that are not part of the current conversation.
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。
Performs zero-shot time-series forecasting with Google's TimesFM, including regular-grid CSV preparation, quantile forecasts, XReg covariates, and held-out evaluation. Uses the Apache-licensed TimesFM 2.5 checkpoint by default and documents the distinct TimesFM 3.0 multivariate API and weight-license requirements.
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。
Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.
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Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.
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Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
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Stores and queries chunked N-D scientific arrays with Zarr-Python 3, including codecs, sharding, S3/GCS storage, and NumPy/Dask/Xarray integration. Use for array layout, bounded I/O, format migration, or scientific metadata preservation.
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Supports research proposal preparation and review for NSF, NIH, DOE, DARPA, and Taiwan NSTC, including opportunity-specific requirements, aims, review criteria, budgets, broader impacts, forms, and resubmissions. Use for investigator-authored grant development, compliance matrices, and proposal critiques.
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。
Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model comparison. Use for probabilistic modeling and uncertainty inference in PyMC.
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 Python/HTSlib workflows for genomic files. Used when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.
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。
Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.
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Solves and validates single-, multi-, and many-objective optimization with pymoo, including NSGA-II, NSGA-III, MOEA/D, constraints, Pareto approximations, reference directions, and ZDT/DTLZ benchmarks for engineering and research problems.
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。
Simulate and audit closed and open quantum-system models with QuTiP 5, including deterministic, trajectory, steady-state, spectral, and phase-space workflows. Use for local quantum-dynamics work where physical assumptions, dimensions, and numerical convergence must be explicit.
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Manages biological datasets and models with LaminDB, including artifact registration, lineage tracking, schema validation, Bionty ontology annotation, query/search, collections, branches, storage, and workflow integrations. Use for reproducible biological data curation or a LaminDB lakehouse.
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。
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.
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。
Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers, including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral.
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。